Diagnostic accuracy of machine learning algorithms in electrocardiogram-based sleep apnea detection: A systematic review and meta-analysis
Sleep apnea is a prevalent disorder affecting 10 % of middle-aged individuals, yet it remains underdiagnosed due to the limitations of polysomnography (PSG), the current diagnostic gold standard. Single-lead electrocardiography (ECG) has been proposed as a potential alternative diagnostic tool, but...
Uložené v:
| Vydané v: | Sleep medicine reviews Ročník 81; s. 102097 |
|---|---|
| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
England
Elsevier Ltd
01.06.2025
|
| Predmet: | |
| ISSN: | 1087-0792, 1532-2955, 1532-2955 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Sleep apnea is a prevalent disorder affecting 10 % of middle-aged individuals, yet it remains underdiagnosed due to the limitations of polysomnography (PSG), the current diagnostic gold standard. Single-lead electrocardiography (ECG) has been proposed as a potential alternative diagnostic tool, but interpretation challenges remain. Recent advances in machine learning and deep learning technologies offer promising approaches for enhancing the detection of sleep apnea through automated analysis of ECG signals. This meta-analysis aims to evaluate the diagnostic accuracy of machine learning (ML) and deep learning (DL) algorithms in detecting sleep apnea patterns from single-lead ECG data. A comprehensive literature search across multiple databases was conducted through November 2023, adhering to PRISMA-DTA guidelines. Studies that included sensitivity and specificity data for ECG-based sleep apnea detection using (machine learning/deep learning) ML/DL were selected. The analysis included 84 studies, demonstrating high diagnostic accuracy for ML/DL algorithms, with pooled sensitivity and specificity of over 90 % in per-segment analysis and close to 97 % in per-record analysis. Despite strong diagnostic performance, variations in algorithm effectiveness and methodological biases were noted. This meta-analysis highlights the potential of ML and DL in improving sleep apnea diagnosis and outlines areas for future research to address current limitations. |
|---|---|
| AbstractList | Sleep apnea is a prevalent disorder affecting 10 % of middle-aged individuals, yet it remains underdiagnosed due to the limitations of polysomnography (PSG), the current diagnostic gold standard. Single-lead electrocardiography (ECG) has been proposed as a potential alternative diagnostic tool, but interpretation challenges remain. Recent advances in machine learning and deep learning technologies offer promising approaches for enhancing the detection of sleep apnea through automated analysis of ECG signals. This meta-analysis aims to evaluate the diagnostic accuracy of machine learning (ML) and deep learning (DL) algorithms in detecting sleep apnea patterns from single-lead ECG data. A comprehensive literature search across multiple databases was conducted through November 2023, adhering to PRISMA-DTA guidelines. Studies that included sensitivity and specificity data for ECG-based sleep apnea detection using (machine learning/deep learning) ML/DL were selected. The analysis included 84 studies, demonstrating high diagnostic accuracy for ML/DL algorithms, with pooled sensitivity and specificity of over 90 % in per-segment analysis and close to 97 % in per-record analysis. Despite strong diagnostic performance, variations in algorithm effectiveness and methodological biases were noted. This meta-analysis highlights the potential of ML and DL in improving sleep apnea diagnosis and outlines areas for future research to address current limitations. AbstractSleep apnea is a prevalent disorder affecting 10 % of middle-aged individuals, yet it remains underdiagnosed due to the limitations of polysomnography (PSG), the current diagnostic gold standard. Single-lead electrocardiography (ECG) has been proposed as a potential alternative diagnostic tool, but interpretation challenges remain. Recent advances in machine learning and deep learning technologies offer promising approaches for enhancing the detection of sleep apnea through automated analysis of ECG signals. This meta-analysis aims to evaluate the diagnostic accuracy of machine learning (ML) and deep learning (DL) algorithms in detecting sleep apnea patterns from single-lead ECG data. A comprehensive literature search across multiple databases was conducted through November 2023, adhering to PRISMA-DTA guidelines. Studies that included sensitivity and specificity data for ECG-based sleep apnea detection using (machine learning/deep learning) ML/DL were selected. The analysis included 84 studies, demonstrating high diagnostic accuracy for ML/DL algorithms, with pooled sensitivity and specificity of over 90 % in per-segment analysis and close to 97 % in per-record analysis. Despite strong diagnostic performance, variations in algorithm effectiveness and methodological biases were noted. This meta-analysis highlights the potential of ML and DL in improving sleep apnea diagnosis and outlines areas for future research to address current limitations. Sleep apnea is a prevalent disorder affecting 10 % of middle-aged individuals, yet it remains underdiagnosed due to the limitations of polysomnography (PSG), the current diagnostic gold standard. Single-lead electrocardiography (ECG) has been proposed as a potential alternative diagnostic tool, but interpretation challenges remain. Recent advances in machine learning and deep learning technologies offer promising approaches for enhancing the detection of sleep apnea through automated analysis of ECG signals. This meta-analysis aims to evaluate the diagnostic accuracy of machine learning (ML) and deep learning (DL) algorithms in detecting sleep apnea patterns from single-lead ECG data. A comprehensive literature search across multiple databases was conducted through November 2023, adhering to PRISMA-DTA guidelines. Studies that included sensitivity and specificity data for ECG-based sleep apnea detection using (machine learning/deep learning) ML/DL were selected. The analysis included 84 studies, demonstrating high diagnostic accuracy for ML/DL algorithms, with pooled sensitivity and specificity of over 90 % in per-segment analysis and close to 97 % in per-record analysis. Despite strong diagnostic performance, variations in algorithm effectiveness and methodological biases were noted. This meta-analysis highlights the potential of ML and DL in improving sleep apnea diagnosis and outlines areas for future research to address current limitations.Sleep apnea is a prevalent disorder affecting 10 % of middle-aged individuals, yet it remains underdiagnosed due to the limitations of polysomnography (PSG), the current diagnostic gold standard. Single-lead electrocardiography (ECG) has been proposed as a potential alternative diagnostic tool, but interpretation challenges remain. Recent advances in machine learning and deep learning technologies offer promising approaches for enhancing the detection of sleep apnea through automated analysis of ECG signals. This meta-analysis aims to evaluate the diagnostic accuracy of machine learning (ML) and deep learning (DL) algorithms in detecting sleep apnea patterns from single-lead ECG data. A comprehensive literature search across multiple databases was conducted through November 2023, adhering to PRISMA-DTA guidelines. Studies that included sensitivity and specificity data for ECG-based sleep apnea detection using (machine learning/deep learning) ML/DL were selected. The analysis included 84 studies, demonstrating high diagnostic accuracy for ML/DL algorithms, with pooled sensitivity and specificity of over 90 % in per-segment analysis and close to 97 % in per-record analysis. Despite strong diagnostic performance, variations in algorithm effectiveness and methodological biases were noted. This meta-analysis highlights the potential of ML and DL in improving sleep apnea diagnosis and outlines areas for future research to address current limitations. |
| ArticleNumber | 102097 |
| Author | Yilancioglu, Yigit Resit Arayici, Mehmet Emin Turan, Oguzhan Ekrem Ozcan, Emin Evren Kilic, Mustafa Eray Yilmaz, Mehmet Birhan |
| Author_xml | – sequence: 1 givenname: Mustafa Eray orcidid: 0000-0002-0894-8790 surname: Kilic fullname: Kilic, Mustafa Eray email: mustafaeraykilic@gmail.com organization: Department of Cardiology, Faculty of Medicine, Dokuz Eylül University, İzmir, Türkiye – sequence: 2 givenname: Mehmet Emin orcidid: 0000-0002-0492-5129 surname: Arayici fullname: Arayici, Mehmet Emin email: mehmetearayici@gmail.com organization: Department of Biostatistics and Medical Informatics, Faculty of Medicine, Dokuz Eylül University, İzmir, Türkiye – sequence: 3 givenname: Oguzhan Ekrem orcidid: 0000-0003-3557-1682 surname: Turan fullname: Turan, Oguzhan Ekrem email: oguzhanekremturan@deu.edu.tr organization: Department of Cardiology, Faculty of Medicine, Dokuz Eylül University, İzmir, Türkiye – sequence: 4 givenname: Yigit Resit orcidid: 0000-0001-5762-1383 surname: Yilancioglu fullname: Yilancioglu, Yigit Resit email: yigityilancioglu@deu.edu.tr organization: Department of Cardiology, Faculty of Medicine, Dokuz Eylül University, İzmir, Türkiye – sequence: 5 givenname: Emin Evren orcidid: 0000-0002-2198-9300 surname: Ozcan fullname: Ozcan, Emin Evren email: eevrenozcan@deu.edu.tr organization: Department of Cardiology, Faculty of Medicine, Dokuz Eylül University, İzmir, Türkiye – sequence: 6 givenname: Mehmet Birhan orcidid: 0000-0002-8169-8628 surname: Yilmaz fullname: Yilmaz, Mehmet Birhan email: profdrmbyilmaz@gmail.com organization: Department of Cardiology, Faculty of Medicine, Dokuz Eylül University, İzmir, Türkiye |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40349509$$D View this record in MEDLINE/PubMed |
| BookMark | eNqFks1u1DAUhSNURH_gBVggL9lkajt2ElcIqSpQkCqxANbWHftm6sGxBztTlFfgqXE07aYSZWXLOt-58jn3tDoKMWBVvWZ0xShrz7erPKa7FadclgdOVfesOmGy4TVXUh6VO-27mnaKH1enOW8ppUqw9kV1LGgjlKTqpPrzwcEmxDw5Q8CYfQIzkziQEcytC0g8QgoubAj4TUxuuh0zcYGgRzOlaCBZFzcJxnoNGS3JHnFHYBcQiMWpiFwMF-SS5DlPOMIyJuGdw98EgiUjTlBDAD9nl19WzwfwGV_dn2fVj08fv199rm--Xn-5urypjWjFVPeColyboYNBGTBMtu2acSP6QXTSUmSDatqWUSmlQAtK2E7ygdp-aKBn_bo5q94efHcp_tpjnvToskHvIWDcZ92UQHupuOJF-uZeul-PaPUuuRHSrB_yK4L-IDAp5pxw0MZNsHx6SuC8ZlQvVemtXqrSS1X6UFVB-SP0wf1J6N0BwhJQiTHpbBwGg9alEra20T2Nv3-EG--CM-B_4ox5G_eplJE105lrqr8tC7TsD5dldyQVxeDi3wb_m_4XOmzX3g |
| CitedBy_id | crossref_primary_10_1016_j_smrv_2025_102152 |
| Cites_doi | 10.1007/s13534-017-0055-y 10.1109/JBHI.2018.2842919 10.3390/app11146622 10.1016/j.neunet.2023.03.019 10.3390/life12101509 10.3390/life12010119 10.1016/j.bspc.2013.05.007 10.3390/e25030399 10.1093/ehjdh/ztac016 10.1016/j.bspc.2023.105444 10.1016/j.smrv.2016.07.002 10.1016/j.cmpb.2020.105626 10.1016/j.engappai.2023.106451 10.1007/s00034-022-02092-6 10.3390/s22020510 10.1016/j.compbiomed.2016.08.012 10.1007/s10439-025-03691-5 10.1016/j.neucom.2013.04.048 10.1016/j.irbm.2018.03.002 10.1109/JSEN.2017.2690805 10.1016/j.compbiomed.2018.06.011 10.1016/j.compbiomed.2021.105124 10.34768/amcs-2023-0036 10.1109/TASE.2014.2345667 10.3390/jcm9020591 10.1016/j.bspc.2021.102685 10.1016/j.artmed.2021.102133 10.1016/j.compbiomed.2017.10.004 10.1183/09031936.00046710 10.1016/j.ijporl.2017.06.013 10.1109/JBHI.2013.2292928 10.1109/TBME.2015.2498199 10.1007/s11325-008-0234-2 10.1109/JBHI.2022.3203560 10.1016/j.neucom.2021.12.001 10.1016/j.asoc.2019.105568 10.1109/TBME.2015.2422378 10.1016/j.eswa.2023.121658 10.1016/j.bmt.2024.10.003 10.3390/jcm11010192 10.1016/j.smrv.2022.101743 10.1016/j.jclinepi.2005.01.016 10.1016/j.neucom.2018.03.011 10.1016/j.bspc.2023.104754 10.1109/TBCAS.2018.2824659 10.1016/j.compbiomed.2022.106100 10.3390/s21165425 10.1186/1471-2288-14-70 10.1016/j.smrv.2024.101991 10.1007/s12530-022-09445-1 10.1038/jhh.2015.15 10.1016/j.imu.2019.100170 10.1016/j.imu.2023.101286 10.1016/j.cmpb.2019.05.002 10.1001/jama.2017.19163 10.1088/1361-6579/ac0a9c 10.1088/1361-6579/aac7b7 10.1007/s10916-018-0963-0 10.1142/S021951941950026X 10.3390/s20154157 10.1016/j.bspc.2022.104401 10.1016/j.asoc.2023.110613 10.1007/s13534-023-00297-5 10.1001/jamanetworkopen.2019.19657 10.5664/jcsm.27184 10.1016/j.measurement.2022.111787 10.1109/ACCESS.2020.3036024 10.1016/j.eswa.2021.115950 10.1109/JBHI.2017.2784415 10.1007/s11042-018-6161-8 10.1088/1361-6579/ac184e 10.1088/1742-6596/2273/1/012015 10.1088/1361-6579/ac826e 10.3390/s23104692 10.1088/2057-1976/ab68e9 10.1007/s40846-021-00646-8 10.1109/JBHI.2022.3166859 10.1016/j.compbiomed.2021.104532 10.7326/0003-4819-155-8-201110180-00009 10.24200/tjer.vol17iss1pp24-33 10.1371/journal.pone.0250618 10.1016/j.eswa.2023.120484 10.1002/ppul.25423 10.1007/s11325-024-03173-3 10.1186/s12874-022-01788-2 10.1161/01.CIR.101.23.e215 10.1016/j.bspc.2020.102005 10.1016/j.irbm.2020.05.006 10.1016/j.cmpb.2019.105001 |
| ContentType | Journal Article |
| Copyright | 2025 Elsevier Ltd Elsevier Ltd Copyright © 2025 Elsevier Ltd. All rights reserved. |
| Copyright_xml | – notice: 2025 Elsevier Ltd – notice: Elsevier Ltd – notice: Copyright © 2025 Elsevier Ltd. All rights reserved. |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8 |
| DOI | 10.1016/j.smrv.2025.102097 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Public Health |
| EISSN | 1532-2955 |
| EndPage | 102097 |
| ExternalDocumentID | 40349509 10_1016_j_smrv_2025_102097 S1087079225000504 1_s2_0_S1087079225000504 |
| Genre | Meta-Analysis Systematic Review Journal Article |
| GroupedDBID | --- --K --M -RU .1- .FO .~1 0R~ 123 1B1 1P~ 1~. 1~5 4.4 457 4G. 53G 5VS 6PF 7-5 71M 8P~ AAEDT AAEDW AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AATTM AAWTL AAXKI AAXLA AAXUO AAYWO ABBQC ABCQJ ABFRF ABIVO ABJNI ABMAC ABMZM ABWVN ABXDB ACDAQ ACGFO ACGFS ACIEU ACLOT ACRLP ACRPL ACVFH ADBBV ADCNI ADEZE ADMUD ADNMO AEBSH AEFWE AEIPS AEKER AENEX AEUPX AEVXI AFJKZ AFPUW AFRHN AFTJW AFXIZ AGHFR AGQPQ AGUBO AGWIK AGYEJ AIEXJ AIGII AIIUN AIKHN AITUG AJRQY AJUYK AKBMS AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU ANZVX APXCP ASPBG AVWKF AXJTR AZFZN BKOJK BLXMC BNPGV CAG COF CS3 DU5 EBS EFJIC EFKBS EFLBG EJD EO8 EO9 EP2 EP3 EX3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-Q GBLVA HVGLF HZ~ IHE J1W KOM M41 MO0 MOBAO N9A O-L O9- OAUVE OJ- OV. OZT P-8 P-9 P2P PC. Q38 R2- ROL RPZ SCC SDF SDG SEL SES SEW SPCBC SSH SSN SSZ T5K UNMZH WOW Z5R ~G- ~HD AFCTW AGCQF AGRNS RIG 9DU AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8 |
| ID | FETCH-LOGICAL-c464t-840e5bcf7af9cac1566b12c48f475d0e1f9366105554eda94d752f0d8f3a818b3 |
| ISICitedReferencesCount | 2 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001491174300001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1087-0792 1532-2955 |
| IngestDate | Thu Oct 02 23:36:52 EDT 2025 Tue Sep 16 01:45:18 EDT 2025 Tue Nov 18 21:41:12 EST 2025 Sat Nov 29 07:54:40 EST 2025 Sat Jun 21 16:53:14 EDT 2025 Thu Jun 12 22:56:18 EDT 2025 Tue Oct 14 19:35:06 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Deep learning Single-lead ECG Diagnostic accuracy Machine learning Sensitivity and specificity Sleep apnea Electrocardiogram (ECG) Sleep medicine Meta-analysis |
| Language | English |
| License | Copyright © 2025 Elsevier Ltd. All rights reserved. |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c464t-840e5bcf7af9cac1566b12c48f475d0e1f9366105554eda94d752f0d8f3a818b3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 ObjectType-Review-3 content type line 23 |
| ORCID | 0000-0002-2198-9300 0000-0001-5762-1383 0000-0002-0894-8790 0000-0002-0492-5129 0000-0003-3557-1682 0000-0002-8169-8628 |
| PMID | 40349509 |
| PQID | 3202859292 |
| PQPubID | 23479 |
| PageCount | 1 |
| ParticipantIDs | proquest_miscellaneous_3202859292 pubmed_primary_40349509 crossref_citationtrail_10_1016_j_smrv_2025_102097 crossref_primary_10_1016_j_smrv_2025_102097 elsevier_sciencedirect_doi_10_1016_j_smrv_2025_102097 elsevier_clinicalkeyesjournals_1_s2_0_S1087079225000504 elsevier_clinicalkey_doi_10_1016_j_smrv_2025_102097 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-06-01 |
| PublicationDateYYYYMMDD | 2025-06-01 |
| PublicationDate_xml | – month: 06 year: 2025 text: 2025-06-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England |
| PublicationTitle | Sleep medicine reviews |
| PublicationTitleAlternate | Sleep Med Rev |
| PublicationYear | 2025 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Hedner, Grote, Bonsignore, McNicholas, Lavie, Parati (bib102) 2011; 38 Jafari (bib43) 2013; 8 Setiawan, Lin (bib62) 2022; 12 Pinho, Pombo, Silva, Bousson, Garcia (bib58) 2019; 83 Sharaf (bib64) 2023; 25 Dey, Chaudhuri, Munshi (bib29) 2018; 8 Lin, Li, Wang, Bai, Cui, Yu (bib117) 2024; 237 Yeo, Byun, Lee, Byun, Rhee, Shin (bib92) 2022; 26 Dong, Li, Chen (bib30) 2018; 22 Zarei, Asl (bib94) 2019; 23 McInnes, Moher, Thombs, McGrath, Bossuyt (bib10) 2018; 319 Yu, Yang, You, Shan (bib93) 2021; 42 Chen, Zhang, Song (bib26) 2015; 12 Song, Liu, Zhang, Chen, Xian (bib74) 2016; 63 Bernardini, Brunello, Gigli, Montanari, Saccomanno (bib21) 2021; 118 Chen, Yue, Zou, Lei, Ma, Fan (bib28) 2023; 162 Scalzitti, Hansen, Maturo, Lospinoso, O'Connor (bib5) 2017; 100 Kandukuri, Prakash, Patro, Neelapu, Tadeusiewicz, Pławiak (bib45) 2023 Sep 1; 33 Venkataraman, Vungarala, Covassin, Somers (bib3) 2020; 9 Qin, Liu (bib60) 2022; 473 Mashrur, Islam, Saha, Islam, Moni (bib51) 2021; 134 Plana, Arevalo-Rodriguez, Fernández-García, Soto, Fabregate, Pérez (bib13) 2022; 22 Nasifoglu, Erogul (bib54) 2021; 42 Bali, Nandi, Hiremath, Patil (bib20) 2024; 7 Chang, Yeh, Lee, Lin (bib24) 2020; 20 Surrel, Aminifar, Rincon, Murali, Atienza (bib76) 2018; 12 Abd-alrazaq, Aslam, AlSaad, Alsahli, Ahmed, Damseh (bib107) 2024; 26 Chen, Shen, Ma, Zheng (bib25) 2022; 16 Hemrajani, Dhaka, Rani, Shukla, Bavirisetti (bib39) 2023; 23 Yeh, Chang, Hu, Lin (bib91) 2022; 22 Gao, Tan, Tan, Ng, Leong, Phua (bib108) 2025; 29 Whiting, Rutjes, Westwood, Mallett, Deeks, Reitsma (bib11) 2011; 155 Wang, Lin, Wang (bib85) 2019; 176 Tyagi, Agarwal (bib6) 2023; 13 Ullah, Mahmood, Kim, Nam, Sultan, Park (bib80) 2023; 123 Wicaksono, u, Alam, Isa (bib89) 2022; 39 Sharma, Agarwal, Acharya (bib66) 2018; 100 Urtnasan, Park, Joo, Lee (bib81) 2018; 42 Hemrajani, Dhaka, Rani (bib38) 2023 Fatimah, Joshi (bib34) 2022; 41 Wang, Lu, Shen, Hong (bib86) 2019; 7 Li, Shi, Zhou, Zhang, Wu, Ren (bib47) 2023; 72 Sharan, Berkovsky, Xiong, Coiera (bib65) 2021; 41 Kumar Tyagi, Agrawal (bib46) 2023; 80 Xu, Faust, Seoni, Chakraborty, Barua, Loh (bib9) 2022; 150 Zarei, Mohammadzadeh Asl (bib95) 2020; 195 Srivastava, Chauhan, Kargeti, Pradhan, Dhaka (bib75) 2023; 84 Li, Pan, Li, Jiang, Liu (bib48) 2018; 294 Zhou, He, Kang (bib97) 2022 Al-Zaiti, Alghwiri, Hu, Clermont, Peace, Macfarlane (bib12) 2022; 3 Pépin, Letesson, Le-Dong, Dedave, Denison, Cuthbert (bib106) 2020; 3 Yang, Zou, Wei, Liu (bib90) 2022; 140 Bozkurt, Uçar, Bozkurt, Bilgin (bib22) 2020; 41 Gutiérrez‐Tobal, Álvarez, Kheirandish‐Gozal, Del Campo, Gozal, Hornero (bib105) 2022; 57 Erdenebayar, Kim, Park, Joo, Lee (bib31) 2019; 180 Ahmadi, Shapiro, Chung, Shapiro (bib4) 2009; 13 Gupta, Bajaj, Ansari (bib37) 2022; 71 Gonzaga, Bertolami, Bertolami, Amodeo, Calhoun (bib1) 2015; 29 Shen, Qin, Wei, Liu (bib70) 2021; 70 Goldberger, Amaral, Glass, Hausdorff, Ivanov, Mark (bib98) 2000; 101 Travieso, Alonso, Del Pozo, Ticay, Castellanos-Dominguez (bib78) 2014; 132 Sun, Hong, Wang, Dong, Han, Li (bib7) 2022; 43 Nguyen, Wilkins, Cheng, Benjamin (bib55) 2014; 18 Chen, Chen, Ma, Fan, Li, Se-Mscnn (bib27) 2021 Fathima, Ahmed (bib114) 2025 Hossen, Qasim (bib40) 2020; 17 Wang, Lu, Shen (bib87) 2019; 2019 Iyengar, Peng, Morin, Goldberger, Lipsitz (bib101) 1996; 271 Tang, Li, Zhang, Deng, Liu, Zheng (bib118) 2024; 8 Varon, Caicedo, Testelmans, Buyse, Van Huffel (bib83) 2015; 62 Sharma, Sharma (bib69) 2020; 6 Cao, Lv (bib23) 2022; 202 Janbakhshi, Shamsollahi (bib44) 2018; 39 Niroshana, Zhu, Nakamura, Chen (bib56) 2021; 16 Fang, Lu, Hong, Jiang, Wang (bib33) 2022; 12 Hong, Zhang, Sun, Zhou, Li (bib8) 2021; 12 Baek, Kim, Kim, Lee (bib18) 2014; vol. 167 Hu, Cai, Gao, Wang (bib41) 2022; 71 Martín-González, Navarro-Mesa, Juliá-Serdá, Kraemer, Wessel, Ravelo-García (bib50) 2017; 91 Smruthy, Suchetha (bib73) 2017; 17 Sharma, Raval, Acharya (bib67) 2019; 16 Wang, Cheng, Wang, Liu, Tian, Jiang (bib88) 2020; 79 Almutairi, Hassan, Datta (bib16) 2021 Feng, Qin, Wu, Pan, Liu (bib36) 2021; 70 Singh, Talwekar (bib109) 2022; 2273 Li, Zhang, Liu, Lin, Zhang, Tang (bib116) 2023; 146 S Band, Yarahmadi, Hsu, Biyari, Sookhak, Ameri (bib115) 2023; 40 Mukherjee, Dhar, Schwenker, Sarkar (bib52) 2021; 21 (bib42) 2022; 12 Shao, Han, Wang, Song, Yao, Hou (bib63) 2022; 26 Abasi, Aloqaily, Guizani (bib14) 2023; 229 Pombo, Silva, Pinho, Garcia (bib59) 2020; 8 Ayatollahi, Afrakhteh, Soltani, Saleh (bib17) 2023; 14 Sharma, Sharma (bib68) 2016; 77 Khor, Khung, Ruehland, Jiao, Lew, Munsif (bib111) 2023; 68 Landry, Semsar-Kazerooni, Chen, Gurberg, Nguyen, Constantin (bib112) 2024; 78 Thompson, Fergus, Chalmers, Reilly (bib77) 2020 Salari, Hosseinian-Far, Mohammadi, Ghasemi, Khazaie, Daneshkhah (bib110) 2022; 187 Lin, Wang, Setiawan, Trang, Lin (bib49) 2021; 11 Conor Heneghan (bib100) 2007 Deeks, Macaskill, Irwig (bib104) 2005; 58 Heneghan, de, Ryan, Chua, Doherty, Boyle (bib113) 2008; 4 Parbat, Chakraborty (bib57) 2024; 87 Sheta, Turabieh, Thaher, Too, Mafarja, Hossain (bib71) 2021; 11 Bahrami, Forouzanfar (bib19) 2022; 71 Faal, Almasganj (bib32) 2021; 68 Penzel, Moody, Mark, Goldberger, Peter (bib99) 2000 van Enst, Ochodo, Scholten, Hooft, Leeflang (bib103) 2014; 14 Urtnasan, Park, Lee (bib82) 2018; 39 Fatimah, Singh, Singhal, Pachori (bib35) 2020; 61 Singh, Majumder (bib72) 2019; 19 Senaratna, Perret, Lodge, Lowe, Campbell, Matheson (bib2) 2017; 34 Hemrajani (10.1016/j.smrv.2025.102097_bib39) 2023; 23 Ayatollahi (10.1016/j.smrv.2025.102097_bib17) 2023; 14 Cao (10.1016/j.smrv.2025.102097_bib23) 2022; 202 Li (10.1016/j.smrv.2025.102097_bib48) 2018; 294 Shao (10.1016/j.smrv.2025.102097_bib63) 2022; 26 Janbakhshi (10.1016/j.smrv.2025.102097_bib44) 2018; 39 Abd-alrazaq (10.1016/j.smrv.2025.102097_bib107) 2024; 26 Sharma (10.1016/j.smrv.2025.102097_bib69) 2020; 6 Sharaf (10.1016/j.smrv.2025.102097_bib64) 2023; 25 Fathima (10.1016/j.smrv.2025.102097_bib114) 2025 Penzel (10.1016/j.smrv.2025.102097_bib99) 2000 Sheta (10.1016/j.smrv.2025.102097_bib71) 2021; 11 Mashrur (10.1016/j.smrv.2025.102097_bib51) 2021; 134 Conor Heneghan (10.1016/j.smrv.2025.102097_bib100) Surrel (10.1016/j.smrv.2025.102097_bib76) 2018; 12 Mukherjee (10.1016/j.smrv.2025.102097_bib52) 2021; 21 Fatimah (10.1016/j.smrv.2025.102097_bib34) 2022; 41 Wicaksono (10.1016/j.smrv.2025.102097_bib89) 2022; 39 McInnes (10.1016/j.smrv.2025.102097_bib10) 2018; 319 Whiting (10.1016/j.smrv.2025.102097_bib11) 2011; 155 Setiawan (10.1016/j.smrv.2025.102097_bib62) 2022; 12 Abasi (10.1016/j.smrv.2025.102097_bib14) 2023; 229 Gupta (10.1016/j.smrv.2025.102097_bib37) 2022; 71 Deeks (10.1016/j.smrv.2025.102097_bib104) 2005; 58 Erdenebayar (10.1016/j.smrv.2025.102097_bib31) 2019; 180 Srivastava (10.1016/j.smrv.2025.102097_bib75) 2023; 84 Almutairi (10.1016/j.smrv.2025.102097_bib16) 2021 Gutiérrez‐Tobal (10.1016/j.smrv.2025.102097_bib105) 2022; 57 Hedner (10.1016/j.smrv.2025.102097_bib102) 2011; 38 Wang (10.1016/j.smrv.2025.102097_bib87) 2019; 2019 Hossen (10.1016/j.smrv.2025.102097_bib40) 2020; 17 Venkataraman (10.1016/j.smrv.2025.102097_bib3) 2020; 9 Nasifoglu (10.1016/j.smrv.2025.102097_bib54) 2021; 42 Li (10.1016/j.smrv.2025.102097_bib47) 2023; 72 Plana (10.1016/j.smrv.2025.102097_bib13) 2022; 22 Baek (10.1016/j.smrv.2025.102097_bib18) 2014; vol. 167 Faal (10.1016/j.smrv.2025.102097_bib32) 2021; 68 Sharan (10.1016/j.smrv.2025.102097_bib65) 2021; 41 Sharma (10.1016/j.smrv.2025.102097_bib68) 2016; 77 Goldberger (10.1016/j.smrv.2025.102097_bib98) 2000; 101 Gonzaga (10.1016/j.smrv.2025.102097_bib1) 2015; 29 Bahrami (10.1016/j.smrv.2025.102097_bib19) 2022; 71 Senaratna (10.1016/j.smrv.2025.102097_bib2) 2017; 34 Bernardini (10.1016/j.smrv.2025.102097_bib21) 2021; 118 Yeo (10.1016/j.smrv.2025.102097_bib92) 2022; 26 Xu (10.1016/j.smrv.2025.102097_bib9) 2022; 150 Zhou (10.1016/j.smrv.2025.102097_bib97) 2022 Tyagi (10.1016/j.smrv.2025.102097_bib6) 2023; 13 Bozkurt (10.1016/j.smrv.2025.102097_bib22) 2020; 41 Bali (10.1016/j.smrv.2025.102097_bib20) 2024; 7 Qin (10.1016/j.smrv.2025.102097_bib60) 2022; 473 S Band (10.1016/j.smrv.2025.102097_bib115) 2023; 40 Travieso (10.1016/j.smrv.2025.102097_bib78) 2014; 132 Pépin (10.1016/j.smrv.2025.102097_bib106) 2020; 3 Kandukuri (10.1016/j.smrv.2025.102097_bib45) 2023; 33 Yu (10.1016/j.smrv.2025.102097_bib93) 2021; 42 Chen (10.1016/j.smrv.2025.102097_bib28) 2023; 162 Niroshana (10.1016/j.smrv.2025.102097_bib56) 2021; 16 Chen (10.1016/j.smrv.2025.102097_bib25) 2022; 16 Fang (10.1016/j.smrv.2025.102097_bib33) 2022; 12 Li (10.1016/j.smrv.2025.102097_bib116) 2023; 146 Chen (10.1016/j.smrv.2025.102097_bib27) 2021 Hu (10.1016/j.smrv.2025.102097_bib41) 2022; 71 Nguyen (10.1016/j.smrv.2025.102097_bib55) 2014; 18 Smruthy (10.1016/j.smrv.2025.102097_bib73) 2017; 17 Jafari (10.1016/j.smrv.2025.102097_bib43) 2013; 8 Sharma (10.1016/j.smrv.2025.102097_bib67) 2019; 16 Iyengar (10.1016/j.smrv.2025.102097_bib101) 1996; 271 Varon (10.1016/j.smrv.2025.102097_bib83) 2015; 62 Urtnasan (10.1016/j.smrv.2025.102097_bib82) 2018; 39 Feng (10.1016/j.smrv.2025.102097_bib36) 2021; 70 Scalzitti (10.1016/j.smrv.2025.102097_bib5) 2017; 100 Heneghan (10.1016/j.smrv.2025.102097_bib113) 2008; 4 Lin (10.1016/j.smrv.2025.102097_bib49) 2021; 11 Lin (10.1016/j.smrv.2025.102097_bib117) 2024; 237 Dong (10.1016/j.smrv.2025.102097_bib30) 2018; 22 Sharma (10.1016/j.smrv.2025.102097_bib66) 2018; 100 Urtnasan (10.1016/j.smrv.2025.102097_bib81) 2018; 42 Hong (10.1016/j.smrv.2025.102097_bib8) 2021; 12 Wang (10.1016/j.smrv.2025.102097_bib86) 2019; 7 Zarei (10.1016/j.smrv.2025.102097_bib95) 2020; 195 Al-Zaiti (10.1016/j.smrv.2025.102097_bib12) 2022; 3 Chang (10.1016/j.smrv.2025.102097_bib24) 2020; 20 Pombo (10.1016/j.smrv.2025.102097_bib59) 2020; 8 Ahmadi (10.1016/j.smrv.2025.102097_bib4) 2009; 13 Landry (10.1016/j.smrv.2025.102097_bib112) 2024; 78 Singh (10.1016/j.smrv.2025.102097_bib109) 2022; 2273 Wang (10.1016/j.smrv.2025.102097_bib85) 2019; 176 Salari (10.1016/j.smrv.2025.102097_bib110) 2022; 187 Chen (10.1016/j.smrv.2025.102097_bib26) 2015; 12 Martín-González (10.1016/j.smrv.2025.102097_bib50) 2017; 91 Dey (10.1016/j.smrv.2025.102097_bib29) 2018; 8 Hemrajani (10.1016/j.smrv.2025.102097_bib38) 2023 Wang (10.1016/j.smrv.2025.102097_bib88) 2020; 79 Gao (10.1016/j.smrv.2025.102097_bib108) 2025; 29 Tang (10.1016/j.smrv.2025.102097_bib118) 2024; 8 Fatimah (10.1016/j.smrv.2025.102097_bib35) 2020; 61 van Enst (10.1016/j.smrv.2025.102097_bib103) 2014; 14 Kumar Tyagi (10.1016/j.smrv.2025.102097_bib46) 2023; 80 (10.1016/j.smrv.2025.102097_bib42) 2022; 12 Yang (10.1016/j.smrv.2025.102097_bib90) 2022; 140 Zarei (10.1016/j.smrv.2025.102097_bib94) 2019; 23 Sun (10.1016/j.smrv.2025.102097_bib7) 2022; 43 Ullah (10.1016/j.smrv.2025.102097_bib80) 2023; 123 Song (10.1016/j.smrv.2025.102097_bib74) 2016; 63 Parbat (10.1016/j.smrv.2025.102097_bib57) 2024; 87 Singh (10.1016/j.smrv.2025.102097_bib72) 2019; 19 Thompson (10.1016/j.smrv.2025.102097_bib77) 2020 Khor (10.1016/j.smrv.2025.102097_bib111) 2023; 68 Yeh (10.1016/j.smrv.2025.102097_bib91) 2022; 22 Pinho (10.1016/j.smrv.2025.102097_bib58) 2019; 83 Shen (10.1016/j.smrv.2025.102097_bib70) 2021; 70 |
| References_xml | – volume: 155 start-page: 529 year: 2011 end-page: 536 ident: bib11 article-title: QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies publication-title: Ann Intern Med – volume: 12 start-page: 106 year: 2015 end-page: 115 ident: bib26 article-title: An automatic screening approach for obstructive sleep apnea diagnosis based on single-lead electrocardiogram publication-title: IEEE Trans Automat Sci Eng – volume: 23 start-page: 1011 year: 2019 end-page: 1021 ident: bib94 article-title: Automatic detection of obstructive sleep apnea using wavelet Transform and entropy-based features from single-lead ECG signal publication-title: IEEE J Biomed Health Inform – volume: 12 start-page: 119 year: 2022 ident: bib33 article-title: Sleep apnea detection based on multi-scale residual network publication-title: Life – volume: 180 year: 2019 ident: bib31 article-title: Deep learning approaches for automatic detection of sleep apnea events from an electrocardiogram publication-title: Comput Methods Progr Biomed – volume: 70 start-page: 1 year: 2021 end-page: 12 ident: bib36 article-title: A sleep apnea detection method based on unsupervised feature learning and single-lead electrocardiogram publication-title: IEEE Trans Instrum Meas – volume: 12 year: 2021 ident: bib8 article-title: Practical lessons on 12-lead ECG classification: meta-analysis of methods from PhysioNet/computing in cardiology challenge 2020 publication-title: Front Physiol – volume: 77 start-page: 116 year: 2016 end-page: 124 ident: bib68 article-title: An algorithm for sleep apnea detection from single-lead ECG using Hermite basis functions publication-title: Comput Biol Med – volume: 39 start-page: 206 year: 2018 end-page: 218 ident: bib44 article-title: Sleep apnea detection from single-lead ECG using features based on ECG-derived respiration (EDR) signals publication-title: IRBM – volume: 83 year: 2019 ident: bib58 article-title: Towards an accurate sleep apnea detection based on ECG signal: the quintessential of a wise feature selection publication-title: Appl Soft Comput – volume: 71 start-page: 1 year: 2022 end-page: 9 ident: bib37 article-title: OSACN-net: automated classification of sleep apnea using deep learning model and smoothed Gabor spectrograms of ECG signal publication-title: IEEE Trans Instrum Meas – volume: 61 year: 2020 ident: bib35 article-title: Detection of apnea events from ECG segments using Fourier decomposition method publication-title: Biomed Signal Process Control – volume: 319 start-page: 388 year: 2018 end-page: 396 ident: bib10 article-title: Preferred reporting Items for a systematic review and meta-analysis of diagnostic test accuracy studies: the PRISMA-DTA statement publication-title: JAMA – volume: 38 start-page: 635 year: 2011 end-page: 642 ident: bib102 article-title: The European Sleep Apnoea Database (ESADA): report from 22 European sleep laboratories publication-title: Eur Respir J – volume: 26 start-page: 5428 year: 2022 end-page: 5438 ident: bib92 article-title: Robust method for screening sleep apnea with single-lead ECG using deep residual network: evaluation with open database and patch-type wearable device data publication-title: IEEE J Biomed Health Inform – volume: 22 start-page: 510 year: 2022 ident: bib91 article-title: Contribution of different Subbands of ECG in sleep apnea detection evaluated using Filter Bank decomposition and a convolutional neural network publication-title: Sensors – volume: 7 start-page: 2852 year: 2024 end-page: 2861 ident: bib20 article-title: Detection of sleep apnea in ECG signal using PAN- TOMPKINS algorithm and ANN classifiers publication-title: Compusoft: An International Journal of Advanced Computer Technology [Internet] – volume: 101 start-page: e215 year: 2000 end-page: e220 ident: bib98 article-title: PhysioBank, PhysioToolkit, and PhysioNet publication-title: Circulation – volume: 91 start-page: 47 year: 2017 end-page: 58 ident: bib50 article-title: Heart rate variability feature selection in the presence of sleep apnea: an expert system for the characterization and detection of the disorder publication-title: Comput Biol Med – volume: 11 start-page: 6622 year: 2021 ident: bib71 article-title: Diagnosis of obstructive sleep apnea from ECG signals using machine learning and deep learning classifiers publication-title: Appl Sci – volume: 12 start-page: 762 year: 2018 end-page: 773 ident: bib76 article-title: Online obstructive sleep apnea detection on medical wearable sensors publication-title: IEEE Trans Biomed Circuits Syst – volume: 71 start-page: 1 year: 2022 end-page: 11 ident: bib41 article-title: A hybrid transformer model for obstructive sleep apnea detection based on Self-attention Mechanism using single-lead ECG publication-title: IEEE Trans Instrum Meas – volume: 41 start-page: 758 year: 2021 end-page: 766 ident: bib65 article-title: End-to-End sleep apnea detection using single-lead ECG signal and 1-D residual neural networks publication-title: J Med Biol Eng – volume: 134 year: 2021 ident: bib51 article-title: SCNN: scalogram-based convolutional neural network to detect obstructive sleep apnea using single-lead electrocardiogram signals publication-title: Comput Biol Med – volume: 17 start-page: 3092 year: 2017 end-page: 3099 ident: bib73 article-title: Real-time classification of healthy and apnea subjects using ECG signals with variational mode decomposition publication-title: IEEE Sens J – volume: 63 start-page: 1532 year: 2016 end-page: 1542 ident: bib74 article-title: An obstructive sleep apnea detection approach using a discriminative hidden Markov model from ECG signals publication-title: IEEE Trans Biomed Eng – volume: 23 start-page: 4692 year: 2023 ident: bib39 article-title: Efficient deep learning based hybrid model to detect obstructive sleep apnea publication-title: Sensors – volume: 42 start-page: 104 year: 2018 ident: bib81 article-title: Automated detection of obstructive sleep apnea events from a single-lead electrocardiogram using a convolutional neural network publication-title: J Med Syst – volume: 20 start-page: 4157 year: 2020 ident: bib24 article-title: A sleep apnea detection system based on a one-dimensional deep convolution neural network model using single-lead electrocardiogram publication-title: Sensors – volume: 22 start-page: 1895 year: 2018 end-page: 1905 ident: bib30 article-title: Frequency network analysis of heart rate variability for obstructive apnea patient detection publication-title: IEEE J Biomed Health Inform – year: 2007 ident: bib100 article-title: St. Vincent's University Hospital/University College Dublin sleep apnea database – volume: 68 year: 2021 ident: bib32 article-title: Obstructive sleep apnea screening from unprocessed ECG signals using statistical modelling publication-title: Biomed Signal Process Control – volume: vol. 167 year: 2014 ident: bib18 publication-title: Computer-aided detection with a portable electrocardiographic recorder and acceleration sensors for monitoring obstructive sleep apnea – volume: 8 start-page: 200477 year: 2020 end-page: 200485 ident: bib59 article-title: Classifier precision analysis for sleep apnea detection using ECG signals publication-title: IEEE Access – volume: 39 year: 2018 ident: bib82 article-title: Multiclass classification of obstructive sleep apnea/hypopnea based on a convolutional neural network from a single-lead electrocardiogram publication-title: Physiol Meas – volume: 229 year: 2023 ident: bib14 article-title: Optimization of CNN using modified honey badger algorithm for sleep apnea detection publication-title: Expert Syst Appl – volume: 6 year: 2020 ident: bib69 article-title: Sleep apnea detection from ECG using variational mode decomposition publication-title: Biomed Phys Eng Express – volume: 14 start-page: 191 year: 2023 end-page: 206 ident: bib17 article-title: Sleep apnea detection from ECG signal using deep CNN-based structures publication-title: Evolving Systems – start-page: 1382 year: 2021 end-page: 1386 ident: bib16 article-title: Detection of obstructive sleep apnoea by ECG signals using deep learning architectures publication-title: 2020 28th European signal processing conference (EUSIPCO) – volume: 3 year: 2020 ident: bib106 article-title: Assessment of Mandibular movement monitoring with machine learning analysis for the diagnosis of obstructive sleep apnea publication-title: JAMA Netw Open – start-page: 1276 year: 2021 end-page: 1280 ident: bib27 article-title: A lightweight multi-scaled fusion network for sleep apnea detection using single-lead ECG signals publication-title: 2021 IEEE international conference on bioinformatics and biomedicine (BIBM) – volume: 8 start-page: 551 year: 2013 end-page: 558 ident: bib43 article-title: Sleep apnoea detection from ECG using features extracted from reconstructed phase space and frequency domain publication-title: Biomed Signal Process Control – start-page: 255 year: 2000 end-page: 258 ident: bib99 article-title: The apnea-ECG database publication-title: Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163) – volume: 72 start-page: 1 year: 2023 end-page: 17 ident: bib47 article-title: TFFormer: a time–frequency information fusion-based CNN-transformer model for OSA detection with single-lead ECG publication-title: IEEE Trans Instrum Meas – volume: 140 year: 2022 ident: bib90 article-title: Obstructive sleep apnea detection from single-lead electrocardiogram signals using one-dimensional squeeze-and-excitation residual group network publication-title: Comput Biol Med – start-page: 1840 year: 2022 end-page: 1845 ident: bib97 article-title: OSA-CCNN: obstructive sleep apnea detection based on a composite deep convolution neural network model using single-lead ECG signal publication-title: 2022 IEEE International conference on Bioinformatics and Biomedicine (BIBM) – volume: 2019 start-page: 1 year: 2019 end-page: 9 ident: bib87 article-title: Detection of sleep apnea from single-lead ECG signal using a time Window artificial neural network publication-title: BioMed Res Int – year: 2025 ident: bib114 article-title: Sleep apnea detection using EEG: a systematic review of datasets, methods, challenges, and future directions publication-title: Ann Biomed Eng – volume: 79 start-page: 15813 year: 2020 end-page: 15827 ident: bib88 article-title: Obstructive sleep apnea detection using ecg-sensor with convolutional neural networks publication-title: Multimed Tool Appl – volume: 2273 year: 2022 ident: bib109 article-title: Comparison of machine learning and deep learning classifier to detect sleep apnea using single-channel ECG and HRV: a Systematic Literature Review publication-title: J Phys Conf Ser – volume: 12 start-page: 1509 year: 2022 ident: bib62 article-title: A deep learning framework for automatic sleep apnea classification based on Empirical mode decomposition derived from single-lead electrocardiogram publication-title: Life – volume: 4 start-page: 223 year: 2008 end-page: 228 ident: bib113 article-title: Electrocardiogram recording as a screening tool for sleep disordered breathing publication-title: J Clin Sleep Med – volume: 41 start-page: 6427 year: 2022 end-page: 6461 ident: bib34 article-title: Signal matched multirate Filter Bank design for optimum coding Gain and its application in real-time sleep apnea detection publication-title: Circ Syst Signal Process – volume: 12 year: 2022 ident: bib42 article-title: Obstructive sleep apnea detection using frequency analysis of electrocardiographic RR interval and machine learning algorithms publication-title: J Biomed Phys Eng – volume: 84 year: 2023 ident: bib75 article-title: ApneaNet: a hybrid 1DCNN-LSTM architecture for detection of Obstructive Sleep Apnea using digitized ECG signals publication-title: Biomed Signal Process Control – volume: 8 start-page: 92 year: 2024 end-page: 103 ident: bib118 article-title: HMS-TENet: a hierarchical multi-scale topological enhanced network based on EEG and EOG for driver vigilance estimation publication-title: Biomedical Technology – volume: 7 year: 2019 ident: bib86 article-title: Sleep apnea detection from a single-lead ECG signal with automatic feature-extraction through a modified LeNet-5 convolutional neural network publication-title: PeerJ – volume: 34 start-page: 70 year: 2017 end-page: 81 ident: bib2 article-title: Prevalence of obstructive sleep apnea in the general population: a systematic review publication-title: Sleep Med Rev – volume: 187 year: 2022 ident: bib110 article-title: Detection of sleep apnea using Machine learning algorithms based on ECG Signals: a comprehensive systematic review publication-title: Expert Syst Appl – volume: 202 year: 2022 ident: bib23 article-title: Multi-task feature fusion network for Obstructive Sleep Apnea detection using single-lead ECG signal publication-title: Measurement – volume: 195 year: 2020 ident: bib95 article-title: Performance evaluation of the spectral autocorrelation function and autoregressive models for automated sleep apnea detection using single-lead ECG signal publication-title: Comput Methods Progr Biomed – volume: 13 start-page: 293 year: 2023 end-page: 312 ident: bib6 article-title: Systematic review of automated sleep apnea detection based on physiological signal data using deep learning algorithm: a meta-analysis approach publication-title: Biomed Eng Lett – volume: 26 year: 2024 ident: bib107 article-title: Detection of sleep apnea using wearable AI: systematic review and meta-analysis publication-title: J Med Internet Res – volume: 80 year: 2023 ident: bib46 article-title: Automatic detection of sleep apnea from single-lead ECG signal using enhanced-deep belief network model publication-title: Biomed Signal Process Control – volume: 162 start-page: 571 year: 2023 end-page: 580 ident: bib28 article-title: RAFNet: restricted attention fusion network for sleep apnea detection publication-title: Neural Netw – volume: 16 year: 2022 ident: bib25 article-title: A spatio-temporal learning-based model for sleep apnea detection using single-lead ECG signals publication-title: Front Neurosci – volume: 9 start-page: 591 year: 2020 ident: bib3 article-title: Sleep apnea, hypertension and the sympathetic nervous system in the adult population publication-title: J Clin Med – volume: 118 year: 2021 ident: bib21 article-title: AIOSA: an approach to the automatic identification of obstructive sleep apnea events based on deep learning publication-title: Artif Intell Med – volume: 16 year: 2021 ident: bib56 article-title: A fused-image-based approach to detect obstructive sleep apnea using a single-lead ECG and a 2D convolutional neural network publication-title: PLoS One – volume: 13 start-page: 221 year: 2009 end-page: 226 ident: bib4 article-title: Clinical diagnosis of sleep apnea based on single night of polysomnography vs. two nights of polysomnography publication-title: Sleep Breath – volume: 19 year: 2019 ident: bib72 article-title: A Novel approach OSA detection using single-lead ECG scalogram based on deep neural network publication-title: J Mech Med Biol – volume: 176 start-page: 93 year: 2019 end-page: 104 ident: bib85 article-title: A RR interval based automated apnea detection approach using residual network publication-title: Comput Methods Progr Biomed – volume: 42 year: 2021 ident: bib93 article-title: {FASSNet}: fast apnea syndrome screening neural network based on single-lead electrocardiogram for wearable devices publication-title: Physiol Meas – volume: 78 year: 2024 ident: bib112 article-title: Diagnostic accuracy of portable sleep monitors in pediatric sleep apnea: a systematic review publication-title: Sleep Med Rev – volume: 42 year: 2021 ident: bib54 article-title: Obstructive sleep apnea prediction from electrocardiogram scalograms and spectrograms using convolutional neural networks publication-title: Physiol Meas – volume: 62 start-page: 2269 year: 2015 end-page: 2278 ident: bib83 article-title: A Novel algorithm for the automatic detection of sleep apnea from single-lead ECG publication-title: IEEE Trans Biomed Eng – volume: 41 start-page: 241 year: 2020 end-page: 251 ident: bib22 article-title: Detection of abnormal respiratory events with single channel ECG and hybrid machine learning model in patients with obstructive sleep apnea publication-title: IRBM – start-page: 518 year: 2023 end-page: 523 ident: bib38 article-title: Hybrid RNN-based classification of obstructive sleep apnea using single-lead ECG signals publication-title: 2023 International conference on recent advances in lectrical, Electronics & Digital healthcare technologies (REEDCON) – volume: 71 start-page: 1 year: 2022 end-page: 11 ident: bib19 article-title: Sleep apnea detection from single-lead ECG: a comprehensive analysis of machine learning and deep learning algorithms publication-title: IEEE Trans Instrum Meas – volume: 18 start-page: 1285 year: 2014 end-page: 1293 ident: bib55 article-title: An online sleep apnea detection method based on Recurrence Quantification analysis publication-title: IEEE J Biomed Health Inform – volume: 150 year: 2022 ident: bib9 article-title: A review of automated sleep disorder detection publication-title: Comput Biol Med – volume: 17 start-page: 24 year: 2020 ident: bib40 article-title: Identification of obstructive sleep apnea using artificial neural networks and wavelet Packet decomposition of the HRV signal publication-title: Jou Eng Res – volume: 26 start-page: 5841 year: 2022 end-page: 5850 ident: bib63 article-title: Obstructive sleep apnea detection Scheme based on manually generated features and Parallel heterogeneous deep learning model under IoMT publication-title: IEEE J Biomed Health Inform – volume: 146 year: 2023 ident: bib116 article-title: An EEG-based brain cognitive dynamic recognition network for representations of brain fatigue publication-title: Appl Soft Comput – volume: 132 start-page: 159 year: 2014 end-page: 165 ident: bib78 article-title: Building a Cepstrum-HMM kernel for apnea identification publication-title: Neurocomputing – volume: 21 start-page: 5425 year: 2021 ident: bib52 article-title: Ensemble of deep learning models for sleep apnea detection: an experimental study publication-title: Sensors – volume: 271 start-page: R1078 year: 1996 end-page: R1084 ident: bib101 article-title: Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics publication-title: Am J Physiol – volume: 22 start-page: 306 year: 2022 ident: bib13 article-title: Meta-DiSc 2.0: a web application for meta-analysis of diagnostic test accuracy data publication-title: BMC Med Res Methodol – volume: 8 start-page: 95 year: 2018 end-page: 100 ident: bib29 article-title: Obstructive sleep apnoea detection using convolutional neural network based deep learning framework publication-title: Biomed Eng Lett – volume: 57 start-page: 1931 year: 2022 end-page: 1943 ident: bib105 article-title: Reliability of machine learning to diagnose pediatric obstructive sleep apnea: systematic review and meta‐analysis publication-title: Pediatr Pulmonol – volume: 14 start-page: 70 year: 2014 ident: bib103 article-title: Investigation of publication bias in meta-analyses of diagnostic test accuracy: a meta-epidemiological study publication-title: BMC Med Res Methodol – volume: 11 start-page: 192 year: 2021 ident: bib49 article-title: Sleep apnea classification algorithm development using a machine-learning framework and Bag-of-features derived from electrocardiogram spectrograms publication-title: JCM – volume: 68 year: 2023 ident: bib111 article-title: Portable evaluation of obstructive sleep apnea in adults: a systematic review publication-title: Sleep Med Rev – volume: 16 year: 2019 ident: bib67 article-title: A new approach to identify obstructive sleep apnea using an optimal orthogonal wavelet filter bank with ECG signals publication-title: Inform Med Unlocked – volume: 100 start-page: 44 year: 2017 end-page: 51 ident: bib5 article-title: Comparison of home sleep apnea testing versus laboratory polysomnography for the diagnosis of obstructive sleep apnea in children publication-title: Int J Pediatr Otorhinolaryngol – volume: 3 start-page: 125 year: 2022 end-page: 140 ident: bib12 article-title: A clinician's guide to understanding and critically appraising machine learning studies: a checklist for Ruling Out Bias Using Standard Tools in Machine Learning (ROBUST-ML) publication-title: Eur Heart J Digit Health – volume: 33 start-page: 493 year: 2023 Sep 1 end-page: 506 ident: bib45 article-title: Constant Q–Transform–based deep learning architecture for detection of obstructive sleep apnea publication-title: Int J Appl Math Comput Sci – start-page: 1 year: 2020 end-page: 8 ident: bib77 article-title: Detection of obstructive sleep apnoea using features extracted from segmented time-series ECG signals using a one dimensional convolutional neural network publication-title: 2020 International Joint conference on Neural networks (IJCNN) – volume: 58 start-page: 882 year: 2005 end-page: 893 ident: bib104 article-title: The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed publication-title: J Clin Epidemiol – volume: 29 start-page: 36 year: 2025 ident: bib108 article-title: Artificial intelligence facial recognition of obstructive sleep apnea: a Bayesian meta-analysis publication-title: Sleep Breath – volume: 294 start-page: 94 year: 2018 end-page: 101 ident: bib48 article-title: A method to detect sleep apnea based on deep neural network and hidden Markov model using single-lead ECG signal publication-title: Neurocomputing – volume: 25 start-page: 399 year: 2023 ident: bib64 article-title: Sleep apnea detection using wavelet scattering transformation and random forest classifier publication-title: Entropy – volume: 100 start-page: 100 year: 2018 end-page: 113 ident: bib66 article-title: Application of an optimal class of antisymmetric wavelet filter banks for obstructive sleep apnea diagnosis using ECG signals publication-title: Comput Biol Med – volume: 70 start-page: 1 year: 2021 end-page: 13 ident: bib70 article-title: Multiscale deep neural network for obstructive sleep apnea detection using RR interval from single-lead ECG signal publication-title: IEEE Trans Instrum Meas – volume: 237 year: 2024 ident: bib117 article-title: An EEG-based cross-subject interpretable CNN for game player expertise level classification publication-title: Expert Syst Appl – volume: 473 start-page: 24 year: 2022 end-page: 36 ident: bib60 article-title: A dual-model deep learning method for sleep apnea detection based on representation learning and temporal dependence publication-title: Neurocomputing – volume: 39 start-page: 1527 year: 2022 end-page: 1536 ident: bib89 article-title: Dealing with imbalanced sleep apnea data using DCGAN publication-title: Tob Situat (US Dept Agric Econ Res Serv) – volume: 43 year: 2022 ident: bib7 article-title: A systematic review of deep learning methods for modeling electrocardiograms during sleep publication-title: Physiol Meas – volume: 123 year: 2023 ident: bib80 article-title: DCDA-Net: dual-convolutional dual-attention network for obstructive sleep apnea diagnosis from single-lead electrocardiograms publication-title: Eng Appl Artif Intell – volume: 29 start-page: 705 year: 2015 end-page: 712 ident: bib1 article-title: Obstructive sleep apnea, hypertension and cardiovascular diseases publication-title: J Hum Hypertens – volume: 40 year: 2023 ident: bib115 article-title: Application of explainable artificial intelligence in medical health: a systematic review of interpretability methods publication-title: Inform Med Unlocked – volume: 87 year: 2024 ident: bib57 article-title: Multiscale entropy analysis of single lead ECG and ECG derived respiration for AI based prediction of sleep apnea events publication-title: Biomed Signal Process Control – start-page: 255 year: 2000 ident: 10.1016/j.smrv.2025.102097_bib99 article-title: The apnea-ECG database – volume: 8 start-page: 95 year: 2018 ident: 10.1016/j.smrv.2025.102097_bib29 article-title: Obstructive sleep apnoea detection using convolutional neural network based deep learning framework publication-title: Biomed Eng Lett doi: 10.1007/s13534-017-0055-y – volume: 23 start-page: 1011 year: 2019 ident: 10.1016/j.smrv.2025.102097_bib94 article-title: Automatic detection of obstructive sleep apnea using wavelet Transform and entropy-based features from single-lead ECG signal publication-title: IEEE J Biomed Health Inform doi: 10.1109/JBHI.2018.2842919 – volume: 11 start-page: 6622 year: 2021 ident: 10.1016/j.smrv.2025.102097_bib71 article-title: Diagnosis of obstructive sleep apnea from ECG signals using machine learning and deep learning classifiers publication-title: Appl Sci doi: 10.3390/app11146622 – volume: 162 start-page: 571 year: 2023 ident: 10.1016/j.smrv.2025.102097_bib28 article-title: RAFNet: restricted attention fusion network for sleep apnea detection publication-title: Neural Netw doi: 10.1016/j.neunet.2023.03.019 – volume: 12 start-page: 1509 year: 2022 ident: 10.1016/j.smrv.2025.102097_bib62 article-title: A deep learning framework for automatic sleep apnea classification based on Empirical mode decomposition derived from single-lead electrocardiogram publication-title: Life doi: 10.3390/life12101509 – volume: 12 start-page: 119 year: 2022 ident: 10.1016/j.smrv.2025.102097_bib33 article-title: Sleep apnea detection based on multi-scale residual network publication-title: Life doi: 10.3390/life12010119 – volume: 8 start-page: 551 year: 2013 ident: 10.1016/j.smrv.2025.102097_bib43 article-title: Sleep apnoea detection from ECG using features extracted from reconstructed phase space and frequency domain publication-title: Biomed Signal Process Control doi: 10.1016/j.bspc.2013.05.007 – volume: 25 start-page: 399 year: 2023 ident: 10.1016/j.smrv.2025.102097_bib64 article-title: Sleep apnea detection using wavelet scattering transformation and random forest classifier publication-title: Entropy doi: 10.3390/e25030399 – volume: 3 start-page: 125 year: 2022 ident: 10.1016/j.smrv.2025.102097_bib12 article-title: A clinician's guide to understanding and critically appraising machine learning studies: a checklist for Ruling Out Bias Using Standard Tools in Machine Learning (ROBUST-ML) publication-title: Eur Heart J Digit Health doi: 10.1093/ehjdh/ztac016 – volume: vol. 167 year: 2014 ident: 10.1016/j.smrv.2025.102097_bib18 – volume: 87 year: 2024 ident: 10.1016/j.smrv.2025.102097_bib57 article-title: Multiscale entropy analysis of single lead ECG and ECG derived respiration for AI based prediction of sleep apnea events publication-title: Biomed Signal Process Control doi: 10.1016/j.bspc.2023.105444 – volume: 34 start-page: 70 year: 2017 ident: 10.1016/j.smrv.2025.102097_bib2 article-title: Prevalence of obstructive sleep apnea in the general population: a systematic review publication-title: Sleep Med Rev doi: 10.1016/j.smrv.2016.07.002 – volume: 195 year: 2020 ident: 10.1016/j.smrv.2025.102097_bib95 article-title: Performance evaluation of the spectral autocorrelation function and autoregressive models for automated sleep apnea detection using single-lead ECG signal publication-title: Comput Methods Progr Biomed doi: 10.1016/j.cmpb.2020.105626 – volume: 123 year: 2023 ident: 10.1016/j.smrv.2025.102097_bib80 article-title: DCDA-Net: dual-convolutional dual-attention network for obstructive sleep apnea diagnosis from single-lead electrocardiograms publication-title: Eng Appl Artif Intell doi: 10.1016/j.engappai.2023.106451 – volume: 41 start-page: 6427 year: 2022 ident: 10.1016/j.smrv.2025.102097_bib34 article-title: Signal matched multirate Filter Bank design for optimum coding Gain and its application in real-time sleep apnea detection publication-title: Circ Syst Signal Process doi: 10.1007/s00034-022-02092-6 – volume: 22 start-page: 510 year: 2022 ident: 10.1016/j.smrv.2025.102097_bib91 article-title: Contribution of different Subbands of ECG in sleep apnea detection evaluated using Filter Bank decomposition and a convolutional neural network publication-title: Sensors doi: 10.3390/s22020510 – volume: 12 year: 2021 ident: 10.1016/j.smrv.2025.102097_bib8 article-title: Practical lessons on 12-lead ECG classification: meta-analysis of methods from PhysioNet/computing in cardiology challenge 2020 publication-title: Front Physiol – volume: 77 start-page: 116 year: 2016 ident: 10.1016/j.smrv.2025.102097_bib68 article-title: An algorithm for sleep apnea detection from single-lead ECG using Hermite basis functions publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2016.08.012 – volume: 39 start-page: 1527 year: 2022 ident: 10.1016/j.smrv.2025.102097_bib89 article-title: Dealing with imbalanced sleep apnea data using DCGAN publication-title: Tob Situat (US Dept Agric Econ Res Serv) – year: 2025 ident: 10.1016/j.smrv.2025.102097_bib114 article-title: Sleep apnea detection using EEG: a systematic review of datasets, methods, challenges, and future directions publication-title: Ann Biomed Eng doi: 10.1007/s10439-025-03691-5 – volume: 132 start-page: 159 year: 2014 ident: 10.1016/j.smrv.2025.102097_bib78 article-title: Building a Cepstrum-HMM kernel for apnea identification publication-title: Neurocomputing doi: 10.1016/j.neucom.2013.04.048 – start-page: 1276 year: 2021 ident: 10.1016/j.smrv.2025.102097_bib27 article-title: A lightweight multi-scaled fusion network for sleep apnea detection using single-lead ECG signals – volume: 39 start-page: 206 year: 2018 ident: 10.1016/j.smrv.2025.102097_bib44 article-title: Sleep apnea detection from single-lead ECG using features based on ECG-derived respiration (EDR) signals publication-title: IRBM doi: 10.1016/j.irbm.2018.03.002 – volume: 17 start-page: 3092 year: 2017 ident: 10.1016/j.smrv.2025.102097_bib73 article-title: Real-time classification of healthy and apnea subjects using ECG signals with variational mode decomposition publication-title: IEEE Sens J doi: 10.1109/JSEN.2017.2690805 – volume: 100 start-page: 100 year: 2018 ident: 10.1016/j.smrv.2025.102097_bib66 article-title: Application of an optimal class of antisymmetric wavelet filter banks for obstructive sleep apnea diagnosis using ECG signals publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2018.06.011 – volume: 140 year: 2022 ident: 10.1016/j.smrv.2025.102097_bib90 article-title: Obstructive sleep apnea detection from single-lead electrocardiogram signals using one-dimensional squeeze-and-excitation residual group network publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2021.105124 – volume: 33 start-page: 493 issue: 3 year: 2023 ident: 10.1016/j.smrv.2025.102097_bib45 article-title: Constant Q–Transform–based deep learning architecture for detection of obstructive sleep apnea publication-title: Int J Appl Math Comput Sci doi: 10.34768/amcs-2023-0036 – volume: 12 start-page: 106 year: 2015 ident: 10.1016/j.smrv.2025.102097_bib26 article-title: An automatic screening approach for obstructive sleep apnea diagnosis based on single-lead electrocardiogram publication-title: IEEE Trans Automat Sci Eng doi: 10.1109/TASE.2014.2345667 – volume: 9 start-page: 591 year: 2020 ident: 10.1016/j.smrv.2025.102097_bib3 article-title: Sleep apnea, hypertension and the sympathetic nervous system in the adult population publication-title: J Clin Med doi: 10.3390/jcm9020591 – volume: 68 year: 2021 ident: 10.1016/j.smrv.2025.102097_bib32 article-title: Obstructive sleep apnea screening from unprocessed ECG signals using statistical modelling publication-title: Biomed Signal Process Control doi: 10.1016/j.bspc.2021.102685 – volume: 118 year: 2021 ident: 10.1016/j.smrv.2025.102097_bib21 article-title: AIOSA: an approach to the automatic identification of obstructive sleep apnea events based on deep learning publication-title: Artif Intell Med doi: 10.1016/j.artmed.2021.102133 – volume: 91 start-page: 47 year: 2017 ident: 10.1016/j.smrv.2025.102097_bib50 article-title: Heart rate variability feature selection in the presence of sleep apnea: an expert system for the characterization and detection of the disorder publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2017.10.004 – volume: 38 start-page: 635 year: 2011 ident: 10.1016/j.smrv.2025.102097_bib102 article-title: The European Sleep Apnoea Database (ESADA): report from 22 European sleep laboratories publication-title: Eur Respir J doi: 10.1183/09031936.00046710 – volume: 100 start-page: 44 year: 2017 ident: 10.1016/j.smrv.2025.102097_bib5 article-title: Comparison of home sleep apnea testing versus laboratory polysomnography for the diagnosis of obstructive sleep apnea in children publication-title: Int J Pediatr Otorhinolaryngol doi: 10.1016/j.ijporl.2017.06.013 – volume: 18 start-page: 1285 year: 2014 ident: 10.1016/j.smrv.2025.102097_bib55 article-title: An online sleep apnea detection method based on Recurrence Quantification analysis publication-title: IEEE J Biomed Health Inform doi: 10.1109/JBHI.2013.2292928 – volume: 26 year: 2024 ident: 10.1016/j.smrv.2025.102097_bib107 article-title: Detection of sleep apnea using wearable AI: systematic review and meta-analysis publication-title: J Med Internet Res – volume: 63 start-page: 1532 year: 2016 ident: 10.1016/j.smrv.2025.102097_bib74 article-title: An obstructive sleep apnea detection approach using a discriminative hidden Markov model from ECG signals publication-title: IEEE Trans Biomed Eng doi: 10.1109/TBME.2015.2498199 – volume: 13 start-page: 221 year: 2009 ident: 10.1016/j.smrv.2025.102097_bib4 article-title: Clinical diagnosis of sleep apnea based on single night of polysomnography vs. two nights of polysomnography publication-title: Sleep Breath doi: 10.1007/s11325-008-0234-2 – start-page: 518 year: 2023 ident: 10.1016/j.smrv.2025.102097_bib38 article-title: Hybrid RNN-based classification of obstructive sleep apnea using single-lead ECG signals – volume: 26 start-page: 5428 year: 2022 ident: 10.1016/j.smrv.2025.102097_bib92 article-title: Robust method for screening sleep apnea with single-lead ECG using deep residual network: evaluation with open database and patch-type wearable device data publication-title: IEEE J Biomed Health Inform doi: 10.1109/JBHI.2022.3203560 – start-page: 1382 year: 2021 ident: 10.1016/j.smrv.2025.102097_bib16 article-title: Detection of obstructive sleep apnoea by ECG signals using deep learning architectures – volume: 473 start-page: 24 year: 2022 ident: 10.1016/j.smrv.2025.102097_bib60 article-title: A dual-model deep learning method for sleep apnea detection based on representation learning and temporal dependence publication-title: Neurocomputing doi: 10.1016/j.neucom.2021.12.001 – volume: 83 year: 2019 ident: 10.1016/j.smrv.2025.102097_bib58 article-title: Towards an accurate sleep apnea detection based on ECG signal: the quintessential of a wise feature selection publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2019.105568 – volume: 62 start-page: 2269 year: 2015 ident: 10.1016/j.smrv.2025.102097_bib83 article-title: A Novel algorithm for the automatic detection of sleep apnea from single-lead ECG publication-title: IEEE Trans Biomed Eng doi: 10.1109/TBME.2015.2422378 – volume: 237 year: 2024 ident: 10.1016/j.smrv.2025.102097_bib117 article-title: An EEG-based cross-subject interpretable CNN for game player expertise level classification publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2023.121658 – volume: 8 start-page: 92 year: 2024 ident: 10.1016/j.smrv.2025.102097_bib118 article-title: HMS-TENet: a hierarchical multi-scale topological enhanced network based on EEG and EOG for driver vigilance estimation publication-title: Biomedical Technology doi: 10.1016/j.bmt.2024.10.003 – volume: 11 start-page: 192 year: 2021 ident: 10.1016/j.smrv.2025.102097_bib49 article-title: Sleep apnea classification algorithm development using a machine-learning framework and Bag-of-features derived from electrocardiogram spectrograms publication-title: JCM doi: 10.3390/jcm11010192 – volume: 68 year: 2023 ident: 10.1016/j.smrv.2025.102097_bib111 article-title: Portable evaluation of obstructive sleep apnea in adults: a systematic review publication-title: Sleep Med Rev doi: 10.1016/j.smrv.2022.101743 – volume: 58 start-page: 882 year: 2005 ident: 10.1016/j.smrv.2025.102097_bib104 article-title: The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed publication-title: J Clin Epidemiol doi: 10.1016/j.jclinepi.2005.01.016 – volume: 294 start-page: 94 year: 2018 ident: 10.1016/j.smrv.2025.102097_bib48 article-title: A method to detect sleep apnea based on deep neural network and hidden Markov model using single-lead ECG signal publication-title: Neurocomputing doi: 10.1016/j.neucom.2018.03.011 – volume: 84 year: 2023 ident: 10.1016/j.smrv.2025.102097_bib75 article-title: ApneaNet: a hybrid 1DCNN-LSTM architecture for detection of Obstructive Sleep Apnea using digitized ECG signals publication-title: Biomed Signal Process Control doi: 10.1016/j.bspc.2023.104754 – volume: 12 start-page: 762 year: 2018 ident: 10.1016/j.smrv.2025.102097_bib76 article-title: Online obstructive sleep apnea detection on medical wearable sensors publication-title: IEEE Trans Biomed Circuits Syst doi: 10.1109/TBCAS.2018.2824659 – volume: 150 year: 2022 ident: 10.1016/j.smrv.2025.102097_bib9 article-title: A review of automated sleep disorder detection publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2022.106100 – volume: 21 start-page: 5425 year: 2021 ident: 10.1016/j.smrv.2025.102097_bib52 article-title: Ensemble of deep learning models for sleep apnea detection: an experimental study publication-title: Sensors doi: 10.3390/s21165425 – volume: 14 start-page: 70 year: 2014 ident: 10.1016/j.smrv.2025.102097_bib103 article-title: Investigation of publication bias in meta-analyses of diagnostic test accuracy: a meta-epidemiological study publication-title: BMC Med Res Methodol doi: 10.1186/1471-2288-14-70 – volume: 78 year: 2024 ident: 10.1016/j.smrv.2025.102097_bib112 article-title: Diagnostic accuracy of portable sleep monitors in pediatric sleep apnea: a systematic review publication-title: Sleep Med Rev doi: 10.1016/j.smrv.2024.101991 – volume: 14 start-page: 191 year: 2023 ident: 10.1016/j.smrv.2025.102097_bib17 article-title: Sleep apnea detection from ECG signal using deep CNN-based structures publication-title: Evolving Systems doi: 10.1007/s12530-022-09445-1 – volume: 29 start-page: 705 year: 2015 ident: 10.1016/j.smrv.2025.102097_bib1 article-title: Obstructive sleep apnea, hypertension and cardiovascular diseases publication-title: J Hum Hypertens doi: 10.1038/jhh.2015.15 – volume: 71 start-page: 1 year: 2022 ident: 10.1016/j.smrv.2025.102097_bib41 article-title: A hybrid transformer model for obstructive sleep apnea detection based on Self-attention Mechanism using single-lead ECG publication-title: IEEE Trans Instrum Meas – volume: 16 year: 2019 ident: 10.1016/j.smrv.2025.102097_bib67 article-title: A new approach to identify obstructive sleep apnea using an optimal orthogonal wavelet filter bank with ECG signals publication-title: Inform Med Unlocked doi: 10.1016/j.imu.2019.100170 – volume: 40 year: 2023 ident: 10.1016/j.smrv.2025.102097_bib115 article-title: Application of explainable artificial intelligence in medical health: a systematic review of interpretability methods publication-title: Inform Med Unlocked doi: 10.1016/j.imu.2023.101286 – volume: 176 start-page: 93 year: 2019 ident: 10.1016/j.smrv.2025.102097_bib85 article-title: A RR interval based automated apnea detection approach using residual network publication-title: Comput Methods Progr Biomed doi: 10.1016/j.cmpb.2019.05.002 – volume: 319 start-page: 388 year: 2018 ident: 10.1016/j.smrv.2025.102097_bib10 article-title: Preferred reporting Items for a systematic review and meta-analysis of diagnostic test accuracy studies: the PRISMA-DTA statement publication-title: JAMA doi: 10.1001/jama.2017.19163 – volume: 42 year: 2021 ident: 10.1016/j.smrv.2025.102097_bib54 article-title: Obstructive sleep apnea prediction from electrocardiogram scalograms and spectrograms using convolutional neural networks publication-title: Physiol Meas doi: 10.1088/1361-6579/ac0a9c – volume: 39 year: 2018 ident: 10.1016/j.smrv.2025.102097_bib82 article-title: Multiclass classification of obstructive sleep apnea/hypopnea based on a convolutional neural network from a single-lead electrocardiogram publication-title: Physiol Meas doi: 10.1088/1361-6579/aac7b7 – volume: 71 start-page: 1 year: 2022 ident: 10.1016/j.smrv.2025.102097_bib37 article-title: OSACN-net: automated classification of sleep apnea using deep learning model and smoothed Gabor spectrograms of ECG signal publication-title: IEEE Trans Instrum Meas – volume: 42 start-page: 104 year: 2018 ident: 10.1016/j.smrv.2025.102097_bib81 article-title: Automated detection of obstructive sleep apnea events from a single-lead electrocardiogram using a convolutional neural network publication-title: J Med Syst doi: 10.1007/s10916-018-0963-0 – volume: 19 year: 2019 ident: 10.1016/j.smrv.2025.102097_bib72 article-title: A Novel approach OSA detection using single-lead ECG scalogram based on deep neural network publication-title: J Mech Med Biol doi: 10.1142/S021951941950026X – volume: 20 start-page: 4157 year: 2020 ident: 10.1016/j.smrv.2025.102097_bib24 article-title: A sleep apnea detection system based on a one-dimensional deep convolution neural network model using single-lead electrocardiogram publication-title: Sensors doi: 10.3390/s20154157 – volume: 7 start-page: 2852 issue: 11 year: 2024 ident: 10.1016/j.smrv.2025.102097_bib20 article-title: Detection of sleep apnea in ECG signal using PAN- TOMPKINS algorithm and ANN classifiers publication-title: Compusoft: An International Journal of Advanced Computer Technology [Internet] – volume: 80 year: 2023 ident: 10.1016/j.smrv.2025.102097_bib46 article-title: Automatic detection of sleep apnea from single-lead ECG signal using enhanced-deep belief network model publication-title: Biomed Signal Process Control doi: 10.1016/j.bspc.2022.104401 – volume: 146 year: 2023 ident: 10.1016/j.smrv.2025.102097_bib116 article-title: An EEG-based brain cognitive dynamic recognition network for representations of brain fatigue publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2023.110613 – volume: 7 year: 2019 ident: 10.1016/j.smrv.2025.102097_bib86 article-title: Sleep apnea detection from a single-lead ECG signal with automatic feature-extraction through a modified LeNet-5 convolutional neural network publication-title: PeerJ – volume: 13 start-page: 293 year: 2023 ident: 10.1016/j.smrv.2025.102097_bib6 article-title: Systematic review of automated sleep apnea detection based on physiological signal data using deep learning algorithm: a meta-analysis approach publication-title: Biomed Eng Lett doi: 10.1007/s13534-023-00297-5 – volume: 71 start-page: 1 year: 2022 ident: 10.1016/j.smrv.2025.102097_bib19 article-title: Sleep apnea detection from single-lead ECG: a comprehensive analysis of machine learning and deep learning algorithms publication-title: IEEE Trans Instrum Meas – volume: 3 year: 2020 ident: 10.1016/j.smrv.2025.102097_bib106 article-title: Assessment of Mandibular movement monitoring with machine learning analysis for the diagnosis of obstructive sleep apnea publication-title: JAMA Netw Open doi: 10.1001/jamanetworkopen.2019.19657 – volume: 4 start-page: 223 year: 2008 ident: 10.1016/j.smrv.2025.102097_bib113 article-title: Electrocardiogram recording as a screening tool for sleep disordered breathing publication-title: J Clin Sleep Med doi: 10.5664/jcsm.27184 – volume: 202 year: 2022 ident: 10.1016/j.smrv.2025.102097_bib23 article-title: Multi-task feature fusion network for Obstructive Sleep Apnea detection using single-lead ECG signal publication-title: Measurement doi: 10.1016/j.measurement.2022.111787 – volume: 271 start-page: R1078 year: 1996 ident: 10.1016/j.smrv.2025.102097_bib101 article-title: Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics publication-title: Am J Physiol – volume: 8 start-page: 200477 year: 2020 ident: 10.1016/j.smrv.2025.102097_bib59 article-title: Classifier precision analysis for sleep apnea detection using ECG signals publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3036024 – volume: 187 year: 2022 ident: 10.1016/j.smrv.2025.102097_bib110 article-title: Detection of sleep apnea using Machine learning algorithms based on ECG Signals: a comprehensive systematic review publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2021.115950 – volume: 22 start-page: 1895 year: 2018 ident: 10.1016/j.smrv.2025.102097_bib30 article-title: Frequency network analysis of heart rate variability for obstructive apnea patient detection publication-title: IEEE J Biomed Health Inform doi: 10.1109/JBHI.2017.2784415 – volume: 79 start-page: 15813 year: 2020 ident: 10.1016/j.smrv.2025.102097_bib88 article-title: Obstructive sleep apnea detection using ecg-sensor with convolutional neural networks publication-title: Multimed Tool Appl doi: 10.1007/s11042-018-6161-8 – start-page: 1 year: 2020 ident: 10.1016/j.smrv.2025.102097_bib77 article-title: Detection of obstructive sleep apnoea using features extracted from segmented time-series ECG signals using a one dimensional convolutional neural network – volume: 42 year: 2021 ident: 10.1016/j.smrv.2025.102097_bib93 article-title: {FASSNet}: fast apnea syndrome screening neural network based on single-lead electrocardiogram for wearable devices publication-title: Physiol Meas doi: 10.1088/1361-6579/ac184e – volume: 2273 year: 2022 ident: 10.1016/j.smrv.2025.102097_bib109 article-title: Comparison of machine learning and deep learning classifier to detect sleep apnea using single-channel ECG and HRV: a Systematic Literature Review publication-title: J Phys Conf Ser doi: 10.1088/1742-6596/2273/1/012015 – volume: 43 year: 2022 ident: 10.1016/j.smrv.2025.102097_bib7 article-title: A systematic review of deep learning methods for modeling electrocardiograms during sleep publication-title: Physiol Meas doi: 10.1088/1361-6579/ac826e – ident: 10.1016/j.smrv.2025.102097_bib100 – volume: 2019 start-page: 1 year: 2019 ident: 10.1016/j.smrv.2025.102097_bib87 article-title: Detection of sleep apnea from single-lead ECG signal using a time Window artificial neural network publication-title: BioMed Res Int – volume: 70 start-page: 1 year: 2021 ident: 10.1016/j.smrv.2025.102097_bib36 article-title: A sleep apnea detection method based on unsupervised feature learning and single-lead electrocardiogram publication-title: IEEE Trans Instrum Meas – start-page: 1840 year: 2022 ident: 10.1016/j.smrv.2025.102097_bib97 article-title: OSA-CCNN: obstructive sleep apnea detection based on a composite deep convolution neural network model using single-lead ECG signal – volume: 23 start-page: 4692 year: 2023 ident: 10.1016/j.smrv.2025.102097_bib39 article-title: Efficient deep learning based hybrid model to detect obstructive sleep apnea publication-title: Sensors doi: 10.3390/s23104692 – volume: 6 year: 2020 ident: 10.1016/j.smrv.2025.102097_bib69 article-title: Sleep apnea detection from ECG using variational mode decomposition publication-title: Biomed Phys Eng Express doi: 10.1088/2057-1976/ab68e9 – volume: 41 start-page: 758 year: 2021 ident: 10.1016/j.smrv.2025.102097_bib65 article-title: End-to-End sleep apnea detection using single-lead ECG signal and 1-D residual neural networks publication-title: J Med Biol Eng doi: 10.1007/s40846-021-00646-8 – volume: 70 start-page: 1 year: 2021 ident: 10.1016/j.smrv.2025.102097_bib70 article-title: Multiscale deep neural network for obstructive sleep apnea detection using RR interval from single-lead ECG signal publication-title: IEEE Trans Instrum Meas – volume: 26 start-page: 5841 year: 2022 ident: 10.1016/j.smrv.2025.102097_bib63 article-title: Obstructive sleep apnea detection Scheme based on manually generated features and Parallel heterogeneous deep learning model under IoMT publication-title: IEEE J Biomed Health Inform doi: 10.1109/JBHI.2022.3166859 – volume: 134 year: 2021 ident: 10.1016/j.smrv.2025.102097_bib51 article-title: SCNN: scalogram-based convolutional neural network to detect obstructive sleep apnea using single-lead electrocardiogram signals publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2021.104532 – volume: 155 start-page: 529 year: 2011 ident: 10.1016/j.smrv.2025.102097_bib11 article-title: QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies publication-title: Ann Intern Med doi: 10.7326/0003-4819-155-8-201110180-00009 – volume: 17 start-page: 24 year: 2020 ident: 10.1016/j.smrv.2025.102097_bib40 article-title: Identification of obstructive sleep apnea using artificial neural networks and wavelet Packet decomposition of the HRV signal publication-title: Jou Eng Res doi: 10.24200/tjer.vol17iss1pp24-33 – volume: 16 year: 2021 ident: 10.1016/j.smrv.2025.102097_bib56 article-title: A fused-image-based approach to detect obstructive sleep apnea using a single-lead ECG and a 2D convolutional neural network publication-title: PLoS One doi: 10.1371/journal.pone.0250618 – volume: 229 year: 2023 ident: 10.1016/j.smrv.2025.102097_bib14 article-title: Optimization of CNN using modified honey badger algorithm for sleep apnea detection publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2023.120484 – volume: 57 start-page: 1931 year: 2022 ident: 10.1016/j.smrv.2025.102097_bib105 article-title: Reliability of machine learning to diagnose pediatric obstructive sleep apnea: systematic review and meta‐analysis publication-title: Pediatr Pulmonol doi: 10.1002/ppul.25423 – volume: 72 start-page: 1 year: 2023 ident: 10.1016/j.smrv.2025.102097_bib47 article-title: TFFormer: a time–frequency information fusion-based CNN-transformer model for OSA detection with single-lead ECG publication-title: IEEE Trans Instrum Meas – volume: 29 start-page: 36 year: 2025 ident: 10.1016/j.smrv.2025.102097_bib108 article-title: Artificial intelligence facial recognition of obstructive sleep apnea: a Bayesian meta-analysis publication-title: Sleep Breath doi: 10.1007/s11325-024-03173-3 – volume: 22 start-page: 306 year: 2022 ident: 10.1016/j.smrv.2025.102097_bib13 article-title: Meta-DiSc 2.0: a web application for meta-analysis of diagnostic test accuracy data publication-title: BMC Med Res Methodol doi: 10.1186/s12874-022-01788-2 – volume: 101 start-page: e215 year: 2000 ident: 10.1016/j.smrv.2025.102097_bib98 article-title: PhysioBank, PhysioToolkit, and PhysioNet publication-title: Circulation doi: 10.1161/01.CIR.101.23.e215 – volume: 12 year: 2022 ident: 10.1016/j.smrv.2025.102097_bib42 article-title: Obstructive sleep apnea detection using frequency analysis of electrocardiographic RR interval and machine learning algorithms publication-title: J Biomed Phys Eng – volume: 61 year: 2020 ident: 10.1016/j.smrv.2025.102097_bib35 article-title: Detection of apnea events from ECG segments using Fourier decomposition method publication-title: Biomed Signal Process Control doi: 10.1016/j.bspc.2020.102005 – volume: 41 start-page: 241 year: 2020 ident: 10.1016/j.smrv.2025.102097_bib22 article-title: Detection of abnormal respiratory events with single channel ECG and hybrid machine learning model in patients with obstructive sleep apnea publication-title: IRBM doi: 10.1016/j.irbm.2020.05.006 – volume: 180 year: 2019 ident: 10.1016/j.smrv.2025.102097_bib31 article-title: Deep learning approaches for automatic detection of sleep apnea events from an electrocardiogram publication-title: Comput Methods Progr Biomed doi: 10.1016/j.cmpb.2019.105001 – volume: 16 year: 2022 ident: 10.1016/j.smrv.2025.102097_bib25 article-title: A spatio-temporal learning-based model for sleep apnea detection using single-lead ECG signals publication-title: Front Neurosci |
| SSID | ssj0009416 |
| Score | 2.4754877 |
| SecondaryResourceType | review_article |
| Snippet | Sleep apnea is a prevalent disorder affecting 10 % of middle-aged individuals, yet it remains underdiagnosed due to the limitations of polysomnography (PSG),... AbstractSleep apnea is a prevalent disorder affecting 10 % of middle-aged individuals, yet it remains underdiagnosed due to the limitations of polysomnography... Sleep apnea is a prevalent disorder affecting 10 % of middle-aged individuals, yet it remains underdiagnosed due to the limitations of polysomnography (PSG),... |
| SourceID | proquest pubmed crossref elsevier |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 102097 |
| SubjectTerms | Algorithms Deep Learning Diagnostic accuracy Electrocardiogram (ECG) Electrocardiography - methods Humans Machine learning Machine Learning - standards Meta-analysis Neurology Polysomnography - methods Sensitivity and Specificity Single-lead ECG Sleep apnea Sleep Apnea Syndromes - diagnosis Sleep Medicine |
| Title | Diagnostic accuracy of machine learning algorithms in electrocardiogram-based sleep apnea detection: A systematic review and meta-analysis |
| URI | https://www.clinicalkey.com/#!/content/1-s2.0-S1087079225000504 https://www.clinicalkey.es/playcontent/1-s2.0-S1087079225000504 https://dx.doi.org/10.1016/j.smrv.2025.102097 https://www.ncbi.nlm.nih.gov/pubmed/40349509 https://www.proquest.com/docview/3202859292 |
| Volume | 81 |
| WOSCitedRecordID | wos001491174300001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1532-2955 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0009416 issn: 1087-0792 databaseCode: AIEXJ dateStart: 19971101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3bitswEBXZbB8KpfTe9LKo0Lfg4NhSbPctbFN6odvCbiFvQpHlxFvHCbYTln5C_7R_0dHFcbLbbC_QF2NsjS1nTkYj6cwMQi-DgAZ9OaBOLHzqkIkfOSqLt0Mj4vM48iYiNMUmgpOTcDyOPrdaP-pYmHUW5Hl4cREt_6uq4RooW4XO_oW6Nw-FC3AOSocjqB2Of6T414Y8pxOxCrEqVD13tYmuWZOyLhMx7fJsuijSajbXjFhbDkdoeqpibDlqfIu7ZSblssuXueTdWFaauJWbcPbLSaANt3guK-5wm-pk2_U91U-q9_KtSLOVlGapLrb-UUV0JRwMdEPvGcI5SOnbcgZv6I7macMghm_UtvPTdPVtBvZq9LWwC9LKnKWZSimymGYrPdyk01QRscu02l7w8GhDzLI22g3V6mq0Y8RN3RdrhcFpcg3r147pzYUrI4ZZvDjvlfNi3VOv621Lb6fnHh1_6Dul13OdU9UF1QOP6gQ6ZLexmVyx0mMuu9L0AB16AY3CNjocvhuN3zcZoomu07v5OhvhZciIl3u3z4vaN0vS3tLZHXTbTnPw0MDzLmrJ_B66ZdaIsQl9u4--N1DFNVTxIsEWqriGKm6gitMc74Eq1lDFGqp4A9VXeIgboGKDOgxAxTtAfYC-vBmdHb91bG0QR5ABqZyQuJJORBLwJBJcqFWISd8TJExIQGNX9pPIB9dTpbMjMuYRiQPqJW4cJj4HH3XiP0TtfJHLxwhz5UT3_TgAa0ZifxD6IReJxz2VW48Oog7q1781EzZxvqrfkrGaIXnOlH6Y0g8z-umg7kZmadLGXNvar1XI6oBoGMIZYPRaqeBXUrK0xqhk-zDYQXQjaR1t40D_9o0vanwxGIXU1iLP5WJVMh9ahRSmWl4HPTLA23w3USmwYF7y5J_7-xTdbAzBM9SuipV8jm6IdZWWxRE6CMbhkf07_QR04Q81 |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Diagnostic+accuracy+of+machine+learning+algorithms+in+electrocardiogram-based+sleep+apnea+detection%3A+A+systematic+review+and+meta-analysis&rft.jtitle=Sleep+medicine+reviews&rft.au=Kilic%2C+Mustafa+Eray&rft.au=Arayici%2C+Mehmet+Emin&rft.au=Turan%2C+Oguzhan+Ekrem&rft.au=Yilancioglu%2C+Yigit+Resit&rft.date=2025-06-01&rft.issn=1087-0792&rft.volume=81&rft.spage=102097&rft.epage=102097&rft_id=info:doi/10.1016%2Fj.smrv.2025.102097&rft.externalDBID=ECK1-s2.0-S1087079225000504&rft.externalDocID=1_s2_0_S1087079225000504 |
| thumbnail_m | http://cvtisr.summon.serialssolutions.com/2.0.0/image/custom?url=https%3A%2F%2Fcdn.clinicalkey.com%2Fck-thumbnails%2F10870792%2FS1087079225X00022%2Fcov150h.gif |