Optimal data fusion for the improvement of QRS complex detection in multi-channel ECG recordings
Abstract The automatic analysis of the electrocardiogram (ECG) begins, traditionally, with the detection of QRS complexes. Afterwards, useful information can be extracted from it, ranging from the estimation of the instantaneous heart rate to nonlinear heart rate variability analysis. A plethora of...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ledezma, Carlos A. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Anmerkung: |
© International Federation for Medical and Biological Engineering 2019 |
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| Übergeordnetes Werk: |
Enthalten in: Medical & biological engineering & computing - Cham : Springer Nature, 1963, 57(2019), 8 vom: 17. Mai, Seite 1673-1681 |
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| Übergeordnetes Werk: |
volume:57 ; year:2019 ; number:8 ; day:17 ; month:05 ; pages:1673-1681 |
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| DOI / URN: |
10.1007/s11517-019-01990-3 |
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SPR020522487 |
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| 520 | |a Abstract The automatic analysis of the electrocardiogram (ECG) begins, traditionally, with the detection of QRS complexes. Afterwards, useful information can be extracted from it, ranging from the estimation of the instantaneous heart rate to nonlinear heart rate variability analysis. A plethora of works have been published on this topic; consequently, there exist many QRS complex detectors with high-performance values. However, just a few detectors have been conceived that profit from the information contained in several ECG leads to provide a robust QRS complex detection. In this work, we explore the fusion of multi-channel ECG recordings QRS detections as a means to improve the detection performance. This paper presents a decentralized multi-channel QRS complex fusion scheme that optimally combines single-channel detections to produce a single detection signal. Using six different widely used QRS complex detectors on the MIT-BIH Arrhythmia and INCART databases, a reduction in false and missed detections was achieved with the proposed approach compared with the single-channel counterpart. Furthermore, our detection results are comparable with the performance of other multi-channel detectors found in the literature, showing, in turn, various advantages in scalability, adaptability, and simplicity in the system’s implementation Graphical AbstractN QRS complex detectors simultaneously monitor N ECG channels. Once a detection occurs in a given channel, a 150 ms long window is opened to look for detections in other channels. Within this window, yn = + 1 if a QRS complex is detected and yn = − 1 otherwise. A coefficient α n, obtained during a training period and related to the detection performance in channel n, multiplies the detection signal yn, so that greater weights are assigned to ECG channels where single-channel detectors performed better. Finally, the binary detection decision (f ) is obtained from the comparison of the weighted sum of single-channel detections (z) with a fixed threshold (β). | ||
| 650 | 4 | |a QRS complex detection |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Electrocardiography |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Digital filters |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Optimal data fusion |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Detection algorithms |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Altuve, Miguel |0 (orcid)0000-0002-4064-2601 |4 aut | |
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10.1007/s11517-019-01990-3 doi (DE-627)SPR020522487 (SPR)s11517-019-01990-3-e DE-627 ger DE-627 rakwb eng Ledezma, Carlos A. verfasserin aut Optimal data fusion for the improvement of QRS complex detection in multi-channel ECG recordings 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Federation for Medical and Biological Engineering 2019 Abstract The automatic analysis of the electrocardiogram (ECG) begins, traditionally, with the detection of QRS complexes. Afterwards, useful information can be extracted from it, ranging from the estimation of the instantaneous heart rate to nonlinear heart rate variability analysis. A plethora of works have been published on this topic; consequently, there exist many QRS complex detectors with high-performance values. However, just a few detectors have been conceived that profit from the information contained in several ECG leads to provide a robust QRS complex detection. In this work, we explore the fusion of multi-channel ECG recordings QRS detections as a means to improve the detection performance. This paper presents a decentralized multi-channel QRS complex fusion scheme that optimally combines single-channel detections to produce a single detection signal. Using six different widely used QRS complex detectors on the MIT-BIH Arrhythmia and INCART databases, a reduction in false and missed detections was achieved with the proposed approach compared with the single-channel counterpart. Furthermore, our detection results are comparable with the performance of other multi-channel detectors found in the literature, showing, in turn, various advantages in scalability, adaptability, and simplicity in the system’s implementation Graphical AbstractN QRS complex detectors simultaneously monitor N ECG channels. Once a detection occurs in a given channel, a 150 ms long window is opened to look for detections in other channels. Within this window, yn = + 1 if a QRS complex is detected and yn = − 1 otherwise. A coefficient α n, obtained during a training period and related to the detection performance in channel n, multiplies the detection signal yn, so that greater weights are assigned to ECG channels where single-channel detectors performed better. Finally, the binary detection decision (f ) is obtained from the comparison of the weighted sum of single-channel detections (z) with a fixed threshold (β). QRS complex detection (dpeaa)DE-He213 Electrocardiography (dpeaa)DE-He213 Digital filters (dpeaa)DE-He213 Optimal data fusion (dpeaa)DE-He213 Detection algorithms (dpeaa)DE-He213 Altuve, Miguel (orcid)0000-0002-4064-2601 aut Enthalten in Medical & biological engineering & computing Cham : Springer Nature, 1963 57(2019), 8 vom: 17. Mai, Seite 1673-1681 (DE-627)331747456 (DE-600)2052667-2 1741-0444 nnns volume:57 year:2019 number:8 day:17 month:05 pages:1673-1681 https://dx.doi.org/10.1007/s11517-019-01990-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 57 2019 8 17 05 1673-1681 |
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10.1007/s11517-019-01990-3 doi (DE-627)SPR020522487 (SPR)s11517-019-01990-3-e DE-627 ger DE-627 rakwb eng Ledezma, Carlos A. verfasserin aut Optimal data fusion for the improvement of QRS complex detection in multi-channel ECG recordings 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Federation for Medical and Biological Engineering 2019 Abstract The automatic analysis of the electrocardiogram (ECG) begins, traditionally, with the detection of QRS complexes. Afterwards, useful information can be extracted from it, ranging from the estimation of the instantaneous heart rate to nonlinear heart rate variability analysis. A plethora of works have been published on this topic; consequently, there exist many QRS complex detectors with high-performance values. However, just a few detectors have been conceived that profit from the information contained in several ECG leads to provide a robust QRS complex detection. In this work, we explore the fusion of multi-channel ECG recordings QRS detections as a means to improve the detection performance. This paper presents a decentralized multi-channel QRS complex fusion scheme that optimally combines single-channel detections to produce a single detection signal. Using six different widely used QRS complex detectors on the MIT-BIH Arrhythmia and INCART databases, a reduction in false and missed detections was achieved with the proposed approach compared with the single-channel counterpart. Furthermore, our detection results are comparable with the performance of other multi-channel detectors found in the literature, showing, in turn, various advantages in scalability, adaptability, and simplicity in the system’s implementation Graphical AbstractN QRS complex detectors simultaneously monitor N ECG channels. Once a detection occurs in a given channel, a 150 ms long window is opened to look for detections in other channels. Within this window, yn = + 1 if a QRS complex is detected and yn = − 1 otherwise. A coefficient α n, obtained during a training period and related to the detection performance in channel n, multiplies the detection signal yn, so that greater weights are assigned to ECG channels where single-channel detectors performed better. Finally, the binary detection decision (f ) is obtained from the comparison of the weighted sum of single-channel detections (z) with a fixed threshold (β). QRS complex detection (dpeaa)DE-He213 Electrocardiography (dpeaa)DE-He213 Digital filters (dpeaa)DE-He213 Optimal data fusion (dpeaa)DE-He213 Detection algorithms (dpeaa)DE-He213 Altuve, Miguel (orcid)0000-0002-4064-2601 aut Enthalten in Medical & biological engineering & computing Cham : Springer Nature, 1963 57(2019), 8 vom: 17. Mai, Seite 1673-1681 (DE-627)331747456 (DE-600)2052667-2 1741-0444 nnns volume:57 year:2019 number:8 day:17 month:05 pages:1673-1681 https://dx.doi.org/10.1007/s11517-019-01990-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 57 2019 8 17 05 1673-1681 |
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10.1007/s11517-019-01990-3 doi (DE-627)SPR020522487 (SPR)s11517-019-01990-3-e DE-627 ger DE-627 rakwb eng Ledezma, Carlos A. verfasserin aut Optimal data fusion for the improvement of QRS complex detection in multi-channel ECG recordings 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Federation for Medical and Biological Engineering 2019 Abstract The automatic analysis of the electrocardiogram (ECG) begins, traditionally, with the detection of QRS complexes. Afterwards, useful information can be extracted from it, ranging from the estimation of the instantaneous heart rate to nonlinear heart rate variability analysis. A plethora of works have been published on this topic; consequently, there exist many QRS complex detectors with high-performance values. However, just a few detectors have been conceived that profit from the information contained in several ECG leads to provide a robust QRS complex detection. In this work, we explore the fusion of multi-channel ECG recordings QRS detections as a means to improve the detection performance. This paper presents a decentralized multi-channel QRS complex fusion scheme that optimally combines single-channel detections to produce a single detection signal. Using six different widely used QRS complex detectors on the MIT-BIH Arrhythmia and INCART databases, a reduction in false and missed detections was achieved with the proposed approach compared with the single-channel counterpart. Furthermore, our detection results are comparable with the performance of other multi-channel detectors found in the literature, showing, in turn, various advantages in scalability, adaptability, and simplicity in the system’s implementation Graphical AbstractN QRS complex detectors simultaneously monitor N ECG channels. Once a detection occurs in a given channel, a 150 ms long window is opened to look for detections in other channels. Within this window, yn = + 1 if a QRS complex is detected and yn = − 1 otherwise. A coefficient α n, obtained during a training period and related to the detection performance in channel n, multiplies the detection signal yn, so that greater weights are assigned to ECG channels where single-channel detectors performed better. Finally, the binary detection decision (f ) is obtained from the comparison of the weighted sum of single-channel detections (z) with a fixed threshold (β). QRS complex detection (dpeaa)DE-He213 Electrocardiography (dpeaa)DE-He213 Digital filters (dpeaa)DE-He213 Optimal data fusion (dpeaa)DE-He213 Detection algorithms (dpeaa)DE-He213 Altuve, Miguel (orcid)0000-0002-4064-2601 aut Enthalten in Medical & biological engineering & computing Cham : Springer Nature, 1963 57(2019), 8 vom: 17. Mai, Seite 1673-1681 (DE-627)331747456 (DE-600)2052667-2 1741-0444 nnns volume:57 year:2019 number:8 day:17 month:05 pages:1673-1681 https://dx.doi.org/10.1007/s11517-019-01990-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 57 2019 8 17 05 1673-1681 |
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10.1007/s11517-019-01990-3 doi (DE-627)SPR020522487 (SPR)s11517-019-01990-3-e DE-627 ger DE-627 rakwb eng Ledezma, Carlos A. verfasserin aut Optimal data fusion for the improvement of QRS complex detection in multi-channel ECG recordings 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Federation for Medical and Biological Engineering 2019 Abstract The automatic analysis of the electrocardiogram (ECG) begins, traditionally, with the detection of QRS complexes. Afterwards, useful information can be extracted from it, ranging from the estimation of the instantaneous heart rate to nonlinear heart rate variability analysis. A plethora of works have been published on this topic; consequently, there exist many QRS complex detectors with high-performance values. However, just a few detectors have been conceived that profit from the information contained in several ECG leads to provide a robust QRS complex detection. In this work, we explore the fusion of multi-channel ECG recordings QRS detections as a means to improve the detection performance. This paper presents a decentralized multi-channel QRS complex fusion scheme that optimally combines single-channel detections to produce a single detection signal. Using six different widely used QRS complex detectors on the MIT-BIH Arrhythmia and INCART databases, a reduction in false and missed detections was achieved with the proposed approach compared with the single-channel counterpart. Furthermore, our detection results are comparable with the performance of other multi-channel detectors found in the literature, showing, in turn, various advantages in scalability, adaptability, and simplicity in the system’s implementation Graphical AbstractN QRS complex detectors simultaneously monitor N ECG channels. Once a detection occurs in a given channel, a 150 ms long window is opened to look for detections in other channels. Within this window, yn = + 1 if a QRS complex is detected and yn = − 1 otherwise. A coefficient α n, obtained during a training period and related to the detection performance in channel n, multiplies the detection signal yn, so that greater weights are assigned to ECG channels where single-channel detectors performed better. Finally, the binary detection decision (f ) is obtained from the comparison of the weighted sum of single-channel detections (z) with a fixed threshold (β). QRS complex detection (dpeaa)DE-He213 Electrocardiography (dpeaa)DE-He213 Digital filters (dpeaa)DE-He213 Optimal data fusion (dpeaa)DE-He213 Detection algorithms (dpeaa)DE-He213 Altuve, Miguel (orcid)0000-0002-4064-2601 aut Enthalten in Medical & biological engineering & computing Cham : Springer Nature, 1963 57(2019), 8 vom: 17. Mai, Seite 1673-1681 (DE-627)331747456 (DE-600)2052667-2 1741-0444 nnns volume:57 year:2019 number:8 day:17 month:05 pages:1673-1681 https://dx.doi.org/10.1007/s11517-019-01990-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 57 2019 8 17 05 1673-1681 |
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10.1007/s11517-019-01990-3 doi (DE-627)SPR020522487 (SPR)s11517-019-01990-3-e DE-627 ger DE-627 rakwb eng Ledezma, Carlos A. verfasserin aut Optimal data fusion for the improvement of QRS complex detection in multi-channel ECG recordings 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Federation for Medical and Biological Engineering 2019 Abstract The automatic analysis of the electrocardiogram (ECG) begins, traditionally, with the detection of QRS complexes. Afterwards, useful information can be extracted from it, ranging from the estimation of the instantaneous heart rate to nonlinear heart rate variability analysis. A plethora of works have been published on this topic; consequently, there exist many QRS complex detectors with high-performance values. However, just a few detectors have been conceived that profit from the information contained in several ECG leads to provide a robust QRS complex detection. In this work, we explore the fusion of multi-channel ECG recordings QRS detections as a means to improve the detection performance. This paper presents a decentralized multi-channel QRS complex fusion scheme that optimally combines single-channel detections to produce a single detection signal. Using six different widely used QRS complex detectors on the MIT-BIH Arrhythmia and INCART databases, a reduction in false and missed detections was achieved with the proposed approach compared with the single-channel counterpart. Furthermore, our detection results are comparable with the performance of other multi-channel detectors found in the literature, showing, in turn, various advantages in scalability, adaptability, and simplicity in the system’s implementation Graphical AbstractN QRS complex detectors simultaneously monitor N ECG channels. Once a detection occurs in a given channel, a 150 ms long window is opened to look for detections in other channels. Within this window, yn = + 1 if a QRS complex is detected and yn = − 1 otherwise. A coefficient α n, obtained during a training period and related to the detection performance in channel n, multiplies the detection signal yn, so that greater weights are assigned to ECG channels where single-channel detectors performed better. Finally, the binary detection decision (f ) is obtained from the comparison of the weighted sum of single-channel detections (z) with a fixed threshold (β). QRS complex detection (dpeaa)DE-He213 Electrocardiography (dpeaa)DE-He213 Digital filters (dpeaa)DE-He213 Optimal data fusion (dpeaa)DE-He213 Detection algorithms (dpeaa)DE-He213 Altuve, Miguel (orcid)0000-0002-4064-2601 aut Enthalten in Medical & biological engineering & computing Cham : Springer Nature, 1963 57(2019), 8 vom: 17. Mai, Seite 1673-1681 (DE-627)331747456 (DE-600)2052667-2 1741-0444 nnns volume:57 year:2019 number:8 day:17 month:05 pages:1673-1681 https://dx.doi.org/10.1007/s11517-019-01990-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 57 2019 8 17 05 1673-1681 |
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Ledezma, Carlos A. @@aut@@ Altuve, Miguel @@aut@@ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR020522487</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230519235854.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201006s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11517-019-01990-3</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR020522487</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11517-019-01990-3-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ledezma, Carlos A.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Optimal data fusion for the improvement of QRS complex detection in multi-channel ECG recordings</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© International Federation for Medical and Biological Engineering 2019</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The automatic analysis of the electrocardiogram (ECG) begins, traditionally, with the detection of QRS complexes. Afterwards, useful information can be extracted from it, ranging from the estimation of the instantaneous heart rate to nonlinear heart rate variability analysis. A plethora of works have been published on this topic; consequently, there exist many QRS complex detectors with high-performance values. However, just a few detectors have been conceived that profit from the information contained in several ECG leads to provide a robust QRS complex detection. In this work, we explore the fusion of multi-channel ECG recordings QRS detections as a means to improve the detection performance. This paper presents a decentralized multi-channel QRS complex fusion scheme that optimally combines single-channel detections to produce a single detection signal. Using six different widely used QRS complex detectors on the MIT-BIH Arrhythmia and INCART databases, a reduction in false and missed detections was achieved with the proposed approach compared with the single-channel counterpart. Furthermore, our detection results are comparable with the performance of other multi-channel detectors found in the literature, showing, in turn, various advantages in scalability, adaptability, and simplicity in the system’s implementation Graphical AbstractN QRS complex detectors simultaneously monitor N ECG channels. Once a detection occurs in a given channel, a 150 ms long window is opened to look for detections in other channels. Within this window, yn = + 1 if a QRS complex is detected and yn = − 1 otherwise. A coefficient α n, obtained during a training period and related to the detection performance in channel n, multiplies the detection signal yn, so that greater weights are assigned to ECG channels where single-channel detectors performed better. Finally, the binary detection decision (f ) is obtained from the comparison of the weighted sum of single-channel detections (z) with a fixed threshold (β).</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">QRS complex detection</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Electrocardiography</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Digital filters</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Optimal data fusion</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Detection algorithms</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Altuve, Miguel</subfield><subfield code="0">(orcid)0000-0002-4064-2601</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Medical & biological engineering & computing</subfield><subfield code="d">Cham : Springer Nature, 1963</subfield><subfield code="g">57(2019), 8 vom: 17. 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|
| author |
Ledezma, Carlos A. |
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Ledezma, Carlos A. misc QRS complex detection misc Electrocardiography misc Digital filters misc Optimal data fusion misc Detection algorithms Optimal data fusion for the improvement of QRS complex detection in multi-channel ECG recordings |
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Ledezma, Carlos A. |
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1741-0444 |
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Optimal data fusion for the improvement of QRS complex detection in multi-channel ECG recordings QRS complex detection (dpeaa)DE-He213 Electrocardiography (dpeaa)DE-He213 Digital filters (dpeaa)DE-He213 Optimal data fusion (dpeaa)DE-He213 Detection algorithms (dpeaa)DE-He213 |
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misc QRS complex detection misc Electrocardiography misc Digital filters misc Optimal data fusion misc Detection algorithms |
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misc QRS complex detection misc Electrocardiography misc Digital filters misc Optimal data fusion misc Detection algorithms |
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misc QRS complex detection misc Electrocardiography misc Digital filters misc Optimal data fusion misc Detection algorithms |
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Elektronische Aufsätze Aufsätze Elektronische Ressource |
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Optimal data fusion for the improvement of QRS complex detection in multi-channel ECG recordings |
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Optimal data fusion for the improvement of QRS complex detection in multi-channel ECG recordings |
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Ledezma, Carlos A. |
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Medical & biological engineering & computing |
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Medical & biological engineering & computing |
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Ledezma, Carlos A. Altuve, Miguel |
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Ledezma, Carlos A. |
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10.1007/s11517-019-01990-3 |
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optimal data fusion for the improvement of qrs complex detection in multi-channel ecg recordings |
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Optimal data fusion for the improvement of QRS complex detection in multi-channel ECG recordings |
| abstract |
Abstract The automatic analysis of the electrocardiogram (ECG) begins, traditionally, with the detection of QRS complexes. Afterwards, useful information can be extracted from it, ranging from the estimation of the instantaneous heart rate to nonlinear heart rate variability analysis. A plethora of works have been published on this topic; consequently, there exist many QRS complex detectors with high-performance values. However, just a few detectors have been conceived that profit from the information contained in several ECG leads to provide a robust QRS complex detection. In this work, we explore the fusion of multi-channel ECG recordings QRS detections as a means to improve the detection performance. This paper presents a decentralized multi-channel QRS complex fusion scheme that optimally combines single-channel detections to produce a single detection signal. Using six different widely used QRS complex detectors on the MIT-BIH Arrhythmia and INCART databases, a reduction in false and missed detections was achieved with the proposed approach compared with the single-channel counterpart. Furthermore, our detection results are comparable with the performance of other multi-channel detectors found in the literature, showing, in turn, various advantages in scalability, adaptability, and simplicity in the system’s implementation Graphical AbstractN QRS complex detectors simultaneously monitor N ECG channels. Once a detection occurs in a given channel, a 150 ms long window is opened to look for detections in other channels. Within this window, yn = + 1 if a QRS complex is detected and yn = − 1 otherwise. A coefficient α n, obtained during a training period and related to the detection performance in channel n, multiplies the detection signal yn, so that greater weights are assigned to ECG channels where single-channel detectors performed better. Finally, the binary detection decision (f ) is obtained from the comparison of the weighted sum of single-channel detections (z) with a fixed threshold (β). © International Federation for Medical and Biological Engineering 2019 |
| abstractGer |
Abstract The automatic analysis of the electrocardiogram (ECG) begins, traditionally, with the detection of QRS complexes. Afterwards, useful information can be extracted from it, ranging from the estimation of the instantaneous heart rate to nonlinear heart rate variability analysis. A plethora of works have been published on this topic; consequently, there exist many QRS complex detectors with high-performance values. However, just a few detectors have been conceived that profit from the information contained in several ECG leads to provide a robust QRS complex detection. In this work, we explore the fusion of multi-channel ECG recordings QRS detections as a means to improve the detection performance. This paper presents a decentralized multi-channel QRS complex fusion scheme that optimally combines single-channel detections to produce a single detection signal. Using six different widely used QRS complex detectors on the MIT-BIH Arrhythmia and INCART databases, a reduction in false and missed detections was achieved with the proposed approach compared with the single-channel counterpart. Furthermore, our detection results are comparable with the performance of other multi-channel detectors found in the literature, showing, in turn, various advantages in scalability, adaptability, and simplicity in the system’s implementation Graphical AbstractN QRS complex detectors simultaneously monitor N ECG channels. Once a detection occurs in a given channel, a 150 ms long window is opened to look for detections in other channels. Within this window, yn = + 1 if a QRS complex is detected and yn = − 1 otherwise. A coefficient α n, obtained during a training period and related to the detection performance in channel n, multiplies the detection signal yn, so that greater weights are assigned to ECG channels where single-channel detectors performed better. Finally, the binary detection decision (f ) is obtained from the comparison of the weighted sum of single-channel detections (z) with a fixed threshold (β). © International Federation for Medical and Biological Engineering 2019 |
| abstract_unstemmed |
Abstract The automatic analysis of the electrocardiogram (ECG) begins, traditionally, with the detection of QRS complexes. Afterwards, useful information can be extracted from it, ranging from the estimation of the instantaneous heart rate to nonlinear heart rate variability analysis. A plethora of works have been published on this topic; consequently, there exist many QRS complex detectors with high-performance values. However, just a few detectors have been conceived that profit from the information contained in several ECG leads to provide a robust QRS complex detection. In this work, we explore the fusion of multi-channel ECG recordings QRS detections as a means to improve the detection performance. This paper presents a decentralized multi-channel QRS complex fusion scheme that optimally combines single-channel detections to produce a single detection signal. Using six different widely used QRS complex detectors on the MIT-BIH Arrhythmia and INCART databases, a reduction in false and missed detections was achieved with the proposed approach compared with the single-channel counterpart. Furthermore, our detection results are comparable with the performance of other multi-channel detectors found in the literature, showing, in turn, various advantages in scalability, adaptability, and simplicity in the system’s implementation Graphical AbstractN QRS complex detectors simultaneously monitor N ECG channels. Once a detection occurs in a given channel, a 150 ms long window is opened to look for detections in other channels. Within this window, yn = + 1 if a QRS complex is detected and yn = − 1 otherwise. A coefficient α n, obtained during a training period and related to the detection performance in channel n, multiplies the detection signal yn, so that greater weights are assigned to ECG channels where single-channel detectors performed better. Finally, the binary detection decision (f ) is obtained from the comparison of the weighted sum of single-channel detections (z) with a fixed threshold (β). © International Federation for Medical and Biological Engineering 2019 |
| collection_details |
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| container_issue |
8 |
| title_short |
Optimal data fusion for the improvement of QRS complex detection in multi-channel ECG recordings |
| url |
https://dx.doi.org/10.1007/s11517-019-01990-3 |
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| author2 |
Altuve, Miguel |
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| doi_str |
10.1007/s11517-019-01990-3 |
| up_date |
2024-07-03T16:35:19.102Z |
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| score |
7.3994884 |