Cascaded redundant convolutional encoder-decoder network improved apnea detection performance using tracheal sounds in post anesthesia care unit patients
Methods of detecting apnea based on acoustic features can be prone to misdiagnosed and missed diagnoses due to the influence of noise. The aim of this paper is to improve the performance of apnea detection algorithms in the Post Anesthesia Care Unit (PACU) using a denoising method that processes tra...
Gespeichert in:
| Veröffentlicht in: | Biomedical physics & engineering express Jg. 10; H. 6 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
England
01.11.2024
|
| Schlagworte: | |
| ISSN: | 2057-1976, 2057-1976 |
| Online-Zugang: | Weitere Angaben |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Methods of detecting apnea based on acoustic features can be prone to misdiagnosed and missed diagnoses due to the influence of noise. The aim of this paper is to improve the performance of apnea detection algorithms in the Post Anesthesia Care Unit (PACU) using a denoising method that processes tracheal sounds without the need for separate background noise. Approach: Tracheal sound data from laboratory subjects was collected using a microphone. Record a segment of clinical background noise and clean tracheal sound data to synthesize the noisy tracheal sound data according to a specified signal-to-noise ratio. Extract the frequency-domain features of the tracheal sounds using the Short Time Fourier Transform (STFT) and input the Cascaded Redundant Convolutional Encoder-Decoder network (CR-CED) network for training. Patients' tracheal sound data collected in the PACU were then fed into the CR-CED network as test data and inversely transformed by STFT to obtain denoised tracheal sounds. The apnea detection algorithm was used to detect the tracheal sound after denoising. Results: Apnea events were correctly detected 207 times and normal respiratory events 11,305 times using tracheal sounds denoised by the CR-CED network. The sensitivity and specificity of apnea detection were 88% and 98.6%, respectively. Significance: The apnea detection results of tracheal sounds after CR-CED network denoising in the PACU are accurate and reliable. Tracheal sound can be denoised using this approach without separate background noise. It effectively improves the applicability of the tracheal sound denoising method in the medical environment while ensuring its correctness.
. |
|---|---|
| AbstractList | Objective. Methods of detecting apnea based on acoustic features can be prone to misdiagnosed and missed diagnoses due to the influence of noise. The aim of this paper is to improve the performance of apnea detection algorithms in the Post Anesthesia Care Unit (PACU) using a denoising method that processes tracheal sounds without the need for separate background noise.Approach. Tracheal sound data from laboratory subjects was collected using a microphone. Record a segment of clinical background noise and clean tracheal sound data to synthesize the noisy tracheal sound data according to a specified signal-to-noise ratio. Extract the frequency-domain features of the tracheal sounds using the Short Time Fourier Transform (STFT) and input the Cascaded Redundant Convolutional Encoder-Decoder network (CR-CED) network for training. Patients' tracheal sound data collected in the PACU were then fed into the CR-CED network as test data and inversely transformed by STFT to obtain denoised tracheal sounds. The apnea detection algorithm was used to detect the tracheal sound after denoising.Results. Apnea events were correctly detected 207 times and normal respiratory events 11,305 times using tracheal sounds denoised by the CR-CED network. The sensitivity and specificity of apnea detection were 88% and 98.6%, respectively.Significance. The apnea detection results of tracheal sounds after CR-CED network denoising in the PACU are accurate and reliable. Tracheal sound can be denoised using this approach without separate background noise. It effectively improves the applicability of the tracheal sound denoising method in the medical environment while ensuring its correctness.Objective. Methods of detecting apnea based on acoustic features can be prone to misdiagnosed and missed diagnoses due to the influence of noise. The aim of this paper is to improve the performance of apnea detection algorithms in the Post Anesthesia Care Unit (PACU) using a denoising method that processes tracheal sounds without the need for separate background noise.Approach. Tracheal sound data from laboratory subjects was collected using a microphone. Record a segment of clinical background noise and clean tracheal sound data to synthesize the noisy tracheal sound data according to a specified signal-to-noise ratio. Extract the frequency-domain features of the tracheal sounds using the Short Time Fourier Transform (STFT) and input the Cascaded Redundant Convolutional Encoder-Decoder network (CR-CED) network for training. Patients' tracheal sound data collected in the PACU were then fed into the CR-CED network as test data and inversely transformed by STFT to obtain denoised tracheal sounds. The apnea detection algorithm was used to detect the tracheal sound after denoising.Results. Apnea events were correctly detected 207 times and normal respiratory events 11,305 times using tracheal sounds denoised by the CR-CED network. The sensitivity and specificity of apnea detection were 88% and 98.6%, respectively.Significance. The apnea detection results of tracheal sounds after CR-CED network denoising in the PACU are accurate and reliable. Tracheal sound can be denoised using this approach without separate background noise. It effectively improves the applicability of the tracheal sound denoising method in the medical environment while ensuring its correctness. Methods of detecting apnea based on acoustic features can be prone to misdiagnosed and missed diagnoses due to the influence of noise. The aim of this paper is to improve the performance of apnea detection algorithms in the Post Anesthesia Care Unit (PACU) using a denoising method that processes tracheal sounds without the need for separate background noise. Approach: Tracheal sound data from laboratory subjects was collected using a microphone. Record a segment of clinical background noise and clean tracheal sound data to synthesize the noisy tracheal sound data according to a specified signal-to-noise ratio. Extract the frequency-domain features of the tracheal sounds using the Short Time Fourier Transform (STFT) and input the Cascaded Redundant Convolutional Encoder-Decoder network (CR-CED) network for training. Patients' tracheal sound data collected in the PACU were then fed into the CR-CED network as test data and inversely transformed by STFT to obtain denoised tracheal sounds. The apnea detection algorithm was used to detect the tracheal sound after denoising. Results: Apnea events were correctly detected 207 times and normal respiratory events 11,305 times using tracheal sounds denoised by the CR-CED network. The sensitivity and specificity of apnea detection were 88% and 98.6%, respectively. Significance: The apnea detection results of tracheal sounds after CR-CED network denoising in the PACU are accurate and reliable. Tracheal sound can be denoised using this approach without separate background noise. It effectively improves the applicability of the tracheal sound denoising method in the medical environment while ensuring its correctness.
. |
| Author | Jia, Xiuzhu Liu, Jing Zhang, Erpeng Wu, Yanan Yu, Lu |
| Author_xml | – sequence: 1 givenname: Erpeng surname: Zhang fullname: Zhang, Erpeng organization: Department of Biomedical Engineering, School of Intelligent Medicine , China Medical University, No. 77, Puhe Road, Shenyang North New Area, Shenyang, Liaoning, P. R. China, Shenyang, Liaoning, 110122, CHINA – sequence: 2 givenname: Xiuzhu surname: Jia fullname: Jia, Xiuzhu organization: Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, No. 77, Puhe Road, Shenyang North New Area, Shenyang, Liaoning, P. R. China, Shenyang, Liaoning, 110122, CHINA – sequence: 3 givenname: Yanan surname: Wu fullname: Wu, Yanan organization: College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, P. R. China, Shenyang, Liaoning, 110819, CHINA – sequence: 4 givenname: Jing surname: Liu fullname: Liu, Jing organization: Wuhan University Zhongnan Hospital, Wuhan, Hubei, P. R. China, Wuhan, Hubei, 430071, CHINA – sequence: 5 givenname: Lu orcidid: 0000-0002-2672-0116 surname: Yu fullname: Yu, Lu organization: Department of Biomedical Engineering, School of Intelligent Medicine , China Medical University, No.77 Puhe Road, Shenyang North New Area, Shenyang, Liaoning Province, P.R. China, Shenyang, Liaoning, 110122, CHINA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39437805$$D View this record in MEDLINE/PubMed |
| BookMark | eNpNkE9PwzAMxSM0xMbYnRPKkUtZ0q5pe0QT_yQkLnCu3MRlgTYpSTrER-HbksFAnJ5l_fxsv2MyMdYgIaecXXBWlsuU5UXCq0IsQZWVFAdk9tea_KunZOH9C2OMi1SIKj8i06xaZUXJ8hn5XIOXoFBRh2o0Ckyg0pqt7cagrYGOopFWoUsUfis1GN6te6W6H5zdxkEYDAJVGFDuRuiArrWuByORjl6bZxocyA1GL2_jCk91hKwPFAz6sEGvgUpwkTY60AGCRhP8CTlsofO42OucPF1fPa5vk_uHm7v15X0i07IKSYMZYiFglaVcyaZBIZgUbQupkkzhSnDFqjIXBZOtbErFYlYIeZNHWOWSpXNy_uMb33kb40F1r73ErovX2dHXGY8hphlnO_Rsj45Nj6oenO7BfdS_caZftWOAPQ |
| ContentType | Journal Article |
| Copyright | 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved. |
| Copyright_xml | – notice: 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved. |
| DBID | NPM 7X8 |
| DOI | 10.1088/2057-1976/ad89c6 |
| DatabaseName | PubMed MEDLINE - Academic |
| DatabaseTitle | PubMed MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic PubMed |
| 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 | no_fulltext_linktorsrc |
| EISSN | 2057-1976 |
| ExternalDocumentID | 39437805 |
| Genre | Journal Article |
| GroupedDBID | 53G AAGCD AAJIO AATNI ABHWH ABJNI ABVAM ACGFS ACHIP AFYNE AKPSB ALMA_UNASSIGNED_HOLDINGS AOAED ASPBG ATQHT CJUJL CRLBU EBS IJHAN IOP IZVLO KOT N5L NPM PJBAE RIN ROL RPA 7X8 ADEQX AEINN |
| ID | FETCH-LOGICAL-c289t-be3ee76a4321dcbbe660c6ffa2dc0de461d0985670cfcb8d0d89ea5b5dcbd5c02 |
| IEDL.DBID | 7X8 |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001346602800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2057-1976 |
| IngestDate | Fri Sep 05 11:35:57 EDT 2025 Wed Feb 19 02:03:48 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 6 |
| Keywords | Apnea Detection Anesthesia CR-CED Tracheal Sounds Denoising |
| Language | English |
| License | 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c289t-be3ee76a4321dcbbe660c6ffa2dc0de461d0985670cfcb8d0d89ea5b5dcbd5c02 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0002-2672-0116 |
| PMID | 39437805 |
| PQID | 3119723100 |
| PQPubID | 23479 |
| ParticipantIDs | proquest_miscellaneous_3119723100 pubmed_primary_39437805 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-11-01 |
| PublicationDateYYYYMMDD | 2024-11-01 |
| PublicationDate_xml | – month: 11 year: 2024 text: 2024-11-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England |
| PublicationTitle | Biomedical physics & engineering express |
| PublicationTitleAlternate | Biomed Phys Eng Express |
| PublicationYear | 2024 |
| SSID | ssj0001626695 |
| Score | 2.2727385 |
| Snippet | Methods of detecting apnea based on acoustic features can be prone to misdiagnosed and missed diagnoses due to the influence of noise. The aim of this paper is... Objective. Methods of detecting apnea based on acoustic features can be prone to misdiagnosed and missed diagnoses due to the influence of noise. The aim of... |
| SourceID | proquest pubmed |
| SourceType | Aggregation Database Index Database |
| Title | Cascaded redundant convolutional encoder-decoder network improved apnea detection performance using tracheal sounds in post anesthesia care unit patients |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/39437805 https://www.proquest.com/docview/3119723100 |
| Volume | 10 |
| WOSCitedRecordID | wos001346602800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LT4QwEG7U9eDFR3ytr9TEa7OFQgsnY4wbD7rZg5q9kb4wXAAp63_x3zoF1j2ZmHiBhLQNGabD1-nX-RC6kVSncaoCYhRlJLKWERXCwlVQlUuIhWnYFat-exKzWbJYpPMh4eYGWuUqJnaB2lTa58gnrBfICii9rT-IV43yu6uDhMYmGjGAMp7SJRbJOscCaJ13wishwBICA6x2KmFuTX6eTaRJUs1_x5jdv2a699-33Ee7A8rEd71bHKANWx6ir3vpPBve4Mb6s2NgU-xJ54PzQXtf1NLYhhjb3XHZc8Rx0WUeoKOsSyuxsW1H4CpxvT52gD2D_h23ja8QDWM5r9fkcAGNKtdiCSEVsKYrJPZkM7yEUIKHoq7uCL1OH17uH8mgzEA0LNBaoiyzVnAZsTAwWinLOdU8z2VoNDU24oGhaRJzQXWuVWIoGNjKWMXQ2MSahsdoq6xKe4qwyRmVsMgTOhGRSlIVMgD8MgKHYZFQdIyuV5bOwPP9dga8cbV02drWY3TSf66s7kt0ZCyNmFdrOPtD73O0EwJS6Q8YXqBRDvPeXqJt_dkWrrnqXAqus_nzN83S2t8 |
| linkProvider | ProQuest |
| 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=Cascaded+redundant+convolutional+encoder-decoder+network+improved+apnea+detection+performance+using+tracheal+sounds+in+post+anesthesia+care+unit+patients&rft.jtitle=Biomedical+physics+%26+engineering+express&rft.au=Zhang%2C+Erpeng&rft.au=Jia%2C+Xiuzhu&rft.au=Wu%2C+Yanan&rft.au=Liu%2C+Jing&rft.date=2024-11-01&rft.eissn=2057-1976&rft_id=info:doi/10.1088%2F2057-1976%2Fad89c6&rft_id=info%3Apmid%2F39437805&rft_id=info%3Apmid%2F39437805&rft.externalDocID=39437805 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2057-1976&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2057-1976&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2057-1976&client=summon |