A Low-Complexity ECG Feature Extraction Algorithm for Mobile Healthcare Applications
This paper introduces a low-complexity algorithm for the extraction of the fiducial points from the electrocardiogram (ECG). The application area we consider is that of remote cardiovascular monitoring, where continuous sensing and processing takes place in low-power, computationally constrained dev...
Gespeichert in:
| Veröffentlicht in: | IEEE journal of biomedical and health informatics Jg. 17; H. 2; S. 459 - 469 |
|---|---|
| Hauptverfasser: | , , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
United States
IEEE
01.03.2013
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Schlagworte: | |
| ISSN: | 2168-2194, 2168-2208, 2168-2208 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | This paper introduces a low-complexity algorithm for the extraction of the fiducial points from the electrocardiogram (ECG). The application area we consider is that of remote cardiovascular monitoring, where continuous sensing and processing takes place in low-power, computationally constrained devices, thus the power consumption and complexity of the processing algorithms should remain at a minimum level. Under this context, we choose to employ the discrete wavelet transform (DWT) with the Haar function being the mother wavelet, as our principal analysis method. From the modulus-maxima analysis on the DWT coefficients, an approximation of the ECG fiducial points is extracted. These initial findings are complimented with a refinement stage, based on the time-domain morphological properties of the ECG, which alleviates the decreased temporal resolution of the DWT. The resulting algorithm is a hybrid scheme of time- and frequency-domain signal processing. Feature extraction results from 27 ECG signals from QTDB were tested against manual annotations and used to compare our approach against the state-of-the art ECG delineators. In addition, 450 signals from the 15-lead PTBDB are used to evaluate the obtained performance against the CSE tolerance limits. Our findings indicate that all but one CSE limits are satisfied. This level of performance combined with a complexity analysis, where the upper bound of the proposed algorithm, in terms of arithmetic operations, is calculated as 2.423 N +214 additions and 1.093 N +12 multiplications for N ≤ 861 or 2.553 N +102 additions and 1.093 N +10 multiplications for N > 861 ( N being the number of input samples), reveals that the proposed method achieves an ideal tradeoff between computational complexity and performance, a key requirement in remote cardiovascular disease monitoring systems. |
|---|---|
| AbstractList | This paper introduces a low-complexity algorithm for the extraction of the fiducial points from the electrocardiogram (ECG). The application area we consider is that of remote cardiovascular monitoring, where continuous sensing and processing takes place in low-power, computationally constrained devices, thus the power consumption and complexity of the processing algorithms should remain at a minimum level. Under this context, we choose to employ the discrete wavelet transform (DWT) with the Haar function being the mother wavelet, as our principal analysis method. From the modulus-maxima analysis on the DWT coefficients, an approximation of the ECG fiducial points is extracted. These initial findings are complimented with a refinement stage, based on the time-domain morphological properties of the ECG, which alleviates the decreased temporal resolution of the DWT. The resulting algorithm is a hybrid scheme of time- and frequency-domain signal processing. Feature extraction results from 27 ECG signals from QTDB were tested against manual annotations and used to compare our approach against the state-of-the art ECG delineators. In addition, 450 signals from the 15-lead PTBDB are used to evaluate the obtained performance against the CSE tolerance limits. Our findings indicate that all but one CSE limits are satisfied. This level of performance combined with a complexity analysis, where the upper bound of the proposed algorithm, in terms of arithmetic operations, is calculated as hbox 2.423 N + hbox 214 additions and hbox 1.093 N + hbox 12 multiplications for N less than or equal to hbox 861 or hbox 2.553 N + hbox 102 additions and hbox 1.093 N + hbox 10 multiplications for syntax error at token & ( N being the number of input samples), reveals that the proposed method achieves an ideal tradeoff between computational complexity and performance, a key requirement in remote cardiovascular disease monitoring systems. This paper introduces a low-complexity algorithm for the extraction of the fiducial points from the Electrocardiogram (ECG). The application area we consider is that of remote cardiovascular monitoring, where continuous sensing and processing takes place in low-power, computationally constrained devices, thus the power consumption and complexity of the processing algorithms should remain at a minimum level. Under this context, we choose to employ the Discrete Wavelet Transform (DWT) with the Haar function being the mother wavelet, as our principal analysis method. From the modulus-maxima analysis on the DWT coefficients, an approximation of the ECG fiducial points is extracted. These initial findings are complimented with a refinement stage, based on the time-domain morphological properties of the ECG, which alleviates the decreased temporal resolution of the DWT. The resulting algorithm is a hybrid scheme of time and frequency domain signal processing. Feature extraction results from 27 ECG signals from QTDB, were tested against manual annotations and used to compare our approach against the state-of-the art ECG delineators. In addition, 450 signals from the 15-lead PTBDB are used to evaluate the obtained performance against the CSE tolerance limits. Our findings indicate that all but one CSE limits are satisfied. This level of performance combined with a complexity analysis, where the upper bound of the proposed algorithm, in terms of arithmetic operations, is calculated as 2:423N + 214 additions and 1:093N + 12 multiplications for N 861 or 2:553N + 102 additions and 1:093N +10 multiplications for N > 861 (N being the number of input samples), reveals that the proposed method achieves an ideal trade-off between computational complexity and performance, a key requirement in remote CVD monitoring systems.This paper introduces a low-complexity algorithm for the extraction of the fiducial points from the Electrocardiogram (ECG). The application area we consider is that of remote cardiovascular monitoring, where continuous sensing and processing takes place in low-power, computationally constrained devices, thus the power consumption and complexity of the processing algorithms should remain at a minimum level. Under this context, we choose to employ the Discrete Wavelet Transform (DWT) with the Haar function being the mother wavelet, as our principal analysis method. From the modulus-maxima analysis on the DWT coefficients, an approximation of the ECG fiducial points is extracted. These initial findings are complimented with a refinement stage, based on the time-domain morphological properties of the ECG, which alleviates the decreased temporal resolution of the DWT. The resulting algorithm is a hybrid scheme of time and frequency domain signal processing. Feature extraction results from 27 ECG signals from QTDB, were tested against manual annotations and used to compare our approach against the state-of-the art ECG delineators. In addition, 450 signals from the 15-lead PTBDB are used to evaluate the obtained performance against the CSE tolerance limits. Our findings indicate that all but one CSE limits are satisfied. This level of performance combined with a complexity analysis, where the upper bound of the proposed algorithm, in terms of arithmetic operations, is calculated as 2:423N + 214 additions and 1:093N + 12 multiplications for N 861 or 2:553N + 102 additions and 1:093N +10 multiplications for N > 861 (N being the number of input samples), reveals that the proposed method achieves an ideal trade-off between computational complexity and performance, a key requirement in remote CVD monitoring systems. This paper introduces a low-complexity algorithm for the extraction of the fiducial points from the Electrocardiogram (ECG). The application area we consider is that of remote cardiovascular monitoring, where continuous sensing and processing takes place in low-power, computationally constrained devices, thus the power consumption and complexity of the processing algorithms should remain at a minimum level. Under this context, we choose to employ the Discrete Wavelet Transform (DWT) with the Haar function being the mother wavelet, as our principal analysis method. From the modulus-maxima analysis on the DWT coefficients, an approximation of the ECG fiducial points is extracted. These initial findings are complimented with a refinement stage, based on the time-domain morphological properties of the ECG, which alleviates the decreased temporal resolution of the DWT. The resulting algorithm is a hybrid scheme of time and frequency domain signal processing. Feature extraction results from 27 ECG signals from QTDB, were tested against manual annotations and used to compare our approach against the state-of-the art ECG delineators. In addition, 450 signals from the 15-lead PTBDB are used to evaluate the obtained performance against the CSE tolerance limits. Our findings indicate that all but one CSE limits are satisfied. This level of performance combined with a complexity analysis, where the upper bound of the proposed algorithm, in terms of arithmetic operations, is calculated as 2:423N + 214 additions and 1:093N + 12 multiplications for N 861 or 2:553N + 102 additions and 1:093N +10 multiplications for N > 861 (N being the number of input samples), reveals that the proposed method achieves an ideal trade-off between computational complexity and performance, a key requirement in remote CVD monitoring systems. This paper introduces a low-complexity algorithm for the extraction of the fiducial points from the electrocardiogram (ECG). The application area we consider is that of remote cardiovascular monitoring, where continuous sensing and processing takes place in low-power, computationally constrained devices, thus the power consumption and complexity of the processing algorithms should remain at a minimum level. Under this context, we choose to employ the discrete wavelet transform (DWT) with the Haar function being the mother wavelet, as our principal analysis method. From the modulus-maxima analysis on the DWT coefficients, an approximation of the ECG fiducial points is extracted. These initial findings are complimented with a refinement stage, based on the time-domain morphological properties of the ECG, which alleviates the decreased temporal resolution of the DWT. The resulting algorithm is a hybrid scheme of time- and frequency-domain signal processing. Feature extraction results from 27 ECG signals from QTDB were tested against manual annotations and used to compare our approach against the state-of-the art ECG delineators. In addition, 450 signals from the 15-lead PTBDB are used to evaluate the obtained performance against the CSE tolerance limits. Our findings indicate that all but one CSE limits are satisfied. This level of performance combined with a complexity analysis, where the upper bound of the proposed algorithm, in terms of arithmetic operations, is calculated as 2.423 N +214 additions and 1.093 N +12 multiplications for N ≤ 861 or 2.553 N +102 additions and 1.093 N +10 multiplications for N > 861 ( N being the number of input samples), reveals that the proposed method achieves an ideal tradeoff between computational complexity and performance, a key requirement in remote cardiovascular disease monitoring systems. This paper introduces a low-complexity algorithm for the extraction of the fiducial points from the electrocardiogram (ECG). The application area we consider is that of remote cardiovascular monitoring, where continuous sensing and processing takes place in low-power, computationally constrained devices, thus the power consumption and complexity of the processing algorithms should remain at a minimum level. Under this context, we choose to employ the discrete wavelet transform (DWT) with the Haar function being the mother wavelet, as our principal analysis method. From the modulus-maxima analysis on the DWT coefficients, an approximation of the ECG fiducial points is extracted. These initial findings are complimented with a refinement stage, based on the time-domain morphological properties of the ECG, which alleviates the decreased temporal resolution of the DWT. The resulting algorithm is a hybrid scheme of time- and frequency-domain signal processing. Feature extraction results from 27 ECG signals from QTDB were tested against manual annotations and used to compare our approach against the state-of-the art ECG delineators. In addition, 450 signals from the 15-lead PTBDB are used to evaluate the obtained performance against the CSE tolerance limits. Our findings indicate that all but one CSE limits are satisfied. This level of performance combined with a complexity analysis, where the upper bound of the proposed algorithm, in terms of arithmetic operations, is calculated as [Formula Omitted] additions and [Formula Omitted] multiplications for [Formula Omitted] or [Formula Omitted] additions and [Formula Omitted] multiplications for [Formula Omitted] ( [Formula Omitted] being the number of input samples), reveals that the proposed method achieves an ideal tradeoff between computational complexity and performance, a key requirement in remote cardiovascular disease monitoring systems. |
| Author | Maharatna, K. Rosengarten, J. Acharyya, A. Biswas, D. Taihai Chen Mazomenos, E. B. Curzen, N. Morgan, J. |
| Author_xml | – sequence: 1 givenname: E. B. surname: Mazomenos fullname: Mazomenos, E. B. email: ebm@ecs.soton.ac.uk organization: Sch. of Electron. & Comput. Sci., Univ. of Southampton, Southampton, UK – sequence: 2 givenname: D. surname: Biswas fullname: Biswas, D. email: db9g10@ecs.soton.ac.uk organization: Sch. of Electron. & Comput. Sci., Univ. of Southampton, Southampton, UK – sequence: 3 givenname: A. surname: Acharyya fullname: Acharyya, A. email: amit_acharyya@iith.ac.in organization: Dept. of Electr. Eng., Indian Inst. of Technol., Hyderabad, Hyderabad, India – sequence: 4 surname: Taihai Chen fullname: Taihai Chen email: tc10g09@ecs.soton.ac.uk organization: Sch. of Electron. & Comput. Sci., Univ. of Southampton, Southampton, UK – sequence: 5 givenname: K. surname: Maharatna fullname: Maharatna, K. email: km3@ecs.soton.ac.uk organization: Sch. of Electron. & Comput. Sci., Univ. of Southampton, Southampton, UK – sequence: 6 givenname: J. surname: Rosengarten fullname: Rosengarten, J. email: james@rosengarten.co.uk organization: Southampton Univ. Hosp. NHS Trust, Southampton, UK – sequence: 7 givenname: J. surname: Morgan fullname: Morgan, J. email: jmm@hrclinic.org organization: Southampton Univ. Hosp. NHS Trust, Southampton, UK – sequence: 8 givenname: N. surname: Curzen fullname: Curzen, N. email: nick.curzen@suht.swest.nhs.uk organization: Southampton Univ. Hosp. NHS Trust, Southampton, UK |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/23362250$$D View this record in MEDLINE/PubMed |
| BookMark | eNqF0stu3CAUBmBUpWouzQNUlSpL3WTjKWCuy-locpEmyma6RgTjhggbF7CSvH1wZqaLLFo2IPSdI3F-TsHREAYLwBcEFwhB-WN7s_25wBDhBcYNahD-AE4wYqLGGIqjwxlJcgzOU3qEZYlyJdkncIybhmFM4QnYLqtNeKpXoR-9fXb5pVqvrqpLq_MUbbV-zlGb7MJQLf3vEF1-6KsuxOo23Dtvq2urfX4wutDlOHpn9GzTZ_Cx0z7Z8_1-Bn5drrer63pzd3WzWm5qQ4jINcesNajlTHIBjWhh1yFqEccdg7KjDZaUyk5qXEiLDLcSt0JQw4iVLaKyOQMXu75jDH8mm7LqXTLWez3YMCWFCJWSM8Hg_2lDBBGc05l-f0cfwxSH8pCiMCGEIj6rb3s13fe2VWN0vY4v6jDaAvgOmBhSirZTxuW3-ZSZOq8QVHOOas5RzTmqfY6lEr2rPDT_V83XXY2z1v71jJS_QHHzCsJIpKM |
| CODEN | IJBHA9 |
| CitedBy_id | crossref_primary_10_1109_ACCESS_2016_2629486 crossref_primary_10_3390_ijerph182010792 crossref_primary_10_1016_j_cmpb_2015_06_003 crossref_primary_10_1016_j_micpro_2020_103771 crossref_primary_10_1109_JBHI_2017_2773097 crossref_primary_10_1109_TCSII_2017_2658670 crossref_primary_10_1007_s13246_018_0642_y crossref_primary_10_1016_j_bspc_2024_106211 crossref_primary_10_1186_s12938_018_0487_3 crossref_primary_10_1016_j_techfore_2021_121127 crossref_primary_10_3103_S0146411619060051 crossref_primary_10_1007_s11063_022_10949_9 crossref_primary_10_1038_s41598_019_51061_8 crossref_primary_10_1016_j_cmpb_2024_108249 crossref_primary_10_1007_s10916_018_1006_6 crossref_primary_10_3390_bioengineering4020032 crossref_primary_10_1016_j_bspc_2020_102291 crossref_primary_10_1109_TCSI_2019_2961643 crossref_primary_10_1049_iet_spr_2017_0296 crossref_primary_10_3390_brainsci10020118 crossref_primary_10_1109_JSEN_2017_2786301 crossref_primary_10_1007_s13748_021_00250_6 crossref_primary_10_3390_diagnostics8010010 crossref_primary_10_1109_JSEN_2014_2363571 crossref_primary_10_1049_htl_2015_0062 crossref_primary_10_1007_s11277_017_4884_x crossref_primary_10_1016_j_cmpb_2018_01_018 crossref_primary_10_1109_TBCAS_2017_2731797 crossref_primary_10_1007_s10586_018_2273_1 crossref_primary_10_1007_s10916_018_0983_9 crossref_primary_10_1088_1674_4926_37_5_055006 crossref_primary_10_3390_app14010342 crossref_primary_10_1007_s10462_020_09928_0 crossref_primary_10_1109_TCSI_2021_3057584 crossref_primary_10_1016_j_bspc_2014_10_012 crossref_primary_10_1016_j_bspc_2020_102260 crossref_primary_10_3233_IDA_160821 crossref_primary_10_1109_JSEN_2015_2413836 crossref_primary_10_1109_TBCAS_2017_2760298 crossref_primary_10_1007_s11432_014_5199_0 crossref_primary_10_1016_j_compbiomed_2016_09_004 crossref_primary_10_1007_s10916_019_1362_x crossref_primary_10_1049_el_2016_4149 crossref_primary_10_1109_TCSI_2018_2867746 crossref_primary_10_3390_app11135908 crossref_primary_10_1109_JBHI_2017_2671443 crossref_primary_10_1088_0967_3334_37_2_203 crossref_primary_10_1109_TBME_2015_2422751 crossref_primary_10_1016_j_vlsi_2017_10_006 crossref_primary_10_1016_j_bspc_2016_12_017 crossref_primary_10_1109_JBHI_2015_2466440 crossref_primary_10_1016_j_bspc_2018_03_003 crossref_primary_10_3390_s17102435 crossref_primary_10_1109_TC_2016_2622262 crossref_primary_10_1016_j_vlsi_2021_09_006 crossref_primary_10_1109_TAFFC_2018_2851236 crossref_primary_10_1155_2017_5980541 crossref_primary_10_1587_transinf_2017EDP7285 crossref_primary_10_1038_s41598_024_59851_5 crossref_primary_10_1371_journal_pone_0318070 crossref_primary_10_1109_ACCESS_2017_2754283 crossref_primary_10_1016_j_bspc_2020_101946 crossref_primary_10_1109_JBHI_2020_2973982 crossref_primary_10_4018_IJEHMC_2018070105 crossref_primary_10_1016_j_bspc_2018_05_005 crossref_primary_10_1109_TBCAS_2018_2833624 crossref_primary_10_1371_journal_pone_0190323 crossref_primary_10_1371_journal_pone_0313815 crossref_primary_10_1016_j_compbiomed_2021_104804 crossref_primary_10_1109_ACCESS_2021_3119630 crossref_primary_10_1016_j_compbiomed_2015_09_005 crossref_primary_10_1109_TBCAS_2019_2892297 crossref_primary_10_1007_s10916_017_0883_4 crossref_primary_10_1016_j_irbm_2019_04_003 crossref_primary_10_1016_j_bspc_2018_08_023 crossref_primary_10_1109_JTEHM_2020_3000327 crossref_primary_10_26634_jes_4_2_4834 crossref_primary_10_1007_s10462_021_09999_7 crossref_primary_10_1016_j_cmpb_2020_105607 crossref_primary_10_3390_s20216318 crossref_primary_10_1007_s13369_015_1658_1 crossref_primary_10_1109_ACCESS_2018_2848201 |
| Cites_doi | 10.1109/IEMBS.2000.897966 10.1109/TBME.2006.877103 10.1007/BF02442095 10.1109/CSSE.2008.1096 10.1109/TITB.2011.2163943 10.1109/IEMBS.2005.1615314 10.1109/10.362922 10.1007/BF02442378 10.1016/0010-4809(87)90024-3 10.1109/TBME.2003.821031 10.1109/CIC.2000.898460 10.1109/JSEN.2010.2045498 10.1109/IEMBS.1995.579466 10.1109/IEMBS.2003.1280402 10.1109/TBME.1986.325695 10.1007/978-3-540-89208-3_7 10.1109/TBME.1985.325532 10.1109/10.704877 10.1109/IEMBS.1999.802343 10.1109/CESA.2006.4281639 10.1109/IEMBS.1996.652646 10.1016/S0169-2607(97)01780-X 10.1016/j.jelectrocard.2005.04.003 10.1016/0165-1684(88)90069-2 10.1109/51.566158 10.1006/cbmr.1994.1006 10.1109/CIC.1997.648140 10.1109/ACSSC.1992.269273 10.1109/IEMBS.1996.647474 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Mar 2013 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Mar 2013 |
| DBID | 97E RIA RIE AAYXX CITATION CGR CUY CVF ECM EIF NPM 7QF 7QO 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7U5 8BQ 8FD F28 FR3 H8D JG9 JQ2 K9. KR7 L7M L~C L~D NAPCQ P64 7X8 |
| DOI | 10.1109/TITB.2012.2231312 |
| DatabaseName | IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Aluminium Industry Abstracts Biotechnology Research Abstracts Ceramic Abstracts Computer and Information Systems Abstracts Corrosion Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts Materials Business File Mechanical & Transportation Engineering Abstracts Solid State and Superconductivity Abstracts METADEX Technology Research Database ANTE: Abstracts in New Technology & Engineering Engineering Research Database Aerospace Database Materials Research Database ProQuest Computer Science Collection ProQuest Health & Medical Complete (Alumni) Civil Engineering Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Nursing & Allied Health Premium Biotechnology and BioEngineering Abstracts MEDLINE - Academic |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Materials Research Database Civil Engineering Abstracts Aluminium Industry Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts ProQuest Health & Medical Complete (Alumni) Ceramic Abstracts Materials Business File METADEX Biotechnology and BioEngineering Abstracts Computer and Information Systems Abstracts Professional Aerospace Database Nursing & Allied Health Premium Engineered Materials Abstracts Biotechnology Research Abstracts Solid State and Superconductivity Abstracts Engineering Research Database Corrosion Abstracts Advanced Technologies Database with Aerospace ANTE: Abstracts in New Technology & Engineering MEDLINE - Academic |
| DatabaseTitleList | Engineering Research Database MEDLINE - Academic MEDLINE Materials Research Database |
| 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: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 2168-2208 |
| EndPage | 469 |
| ExternalDocumentID | 2938744981 23362250 10_1109_TITB_2012_2231312 6420852 |
| Genre | orig-research Research Support, Non-U.S. Gov't Journal Article |
| GroupedDBID | 0R~ 4.4 6IF 6IH 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACIWK ACPRK AENEX AFRAH AGQYO AGSQL AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ EBS EJD HZ~ IFIPE IPLJI JAVBF M43 O9- OCL PQQKQ RIA RIE RNS AAYXX CITATION CGR CUY CVF ECM EIF NPM RIG 7QF 7QO 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7U5 8BQ 8FD F28 FR3 H8D JG9 JQ2 K9. KR7 L7M L~C L~D NAPCQ P64 7X8 |
| ID | FETCH-LOGICAL-c448t-726dc1d769780c8d0ff15e172f609f5329559f9a2d76d1c7e92d885c64e9d1593 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 134 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000321144400022&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2168-2194 2168-2208 |
| IngestDate | Thu Oct 02 05:32:35 EDT 2025 Tue Oct 07 09:43:15 EDT 2025 Sun Nov 09 06:52:15 EST 2025 Thu Apr 03 07:03:47 EDT 2025 Sat Nov 29 04:18:07 EST 2025 Tue Nov 18 22:30:38 EST 2025 Tue Aug 26 16:42:50 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2 |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c448t-726dc1d769780c8d0ff15e172f609f5329559f9a2d76d1c7e92d885c64e9d1593 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| PMID | 23362250 |
| PQID | 1324445170 |
| PQPubID | 85417 |
| PageCount | 11 |
| ParticipantIDs | ieee_primary_6420852 proquest_journals_1324445170 proquest_miscellaneous_1348487750 pubmed_primary_23362250 proquest_miscellaneous_1459976860 crossref_citationtrail_10_1109_TITB_2012_2231312 crossref_primary_10_1109_TITB_2012_2231312 |
| PublicationCentury | 2000 |
| PublicationDate | 2013-03-01 |
| PublicationDateYYYYMMDD | 2013-03-01 |
| PublicationDate_xml | – month: 03 year: 2013 text: 2013-03-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: Piscataway |
| PublicationTitle | IEEE journal of biomedical and health informatics |
| PublicationTitleAbbrev | JBHI |
| PublicationTitleAlternate | IEEE J Biomed Health Inform |
| PublicationYear | 2013 |
| Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | ref35 ref13 ref34 ref37 ref15 ref36 ref30 yang (ref18) 1997; 1 ref33 ref11 ref10 de lannoy (ref23) 2008; 22 (ref12) 0 ref17 ref38 ref16 ref19 (ref1) 0 (ref3) 0 (ref22) 0 ref24 (ref5) 1985; 6 ref25 ref20 ref21 velchev (ref32) 2008; 1 virgilio (ref26) 1995; 2 (ref6) 0 ref28 ref27 ref29 ref8 ref7 (ref2) 0 ref9 ref4 dokur (ref14) 1996; 3 josko (ref31) 2007 de chazal (ref39) 1996; 4 |
| References_xml | – ident: ref20 doi: 10.1109/IEMBS.2000.897966 – ident: ref16 doi: 10.1109/TBME.2006.877103 – ident: ref8 doi: 10.1007/BF02442095 – year: 0 ident: ref22 – year: 0 ident: ref12 – ident: ref17 doi: 10.1109/CSSE.2008.1096 – ident: ref4 doi: 10.1109/TITB.2011.2163943 – ident: ref29 doi: 10.1109/IEMBS.2005.1615314 – ident: ref24 doi: 10.1109/10.362922 – ident: ref7 doi: 10.1007/BF02442378 – year: 0 ident: ref2 – ident: ref11 doi: 10.1016/0010-4809(87)90024-3 – year: 0 ident: ref3 – year: 0 ident: ref1 – volume: 6 start-page: 815 year: 1985 ident: ref5 article-title: The CSE working party, "Recommendations for measurement standards in quantitative electrocardiography publication-title: Eur Heart J – ident: ref34 doi: 10.1109/TBME.2003.821031 – ident: ref33 doi: 10.1109/CIC.2000.898460 – ident: ref35 doi: 10.1109/JSEN.2010.2045498 – year: 0 ident: ref6 – volume: 1 start-page: 289 year: 1997 ident: ref18 article-title: ECG events detection and classification using wavelet and neural networks publication-title: Proc IEEE Eng Med Biol Soc – volume: 2 start-page: 1051 year: 1995 ident: ref26 article-title: ECG fiducial points detection through wavelet transform publication-title: Proc IEEE Eng Med Biol Soc doi: 10.1109/IEMBS.1995.579466 – ident: ref21 doi: 10.1109/IEMBS.2003.1280402 – ident: ref10 doi: 10.1109/TBME.1986.325695 – volume: 22 start-page: 22 year: 2008 ident: ref23 article-title: Supervised ECG delineation using the wavelet transform and hidden Markov models publication-title: Proc Conf Int Federation Med Biol Eng doi: 10.1007/978-3-540-89208-3_7 – ident: ref9 doi: 10.1109/TBME.1985.325532 – volume: 1 start-page: 22 year: 2008 ident: ref32 article-title: Wavelet transform based ECG characteristic points detector publication-title: Proc Int Sci Conf Comp Sci – ident: ref15 doi: 10.1109/10.704877 – ident: ref19 doi: 10.1109/IEMBS.1999.802343 – start-page: 1 year: 2007 ident: ref31 article-title: Discrete wavelet transform in automatic ECG signal analysis publication-title: Proc IEEE Instrum Meas Technol Conf – ident: ref30 doi: 10.1109/CESA.2006.4281639 – volume: 3 start-page: 929 year: 1996 ident: ref14 article-title: Detection of ECG waveforms by using artificial neural networks publication-title: Proc IEEE Eng Med Biol Soc doi: 10.1109/IEMBS.1996.652646 – ident: ref28 doi: 10.1016/S0169-2607(97)01780-X – ident: ref37 doi: 10.1016/j.jelectrocard.2005.04.003 – ident: ref13 doi: 10.1016/0165-1684(88)90069-2 – ident: ref27 doi: 10.1109/51.566158 – ident: ref38 doi: 10.1006/cbmr.1994.1006 – ident: ref36 doi: 10.1109/CIC.1997.648140 – ident: ref25 doi: 10.1109/ACSSC.1992.269273 – volume: 4 start-page: 1399 year: 1996 ident: ref39 article-title: Automatic measurement of the QRS onset and offset in individual ECG leads publication-title: Proc IEEE Eng Med Biol Soc doi: 10.1109/IEMBS.1996.647474 |
| SSID | ssj0000816896 |
| Score | 2.449182 |
| Snippet | This paper introduces a low-complexity algorithm for the extraction of the fiducial points from the electrocardiogram (ECG). The application area we consider... This paper introduces a low-complexity algorithm for the extraction of the fiducial points from the Electrocardiogram (ECG). The application area we consider... |
| SourceID | proquest pubmed crossref ieee |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 459 |
| SubjectTerms | Algorithm design and analysis Algorithms Databases, Factual Discrete wavelet transform (DWT) Discrete wavelet transforms electrocardiogram (ECG) feature extraction Electrocardiography Electrocardiography - methods Feature extraction Humans low complexity algorithm mobile healthcare Monitoring Monitoring systems Noise Signal processing Signal processing algorithms Studies Wavelet Analysis |
| Title | A Low-Complexity ECG Feature Extraction Algorithm for Mobile Healthcare Applications |
| URI | https://ieeexplore.ieee.org/document/6420852 https://www.ncbi.nlm.nih.gov/pubmed/23362250 https://www.proquest.com/docview/1324445170 https://www.proquest.com/docview/1348487750 https://www.proquest.com/docview/1459976860 |
| Volume | 17 |
| WOSCitedRecordID | wos000321144400022&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: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 2168-2208 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000816896 issn: 2168-2194 databaseCode: RIE dateStart: 20130101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT9wwEB4BqhAX2vIo21JkpJ4qDLGT2PFxi5ZSqaAettLeosR2YCW6QSFL23_fGScbcWiRerOUiWJ5Hv4m8wL4YFFs0MkpuYhtzBMlDc9kYXkZFanSsRO-LMKwCX19nc1m5tsanAy1MN77kHzmT2kZYvmutkv6VXamKBacosFd11p1tVrD_5QwQCKM45K44KiISR_EFJE5m36ZfqI8LnmK16GIBQ2xkTEab0kF909upDBi5d9oM9w6Fy__b7-vYLtHl2zcicNrWPOLHdi86uPnuzAds6_1T05GgBphtr_Z5PwzIxi4bDyb_Gqbrs6Bje9u6mbe3v5gCGrZVV2i8WCXQ64YGz-Je-_B94vJ9PyS93MVuEVnrOVaKmeF04q6D9nMRVUlUo9IplKRqdJYUle6yhQSSZyw2hvpsiy1KvHGIfyJ92FjUS_8ATCNAAYJpDNphSbXG6pzRbVOhXa-dMUIotXZ5rZvOk6zL-7y4HxEJifO5MSZvOfMCD4Or9x3HTeeI96lYx8I-xMfweGKgXmvkw85imRC7dh0NILj4TFqE4VIioWvl0STZOjCIYx6hiZJDYK4TCHNm044hu-vZOrt3_f1DrZkGKdBOWyHsNE2S_8eXtjHdv7QHKFYz7KjINZ_AEGn7bo |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwEB5VpYJeSqHQbilgJE4It7ETJ_FxqbZsxe6KQ5B6ixLbgUplU6XZAv--M0426gEqcbOUiWJ5Hv4m8wJ4b1Bs0MkpuQhNyKNYap7KwvAyKFSchFa4svDDJpLFIr240F834ONQC-Oc88ln7piWPpZva7OiX2UnMcWCFRrcRyrCZVetNfxR8SMk_EAuiQuOqhj1YUwR6JPsPPtEmVzyGC9EEQoaYyNDNN-SSu7v3Ul-yMq_8aa_d86e_t-Od2Gnx5ds3AnEM9hwy-fweN5H0PcgG7NZ_YuTGaBWmO0fNjn9zAgIrhrHJr_bpqt0YOOr73Vz2f74yRDWsnldovlg0yFbjI3vRb5fwLezSXY65f1kBW7QHWt5ImNrhE1i6j9kUhtUlVAOsUwVB7pSoaS-dJUuJJJYYRKnpU1TZeLIaYsAKHwJm8t66Q6AJQhhkEBarSo0uk5TpSsqthKJdaUtRhCszzY3fdtxmn5xlXv3I9A5cSYnzuQ9Z0bwYXjluuu58RDxHh37QNif-AiO1gzMe628yVEoI2rIlgQjeDc8Rn2iIEmxdPWKaKIUnTgEUg_QREojjEtjpNnvhGP4_lqmDv--r7fwZJrNZ_nsfPHlFWxLP1yDMtqOYLNtVu41bJnb9vKmeeOF-w4s-PAZ |
| 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=A+Low-Complexity+ECG+Feature+Extraction+Algorithm+for+Mobile+Healthcare+Applications&rft.jtitle=IEEE+journal+of+biomedical+and+health+informatics&rft.au=Mazomenos%2C+E.+B.&rft.au=Biswas%2C+D.&rft.au=Acharyya%2C+A.&rft.au=Taihai+Chen&rft.date=2013-03-01&rft.issn=2168-2194&rft.eissn=2168-2208&rft.volume=17&rft.issue=2&rft.spage=459&rft.epage=469&rft_id=info:doi/10.1109%2FTITB.2012.2231312&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_TITB_2012_2231312 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2168-2194&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2168-2194&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2168-2194&client=summon |