Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks
Goal: This paper presents a fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system. Methods: An adaptive implementation of 1-D convolutional neural networks (CNNs) is inherently used to fuse the two major blocks of the ECG classification into a single learnin...
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| Published in | IEEE transactions on biomedical engineering Vol. 63; no. 3; pp. 664 - 675 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
IEEE
01.03.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Goal: This paper presents a fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system. Methods: An adaptive implementation of 1-D convolutional neural networks (CNNs) is inherently used to fuse the two major blocks of the ECG classification into a single learning body: feature extraction and classification. Therefore, for each patient, an individual and simple CNN will be trained by using relatively small common and patient-specific training data, and thus, such patient-specific feature extraction ability can further improve the classification performance. Since this also negates the necessity to extract hand-crafted manual features, once a dedicated CNN is trained for a particular patient, it can solely be used to classify possibly long ECG data stream in a fast and accurate manner or alternatively, such a solution can conveniently be used for real-time ECG monitoring and early alert system on a light-weight wearable device. Results: The results over the MIT-BIH arrhythmia benchmark database demonstrate that the proposed solution achieves a superior classification performance than most of the state-of-the-art methods for the detection of ventricular ectopic beats and supraventricular ectopic beats. Conclusion: Besides the speed and computational efficiency achieved, once a dedicated CNN is trained for an individual patient, it can solely be used to classify his/her long ECG records such as Holter registers in a fast and accurate manner. Significance: Due to its simple and parameter invariant nature, the proposed system is highly generic, and, thus, applicable to any ECG dataset. |
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| AbstractList | This paper presents a fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system.GOALThis paper presents a fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system.An adaptive implementation of 1-D convolutional neural networks (CNNs) is inherently used to fuse the two major blocks of the ECG classification into a single learning body: feature extraction and classification. Therefore, for each patient, an individual and simple CNN will be trained by using relatively small common and patient-specific training data, and thus, such patient-specific feature extraction ability can further improve the classification performance. Since this also negates the necessity to extract hand-crafted manual features, once a dedicated CNN is trained for a particular patient, it can solely be used to classify possibly long ECG data stream in a fast and accurate manner or alternatively, such a solution can conveniently be used for real-time ECG monitoring and early alert system on a light-weight wearable device.METHODSAn adaptive implementation of 1-D convolutional neural networks (CNNs) is inherently used to fuse the two major blocks of the ECG classification into a single learning body: feature extraction and classification. Therefore, for each patient, an individual and simple CNN will be trained by using relatively small common and patient-specific training data, and thus, such patient-specific feature extraction ability can further improve the classification performance. Since this also negates the necessity to extract hand-crafted manual features, once a dedicated CNN is trained for a particular patient, it can solely be used to classify possibly long ECG data stream in a fast and accurate manner or alternatively, such a solution can conveniently be used for real-time ECG monitoring and early alert system on a light-weight wearable device.The results over the MIT-BIH arrhythmia benchmark database demonstrate that the proposed solution achieves a superior classification performance than most of the state-of-the-art methods for the detection of ventricular ectopic beats and supraventricular ectopic beats.RESULTSThe results over the MIT-BIH arrhythmia benchmark database demonstrate that the proposed solution achieves a superior classification performance than most of the state-of-the-art methods for the detection of ventricular ectopic beats and supraventricular ectopic beats.Besides the speed and computational efficiency achieved, once a dedicated CNN is trained for an individual patient, it can solely be used to classify his/her long ECG records such as Holter registers in a fast and accurate manner.CONCLUSIONBesides the speed and computational efficiency achieved, once a dedicated CNN is trained for an individual patient, it can solely be used to classify his/her long ECG records such as Holter registers in a fast and accurate manner.Due to its simple and parameter invariant nature, the proposed system is highly generic, and, thus, applicable to any ECG dataset.SIGNIFICANCEDue to its simple and parameter invariant nature, the proposed system is highly generic, and, thus, applicable to any ECG dataset. This paper presents a fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system. An adaptive implementation of 1-D convolutional neural networks (CNNs) is inherently used to fuse the two major blocks of the ECG classification into a single learning body: feature extraction and classification. Therefore, for each patient, an individual and simple CNN will be trained by using relatively small common and patient-specific training data, and thus, such patient-specific feature extraction ability can further improve the classification performance. Since this also negates the necessity to extract hand-crafted manual features, once a dedicated CNN is trained for a particular patient, it can solely be used to classify possibly long ECG data stream in a fast and accurate manner or alternatively, such a solution can conveniently be used for real-time ECG monitoring and early alert system on a light-weight wearable device. The results over the MIT-BIH arrhythmia benchmark database demonstrate that the proposed solution achieves a superior classification performance than most of the state-of-the-art methods for the detection of ventricular ectopic beats and supraventricular ectopic beats. Besides the speed and computational efficiency achieved, once a dedicated CNN is trained for an individual patient, it can solely be used to classify his/her long ECG records such as Holter registers in a fast and accurate manner. Due to its simple and parameter invariant nature, the proposed system is highly generic, and, thus, applicable to any ECG dataset. Goal: This paper presents a fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system. Methods: An adaptive implementation of 1-D convolutional neural networks (CNNs) is inherently used to fuse the two major blocks of the ECG classification into a single learning body: feature extraction and classification. Therefore, for each patient, an individual and simple CNN will be trained by using relatively small common and patient-specific training data, and thus, such patient-specific feature extraction ability can further improve the classification performance. Since this also negates the necessity to extract hand-crafted manual features, once a dedicated CNN is trained for a particular patient, it can solely be used to classify possibly long ECG data stream in a fast and accurate manner or alternatively, such a solution can conveniently be used for real-time ECG monitoring and early alert system on a light-weight wearable device. Results: The results over the MIT-BIH arrhythmia benchmark database demonstrate that the proposed solution achieves a superior classification performance than most of the state-of-the-art methods for the detection of ventricular ectopic beats and supraventricular ectopic beats. Conclusion: Besides the speed and computational efficiency achieved, once a dedicated CNN is trained for an individual patient, it can solely be used to classify his/her long ECG records such as Holter registers in a fast and accurate manner. Significance: Due to its simple and parameter invariant nature, the proposed system is highly generic, and, thus, applicable to any ECG dataset. |
| Author | Kiranyaz, Serkan Ince, Turker Gabbouj, Moncef |
| Author_xml | – sequence: 1 givenname: Serkan surname: Kiranyaz fullname: Kiranyaz, Serkan email: mkiranyaz@qu.edu.qa organization: Electrical Engineering Department, College of Engineering, Doha, Qatar – sequence: 2 givenname: Turker surname: Ince fullname: Ince, Turker organization: the Electrical & Electronics Engineering Department, Izmir University of Economics, İzmir, Turkey – sequence: 3 givenname: Moncef surname: Gabbouj fullname: Gabbouj, Moncef organization: Tampere University of Technology |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26285054$$D View this record in MEDLINE/PubMed |
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| CODEN | IEBEAX |
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| Cites_doi | 10.1109/10.623058 10.1109/51.932724 10.1113/jphysiol.1959.sp006308 10.1109/10.362922 10.1109/10.740882 10.1109/TBME.2009.2013934 10.1109/TBME.1985.325532 10.1109/IEMBS.1989.96577 10.1109/10.58593 10.1109/TBME.2011.2113395 10.1109/TBME.2004.824138 10.1109/TNN.2007.900239 10.1109/TBME.2006.880879 10.1109/10.918594 10.1109/10.740880 10.1109/TBME.2012.2202662 10.1162/NECO_a_00052 10.1109/TBME.2004.827359 10.1109/TBME.2006.883802 10.1016/S0022-0736(87)80096-1 |
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| Keywords | real-time heart monitoring Convolutional neural networks (CNNs) patient-specific ECG classification |
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| References | ref12 ref15 ref14 hu (ref9) 1994; 26 ref11 ref2 ref1 ref16 ref18 hu (ref10) 1997; 44 (ref13) 1987 scherer (ref25) 0 ref24 ref23 kiranyaz (ref17) 2013 ref20 ref27 talmon (ref6) 1983 ref8 ref7 ref4 ref3 ref5 li (ref19) 1995; 42 krizhevsky (ref26) 0 wiesel (ref21) 1959; 148 mark (ref22) 0 |
| References_xml | – volume: 44 start-page: 891 year: 1997 ident: ref10 article-title: A patient-adaptable ECG beat classifier using a mixture of experts approach publication-title: IEEE Trans Biomed Eng doi: 10.1109/10.623058 – year: 2013 ident: ref17 publication-title: Multi-dimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition – ident: ref27 doi: 10.1109/51.932724 – year: 1987 ident: ref13 publication-title: Recommended Practice for Testing and Reporting Performance Results of Ventricular Arrhythmia Detection Algorithms – volume: 148 start-page: 574 year: 1959 ident: ref21 article-title: Receptive fields of single neurones in the cat's striate cortex publication-title: J Physiol doi: 10.1113/jphysiol.1959.sp006308 – volume: 42 start-page: 21 year: 1995 ident: ref19 article-title: Detection of ECG characteristic points using wavelet transforms publication-title: IEEE Trans Biomed Eng doi: 10.1109/10.362922 – ident: ref4 doi: 10.1109/10.740882 – ident: ref16 doi: 10.1109/TBME.2009.2013934 – volume: 26 start-page: 66 year: 1994 ident: ref9 article-title: Applications of artificial neural networks for ECG signal detection and classification publication-title: J Electrocardiol – ident: ref23 doi: 10.1109/TBME.1985.325532 – ident: ref11 doi: 10.1109/IEMBS.1989.96577 – ident: ref7 doi: 10.1109/10.58593 – ident: ref20 doi: 10.1109/TBME.2011.2113395 – year: 0 ident: ref22 article-title: MIT-BIH Arrhythmia Database Directory – year: 1983 ident: ref6 publication-title: Pattern Recognition of the ECG – ident: ref8 doi: 10.1109/TBME.2004.824138 – start-page: 1106 year: 0 ident: ref26 article-title: Imagenet classification with deep convolutional neural networks publication-title: Proc Adv Neural Inf Process Syst Conf – ident: ref15 doi: 10.1109/TNN.2007.900239 – ident: ref3 doi: 10.1109/TBME.2006.880879 – ident: ref1 doi: 10.1109/10.918594 – ident: ref2 doi: 10.1109/10.740880 – ident: ref18 doi: 10.1109/TBME.2012.2202662 – ident: ref24 doi: 10.1162/NECO_a_00052 – ident: ref14 doi: 10.1109/TBME.2004.827359 – ident: ref12 doi: 10.1109/TBME.2006.883802 – ident: ref5 doi: 10.1016/S0022-0736(87)80096-1 – start-page: 92 year: 0 ident: ref25 article-title: Evaluation of pooling operations in convolutional architectures for object recognition publication-title: Proc Int Conf Artif Neural Netw |
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| Snippet | Goal: This paper presents a fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system. Methods: An adaptive... This paper presents a fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system. An adaptive implementation of 1-D... This paper presents a fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system.GOALThis paper presents a fast and... |
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| SubjectTerms | Algorithms Classification Convolutional Neural Networks Databases, Factual Electrocardiography Electrocardiography - methods Feature extraction Humans Kernel Monitoring Neural networks Neural Networks (Computer) Neurons Patient-specific ECG classification Precision Medicine real-time heart monitoring Signal Processing, Computer-Assisted Training |
| Title | Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks |
| URI | https://ieeexplore.ieee.org/document/7202837 https://www.ncbi.nlm.nih.gov/pubmed/26285054 https://www.proquest.com/docview/1787244351 https://www.proquest.com/docview/1768562789 |
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