O-WCNN: an optimized integration of spatial and spectral feature map for arrhythmia classification
The regular monitoring and accurate diagnosis of arrhythmia are critically important, leading to a reduction in mortality rate due to cardiovascular diseases (CVD) such as heart stroke or cardiac arrest. This paper proposes a novel convolutional neural network (CNN) model for arrhythmia classificati...
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| Vydáno v: | Complex & intelligent systems Ročník 9; číslo 3; s. 2685 - 2698 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Springer International Publishing
01.06.2023
Springer Nature B.V Springer |
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| ISSN: | 2199-4536, 2198-6053, 2198-6053 |
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| Abstract | The regular monitoring and accurate diagnosis of arrhythmia are critically important, leading to a reduction in mortality rate due to cardiovascular diseases (CVD) such as heart stroke or cardiac arrest. This paper proposes a novel convolutional neural network (CNN) model for arrhythmia classification. The proposed model offers the following improvements compared with traditional CNN models. Firstly, the multi-channel model can concatenate spectral and spatial feature maps. Secondly, the structural unit is composed of a depthwise separable convolution layer followed by activation and batch normalization layers. The structural unit offers effective utilization of network parameters. Also, the optimization of hyperparameters is done using Hyperopt library, based on Sequential Model-Based Global Optimization algorithm (SMBO). These improvements make the network more efficient and accurate for arrhythmia classification. The proposed model is evaluated using tenfold cross-validation following both subject-oriented inter-patient and class-oriented intra-patient evaluation protocols. Our model achieved 99.48% and 99.46% accuracy in VEB (ventricular ectopic beat) and SVEB (supraventricular ectopic beat) class classification, respectively. The model is compared with state-of-the-art models and has shown significant performance improvement. |
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| AbstractList | The regular monitoring and accurate diagnosis of arrhythmia are critically important, leading to a reduction in mortality rate due to cardiovascular diseases (CVD) such as heart stroke or cardiac arrest. This paper proposes a novel convolutional neural network (CNN) model for arrhythmia classification. The proposed model offers the following improvements compared with traditional CNN models. Firstly, the multi-channel model can concatenate spectral and spatial feature maps. Secondly, the structural unit is composed of a depthwise separable convolution layer followed by activation and batch normalization layers. The structural unit offers effective utilization of network parameters. Also, the optimization of hyperparameters is done using Hyperopt library, based on Sequential Model-Based Global Optimization algorithm (SMBO). These improvements make the network more efficient and accurate for arrhythmia classification. The proposed model is evaluated using tenfold cross-validation following both subject-oriented inter-patient and class-oriented intra-patient evaluation protocols. Our model achieved 99.48% and 99.46% accuracy in VEB (ventricular ectopic beat) and SVEB (supraventricular ectopic beat) class classification, respectively. The model is compared with state-of-the-art models and has shown significant performance improvement. Abstract The regular monitoring and accurate diagnosis of arrhythmia are critically important, leading to a reduction in mortality rate due to cardiovascular diseases (CVD) such as heart stroke or cardiac arrest. This paper proposes a novel convolutional neural network (CNN) model for arrhythmia classification. The proposed model offers the following improvements compared with traditional CNN models. Firstly, the multi-channel model can concatenate spectral and spatial feature maps. Secondly, the structural unit is composed of a depthwise separable convolution layer followed by activation and batch normalization layers. The structural unit offers effective utilization of network parameters. Also, the optimization of hyperparameters is done using Hyperopt library, based on Sequential Model-Based Global Optimization algorithm (SMBO). These improvements make the network more efficient and accurate for arrhythmia classification. The proposed model is evaluated using tenfold cross-validation following both subject-oriented inter-patient and class-oriented intra-patient evaluation protocols. Our model achieved 99.48% and 99.46% accuracy in VEB (ventricular ectopic beat) and SVEB (supraventricular ectopic beat) class classification, respectively. The model is compared with state-of-the-art models and has shown significant performance improvement. The regular monitoring and accurate diagnosis of arrhythmia are critically important, leading to a reduction in mortality rate due to cardiovascular diseases (CVD) such as heart stroke or cardiac arrest. This paper proposes a novel convolutional neural network (CNN) model for arrhythmia classification. The proposed model offers the following improvements compared with traditional CNN models. Firstly, the multi-channel model can concatenate spectral and spatial feature maps. Secondly, the structural unit is composed of a depthwise separable convolution layer followed by activation and batch normalization layers. The structural unit offers effective utilization of network parameters. Also, the optimization of hyperparameters is done using Hyperopt library, based on Sequential Model-Based Global Optimization algorithm (SMBO). These improvements make the network more efficient and accurate for arrhythmia classification. The proposed model is evaluated using tenfold cross-validation following both subject-oriented inter-patient and class-oriented intra-patient evaluation protocols. Our model achieved 99.48% and 99.46% accuracy in VEB (ventricular ectopic beat) and SVEB (supraventricular ectopic beat) class classification, respectively. The model is compared with state-of-the-art models and has shown significant performance improvement.The regular monitoring and accurate diagnosis of arrhythmia are critically important, leading to a reduction in mortality rate due to cardiovascular diseases (CVD) such as heart stroke or cardiac arrest. This paper proposes a novel convolutional neural network (CNN) model for arrhythmia classification. The proposed model offers the following improvements compared with traditional CNN models. Firstly, the multi-channel model can concatenate spectral and spatial feature maps. Secondly, the structural unit is composed of a depthwise separable convolution layer followed by activation and batch normalization layers. The structural unit offers effective utilization of network parameters. Also, the optimization of hyperparameters is done using Hyperopt library, based on Sequential Model-Based Global Optimization algorithm (SMBO). These improvements make the network more efficient and accurate for arrhythmia classification. The proposed model is evaluated using tenfold cross-validation following both subject-oriented inter-patient and class-oriented intra-patient evaluation protocols. Our model achieved 99.48% and 99.46% accuracy in VEB (ventricular ectopic beat) and SVEB (supraventricular ectopic beat) class classification, respectively. The model is compared with state-of-the-art models and has shown significant performance improvement. |
| Author | Singh, Akansha Jangra, Manisha Singh, Krishna Kant Dhull, Sanjeev Kumar Cheng, Xiaochun |
| Author_xml | – sequence: 1 givenname: Manisha surname: Jangra fullname: Jangra, Manisha organization: Department of Electronics and Communication Engineering, Guru Jambheshwar University of Science and Technology – sequence: 2 givenname: Sanjeev Kumar surname: Dhull fullname: Dhull, Sanjeev Kumar organization: Department of Electronics and Communication Engineering, Guru Jambheshwar University of Science and Technology – sequence: 3 givenname: Krishna Kant orcidid: 0000-0002-6510-6768 surname: Singh fullname: Singh, Krishna Kant email: krishnaiitr2011@gmail.com organization: Faculty of Engineering and Technology, Jain (Deemed-to-be University) – sequence: 4 givenname: Akansha surname: Singh fullname: Singh, Akansha organization: Department of Computer Science Engineering, ASET, Amity University Uttar Pradesh – sequence: 5 givenname: Xiaochun surname: Cheng fullname: Cheng, Xiaochun organization: Department of Computer Science, Middlesex University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34777963$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1016/j.compbiomed.2018.03.016 10.1016/j.bspc.2013.01.005 10.1109/TITB.2008.923147 10.1161/CIRCULATIONAHA.120.046941 10.1109/IITSI.2010.85 10.1016/j.knosys.2013.02.007 10.1155/2013/261917 10.1007/978-3-030-58669-0_12 10.1088/0967-3334/26/5/R01 10.1016/j.compbiomed.2020.103866 10.1002/acs.2762 10.1109/ACCESS.2018.2833841 10.1109/ICSCCW.2009.5379457 10.1016/j.measurement.2011.10.025 10.1016/j.compbiomed.2017.08.022 10.3233/JIFS-191135 10.1201/9781315364094-33 10.1109/CVPR.2017.195 10.1016/j.cmpb.2020.105479 10.1109/TBMEL.1962.4322946 10.1109/ACCESS.2018.2807700 10.1109/ACCESS.2019.2890865 10.1214/aoms/1177729392 10.1109/ACCESS.2020.2964749 10.1109/ICASSP40776.2020.9054749 10.1109/LSENS.2020.3006756 10.32604/cmes.2020.010798 10.3233/IFS-141192 10.1016/j.eswa.2012.04.072 10.1016/j.media.2017.01.004 10.1186/1475-925X-13-90 10.1109/TBME.2004.827359 10.1049/iet-spr.2017.0296 10.1007/s00138-020-01128-8 10.1109/ACCESS.2020.2987930 10.1109/JBHI.2018.2858789 10.1016/j.measurement.2016.07.043 10.1109/ACCESS.2020.2998788 10.1214/aoms/1177729586 10.1007/978-3-030-21642-9_8.4 10.1109/51.932724 10.1109/TBME.2015.2468589 10.1016/j.asoc.2012.07.007 |
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| Keywords | Deep learning ECG CNN Wavelet transform Depthwise separable convolution Arrhythmia |
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| References | GaoZRobust estimation of carotid artery wall motion using the elasticity-based state-space approachMed Image Anal20173712110.1016/j.media.2017.01.004 MartisRJAcharyaURLimCMSuriJSCharacterization of ECG beats from cardiac arrhythmia using discrete cosine transform in PCA frameworkKnowl-Based Syst201345768210.1016/j.knosys.2013.02.007 El-KhafifSHEl-BrawanyMAArtificial neural network-based automated ECG signal classifierInt Scholar Res Notices201320131610.1155/2013/261917 GeronAHands-on machine learning with Scikit-Learn & TensorFlow2018O'Reilly Media Inc.ISBN:978-93-5213-521-9 World Health Organization (2020) Cardiovascular Disease. [Online]. Available via link http://www.who.int/cardiovascular_diseases/en/index.html El-Bouny L, Khalil M, Adib A (2020) ECG heartbeat classification based on multi-scale wavelet convolutional neural networks. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp 3212–3216 GaoZAutomatic segmentation of coronary tree in CT angiography imagesInt J Adapt Control Signal Process20193312391247399555510.1002/acs.27621432.92051 XiaYXieYA novel wearable electrocardiogram classification system using convolutional neural networks and active learningIEEE Access201977989800110.1109/ACCESS.2019.2890865 AndreottiFCarrOPimentelMAFMahdiAVosMDComparing feature-based classifiers and convolutional neural networks to detect arrhythmia from short segments of ECGComputCardiol20174414 NejadHCKhayatOAzadbakhBMohammadiMUsing feed forward neural network for electrocardiogram signal analysis in chaotic domainJ Intell Fuzzy Syst20142752289229610.3233/IFS-141192 ZhaiXTinCAutomated ECG classification using dual heartbeat coupling based on convolutional neural networkIEEE Access20186274652747210.1109/ACCESS.2018.2833841 FanXYaoQCaiYMiaoFSunFLiYMultiscaled fusion of deep convolutional neural networks for screening atrial fibrillation from single lead short ECG recordingsIEEE J Biomed Health Inform201822674475310.1109/JBHI.2018.2858789 Guangying Y, and Yue C (2010) The study of electrocardiograph based on radial basis function neural network. In: Proceedings of Third International Symposium on Intelligent Information Technology and Security Informatics IEEE, 2010, pp 143–145 ChenAMulti-information fusion neural networks for arrhythmia automatic detectionComput Methods Programs Biomed202019310547910.1016/j.cmpb.2020.105479 XuSSMakMWCheungCCTowards end-to-end ECG classification with raw signal extraction and deep neural networksIEEE J Biomed Health Inform2018148111 RobbinsHMonroSA stochastic approximation methodAnn Math Stat19512234004074266810.1214/aoms/11777295860054.05901 LuLYanJde SilvaCWFeature selection for ECG signal processing using improved genetic algorithm and empirical mode decompositionMeasurement20169437238110.1016/j.measurement.2016.07.043 Simonyan K, Zisserman A (2015) Very deep convolutional networks for large- scale image recognition In: Proceedings of International Conference on Learning Representations. pp 1–14 Shaker AM, Tantawi M, Shedeed HA, Tolba MF (2021) Deep convolutional neural networks for ECG heartbeat classification using two-stage hierarchical method. In Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2020. AISI 2020. Advances in Intelligent Systems and Computing, vol 1261. Springer, Cham. https://doi.org/10.1007/978-3-030-58669-0_12 OsowskiSHoaiLTAnalysis of features for efficient ECG signal classification using neuro-fuzzy networkProc IEEE Int Joint Conf Neural Networks2004324432448 BanerjeeSGuptaRMitraMDelineation of ECG characteristic features using multi-resolution wavelet analysis methodMeasurement201245347448710.1016/j.measurement.2011.10.025 Jangra M, Singh KK and Dhull SK (2017) Recent trends in arrhythmia beat detection: a review. In: Communication and Computing System. Proceedings of the International Conference on Communication and Computing Systems, ICCCS 2016, pp 177–184. https://doi.org/10.1201/9781315364094-33. MartisRJAcharyaURMinLCECG beat classification using PCA, LDA, ICA and Discrete Wavelet TransformBiomed Signal Process Control20138543744810.1016/j.bspc.2013.01.005 BergstraJKomerBEliasmithCYaminsDCoxDDHyperopt: a Python library for model selection and hyperparameter optimizationComputSciDiscov20158124 ANSI/AAMI EC57 (1998) Testing and Reporting Performance Results of Cardiac Rhythm and ST Segment Measurement Algorithms (AAMI Recommended Practice/American National Standard), Order Code: EC57–293. http://www.aami.org XuXLiuHECG heartbeat classification using convolutional neural networksIEEE Access202088614861910.1109/ACCESS.2020.2964749 LeiteJPRRMorenoRLHeartbeat classification with low computational cost using Hjorth parametersIET Signal Proc201812443143810.1049/iet-spr.2017.0296 Chollet F (2017) Xception: deep learning with depthwise separable convolutions. In: Proceedings—30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, pp 1800–1807. https://doi.org/10.1109/CVPR.2017.195 SteinbergCAAbrahamSCaceresCAPattern recognition in the clinical electrocardiogramIRE Trans Biomed Electron196291233010.1109/TBMEL.1962.4322946 AcharyaUROhSLHagiwaraYTanJHAdamMGertychASanTRA deep convolutional neural network model to classify heartbeatsComputBiol Med20178938939610.1016/j.compbiomed.2017.08.022 RomdhaneTFElectrocardiogram heartbeat classification based on a deep convolutional neural network and focal lossComputBiol Med202012310386610.1016/j.compbiomed.2020.103866 JangraMDhullSKSinghKKECG arrhythmia classification using modified visual geometry group network (mVGGNet)J Intell Fuzzy Syst20203833151316510.3233/JIFS-191135 Bergstra J, Bardenet R, Bengio Y, Kegl B (2011) Algorithms for hyper-parameter optimization. In: Proceedings of Conference on Advances in Neural Information Processing Systems. pp 1–9. https://github.com/maxpumperla/hyperas YildirimÖA novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classificationComputBiol Med20189618920210.1016/j.compbiomed.2018.03.016 Roy S, Kiral-Kornek I, Harrer S (2019) Chrononet: a deep recurrent neural network for abnormal EEG identification, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11526 LNAI, pp 47–56. https://doi.org/10.1007/978-3-030-21642-9_8.4 ZhangYDA five-layer deep convolutional neural network with stochastic pooling for chest CT-based COVID-19 diagnosisMach Vis Appl202110.1007/s00138-020-01128-8Springer Berlin Heidelberg ClerkinKJCOVID-19 and cardiovascular diseaseAHA Circ202010.1161/CIRCULATIONAHA.120.046941 DoganBKorürekMA New ECG beat clustering method based on kernelized fuzzy C- mean and hybrid ant colony optimization for continuous domainsAppl Soft Comput201212113442345110.1016/j.asoc.2012.07.007 GangulyBAutomated detection and classification of arrhythmia from ecg signals using feature induced long short-term memory networkIEEE SensLett2020235810.1109/LSENS.2020.3006756 MelganiFBaziYClassification of electrocardiogram signals with support vector machine and particle swarm optimizationIEEE Trans InfTechnol Biomed200812566767710.1109/TITB.2008.923147 HuangHA new hierarchical method for inter-patient heartbeat classification using random projections and RR intervalsBiomed Eng Online201413112610.1186/1475-925X-13-90 MartisRJAcharyaURMandanaKMRayAKChakrabortyCApplication of principal component analysis to ECG signals for automated diagnosis of cardiac healthExpert SystAppl20123914117921180010.1016/j.eswa.2012.04.072 Emanet N (2009) ECG beat classification by using discrete wavelet transform and random forest algorithm. In: Fifth International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control, 2009 pp 1–4 KiranyazSInceTGabboujMReal-time patient-specific ECG classification by 1-D convolutional neural networksIEEE Trans Biomed Eng201663366467510.1109/TBME.2015.2468589 XiaYZhangHXuLGaoZZhangHLiuHLiSAn automatic cardiac arrhythmia classification system with wearable electrocardiogramIEEE Access20186165291653810.1109/ACCESS.2018.2807700 MahmudTFattahSASaquibMDeepArrNet: an efficient deep CNN architecture for automatic arrhythmia detection and classification from denoised ECG beatsIEEE Access2020810478810480010.1109/ACCESS.2020.2998788 MoodyGBMarkRGThe impact of the MIT- BIH arrhythmia databaseIEEE Eng Med Biol2001203455010.1109/51.932724 YangFZhangXZhuYPDNet: a convolutional neural network has potential to be deployed on small intelligent devices for arrhythmia diagnosisComput Model EngSci2020125136538210.32604/cmes.2020.010798 KieferJWolfowitzJStochastic estimation of the maximum of a regression functionAnn Math Stat19522334624665024310.1214/aoms/11777293920049.36601 Jun TJ, Nguyen HM, Kang D, Kim D, Kim D, and Kim YH (2018) ECG arrhythmia classification using a 2-D convolutional neural network. Computer Vision and Pattern Recognition. pp 1–22. AddisonPSWavelet transforms and the ECG: a reviewPhysiolMeasur200510.1088/0967-3334/26/5/R01 ChazalPDO'DwyerMReillyRBAutomatic classification of heartbeats using ECG morphology and heartbeat interval featuresIEEE Trans Biomed Eng20045171196120610.1109/TBME.2004.827359 QiaoFA fast and accurate recognition of ECG signals based on ELM-LRF and BLSTM algorithmIEEE Access20208711897119810.1109/ACCESS.2020.2987930 A Geron (371_CR42) 2018 SS Xu (371_CR39) 2018; 14 PD Chazal (371_CR46) 2004; 51 371_CR34 371_CR32 JPRR Leite (371_CR16) 2018; 12 HC Nejad (371_CR4) 2014; 27 KJ Clerkin (371_CR2) 2020 TF Romdhane (371_CR49) 2020; 123 371_CR41 RJ Martis (371_CR12) 2012; 39 F Melgani (371_CR19) 2008; 12 X Fan (371_CR23) 2018; 22 UR Acharya (371_CR21) 2017; 89 Y Xia (371_CR27) 2018; 6 H Robbins (371_CR43) 1951; 22 X Zhai (371_CR31) 2018; 6 371_CR3 371_CR1 F Qiao (371_CR51) 2020; 8 Z Gao (371_CR6) 2017; 37 371_CR47 CA Steinberg (371_CR8) 1962; 9 T Mahmud (371_CR25) 2020; 8 371_CR48 Ö Yildirim (371_CR26) 2018; 96 B Dogan (371_CR18) 2012; 12 H Huang (371_CR7) 2014; 13 F Yang (371_CR35) 2020; 125 X Xu (371_CR52) 2020; 8 371_CR13 371_CR10 M Jangra (371_CR36) 2020; 38 RJ Martis (371_CR14) 2013; 45 Z Gao (371_CR5) 2019; 33 S Osowski (371_CR17) 2004; 3 S Banerjee (371_CR38) 2012; 45 RJ Martis (371_CR9) 2013; 8 F Andreotti (371_CR28) 2017; 44 371_CR29 YD Zhang (371_CR33) 2021 L Lu (371_CR15) 2016; 94 GB Moody (371_CR45) 2001; 20 371_CR24 B Ganguly (371_CR53) 2020; 2 SH El-Khafif (371_CR11) 2013; 2013 371_CR30 PS Addison (371_CR40) 2005 S Kiranyaz (371_CR20) 2016; 63 Y Xia (371_CR22) 2019; 7 A Chen (371_CR50) 2020; 193 J Bergstra (371_CR37) 2015; 8 J Kiefer (371_CR44) 1952; 23 |
| References_xml | – reference: MoodyGBMarkRGThe impact of the MIT- BIH arrhythmia databaseIEEE Eng Med Biol2001203455010.1109/51.932724 – reference: YangFZhangXZhuYPDNet: a convolutional neural network has potential to be deployed on small intelligent devices for arrhythmia diagnosisComput Model EngSci2020125136538210.32604/cmes.2020.010798 – reference: SteinbergCAAbrahamSCaceresCAPattern recognition in the clinical electrocardiogramIRE Trans Biomed Electron196291233010.1109/TBMEL.1962.4322946 – reference: Shaker AM, Tantawi M, Shedeed HA, Tolba MF (2021) Deep convolutional neural networks for ECG heartbeat classification using two-stage hierarchical method. In Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2020. AISI 2020. Advances in Intelligent Systems and Computing, vol 1261. Springer, Cham. https://doi.org/10.1007/978-3-030-58669-0_12 – reference: MahmudTFattahSASaquibMDeepArrNet: an efficient deep CNN architecture for automatic arrhythmia detection and classification from denoised ECG beatsIEEE Access2020810478810480010.1109/ACCESS.2020.2998788 – reference: BanerjeeSGuptaRMitraMDelineation of ECG characteristic features using multi-resolution wavelet analysis methodMeasurement201245347448710.1016/j.measurement.2011.10.025 – reference: ZhaiXTinCAutomated ECG classification using dual heartbeat coupling based on convolutional neural networkIEEE Access20186274652747210.1109/ACCESS.2018.2833841 – reference: AndreottiFCarrOPimentelMAFMahdiAVosMDComparing feature-based classifiers and convolutional neural networks to detect arrhythmia from short segments of ECGComputCardiol20174414 – reference: AddisonPSWavelet transforms and the ECG: a reviewPhysiolMeasur200510.1088/0967-3334/26/5/R01 – reference: Bergstra J, Bardenet R, Bengio Y, Kegl B (2011) Algorithms for hyper-parameter optimization. In: Proceedings of Conference on Advances in Neural Information Processing Systems. pp 1–9. https://github.com/maxpumperla/hyperas – reference: KieferJWolfowitzJStochastic estimation of the maximum of a regression functionAnn Math Stat19522334624665024310.1214/aoms/11777293920049.36601 – reference: GangulyBAutomated detection and classification of arrhythmia from ecg signals using feature induced long short-term memory networkIEEE SensLett2020235810.1109/LSENS.2020.3006756 – reference: ChenAMulti-information fusion neural networks for arrhythmia automatic detectionComput Methods Programs Biomed202019310547910.1016/j.cmpb.2020.105479 – reference: MelganiFBaziYClassification of electrocardiogram signals with support vector machine and particle swarm optimizationIEEE Trans InfTechnol Biomed200812566767710.1109/TITB.2008.923147 – reference: MartisRJAcharyaURMinLCECG beat classification using PCA, LDA, ICA and Discrete Wavelet TransformBiomed Signal Process Control20138543744810.1016/j.bspc.2013.01.005 – reference: KiranyazSInceTGabboujMReal-time patient-specific ECG classification by 1-D convolutional neural networksIEEE Trans Biomed Eng201663366467510.1109/TBME.2015.2468589 – reference: FanXYaoQCaiYMiaoFSunFLiYMultiscaled fusion of deep convolutional neural networks for screening atrial fibrillation from single lead short ECG recordingsIEEE J Biomed Health Inform201822674475310.1109/JBHI.2018.2858789 – reference: BergstraJKomerBEliasmithCYaminsDCoxDDHyperopt: a Python library for model selection and hyperparameter optimizationComputSciDiscov20158124 – reference: AcharyaUROhSLHagiwaraYTanJHAdamMGertychASanTRA deep convolutional neural network model to classify heartbeatsComputBiol Med20178938939610.1016/j.compbiomed.2017.08.022 – reference: XuSSMakMWCheungCCTowards end-to-end ECG classification with raw signal extraction and deep neural networksIEEE J Biomed Health Inform2018148111 – reference: DoganBKorürekMA New ECG beat clustering method based on kernelized fuzzy C- mean and hybrid ant colony optimization for continuous domainsAppl Soft Comput201212113442345110.1016/j.asoc.2012.07.007 – reference: XuXLiuHECG heartbeat classification using convolutional neural networksIEEE Access202088614861910.1109/ACCESS.2020.2964749 – reference: HuangHA new hierarchical method for inter-patient heartbeat classification using random projections and RR intervalsBiomed Eng Online201413112610.1186/1475-925X-13-90 – reference: NejadHCKhayatOAzadbakhBMohammadiMUsing feed forward neural network for electrocardiogram signal analysis in chaotic domainJ Intell Fuzzy Syst20142752289229610.3233/IFS-141192 – reference: World Health Organization (2020) Cardiovascular Disease. [Online]. Available via link http://www.who.int/cardiovascular_diseases/en/index.html – reference: GaoZAutomatic segmentation of coronary tree in CT angiography imagesInt J Adapt Control Signal Process20193312391247399555510.1002/acs.27621432.92051 – reference: RobbinsHMonroSA stochastic approximation methodAnn Math Stat19512234004074266810.1214/aoms/11777295860054.05901 – reference: XiaYZhangHXuLGaoZZhangHLiuHLiSAn automatic cardiac arrhythmia classification system with wearable electrocardiogramIEEE Access20186165291653810.1109/ACCESS.2018.2807700 – reference: ANSI/AAMI EC57 (1998) Testing and Reporting Performance Results of Cardiac Rhythm and ST Segment Measurement Algorithms (AAMI Recommended Practice/American National Standard), Order Code: EC57–293. http://www.aami.org – reference: ClerkinKJCOVID-19 and cardiovascular diseaseAHA Circ202010.1161/CIRCULATIONAHA.120.046941 – reference: GaoZRobust estimation of carotid artery wall motion using the elasticity-based state-space approachMed Image Anal20173712110.1016/j.media.2017.01.004 – reference: LeiteJPRRMorenoRLHeartbeat classification with low computational cost using Hjorth parametersIET Signal Proc201812443143810.1049/iet-spr.2017.0296 – reference: XiaYXieYA novel wearable electrocardiogram classification system using convolutional neural networks and active learningIEEE Access201977989800110.1109/ACCESS.2019.2890865 – reference: MartisRJAcharyaURMandanaKMRayAKChakrabortyCApplication of principal component analysis to ECG signals for automated diagnosis of cardiac healthExpert SystAppl20123914117921180010.1016/j.eswa.2012.04.072 – reference: El-KhafifSHEl-BrawanyMAArtificial neural network-based automated ECG signal classifierInt Scholar Res Notices201320131610.1155/2013/261917 – reference: Jun TJ, Nguyen HM, Kang D, Kim D, Kim D, and Kim YH (2018) ECG arrhythmia classification using a 2-D convolutional neural network. Computer Vision and Pattern Recognition. pp 1–22. – reference: RomdhaneTFElectrocardiogram heartbeat classification based on a deep convolutional neural network and focal lossComputBiol Med202012310386610.1016/j.compbiomed.2020.103866 – reference: ChazalPDO'DwyerMReillyRBAutomatic classification of heartbeats using ECG morphology and heartbeat interval featuresIEEE Trans Biomed Eng20045171196120610.1109/TBME.2004.827359 – reference: Simonyan K, Zisserman A (2015) Very deep convolutional networks for large- scale image recognition In: Proceedings of International Conference on Learning Representations. pp 1–14 – reference: Roy S, Kiral-Kornek I, Harrer S (2019) Chrononet: a deep recurrent neural network for abnormal EEG identification, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11526 LNAI, pp 47–56. https://doi.org/10.1007/978-3-030-21642-9_8.4 – reference: MartisRJAcharyaURLimCMSuriJSCharacterization of ECG beats from cardiac arrhythmia using discrete cosine transform in PCA frameworkKnowl-Based Syst201345768210.1016/j.knosys.2013.02.007 – reference: LuLYanJde SilvaCWFeature selection for ECG signal processing using improved genetic algorithm and empirical mode decompositionMeasurement20169437238110.1016/j.measurement.2016.07.043 – reference: JangraMDhullSKSinghKKECG arrhythmia classification using modified visual geometry group network (mVGGNet)J Intell Fuzzy Syst20203833151316510.3233/JIFS-191135 – reference: OsowskiSHoaiLTAnalysis of features for efficient ECG signal classification using neuro-fuzzy networkProc IEEE Int Joint Conf Neural Networks2004324432448 – reference: ZhangYDA five-layer deep convolutional neural network with stochastic pooling for chest CT-based COVID-19 diagnosisMach Vis Appl202110.1007/s00138-020-01128-8Springer Berlin Heidelberg – reference: Guangying Y, and Yue C (2010) The study of electrocardiograph based on radial basis function neural network. In: Proceedings of Third International Symposium on Intelligent Information Technology and Security Informatics IEEE, 2010, pp 143–145 – reference: QiaoFA fast and accurate recognition of ECG signals based on ELM-LRF and BLSTM algorithmIEEE Access20208711897119810.1109/ACCESS.2020.2987930 – reference: Emanet N (2009) ECG beat classification by using discrete wavelet transform and random forest algorithm. In: Fifth International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control, 2009 pp 1–4 – reference: Jangra M, Singh KK and Dhull SK (2017) Recent trends in arrhythmia beat detection: a review. In: Communication and Computing System. Proceedings of the International Conference on Communication and Computing Systems, ICCCS 2016, pp 177–184. https://doi.org/10.1201/9781315364094-33. – reference: El-Bouny L, Khalil M, Adib A (2020) ECG heartbeat classification based on multi-scale wavelet convolutional neural networks. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp 3212–3216 – reference: GeronAHands-on machine learning with Scikit-Learn & TensorFlow2018O'Reilly Media Inc.ISBN:978-93-5213-521-9 – reference: Chollet F (2017) Xception: deep learning with depthwise separable convolutions. In: Proceedings—30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, pp 1800–1807. https://doi.org/10.1109/CVPR.2017.195 – reference: YildirimÖA novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classificationComputBiol Med20189618920210.1016/j.compbiomed.2018.03.016 – volume: 96 start-page: 189 year: 2018 ident: 371_CR26 publication-title: ComputBiol Med doi: 10.1016/j.compbiomed.2018.03.016 – volume: 3 start-page: 2443 year: 2004 ident: 371_CR17 publication-title: Proc IEEE Int Joint Conf Neural Networks – volume: 8 start-page: 437 issue: 5 year: 2013 ident: 371_CR9 publication-title: Biomed Signal Process Control doi: 10.1016/j.bspc.2013.01.005 – volume: 12 start-page: 667 issue: 5 year: 2008 ident: 371_CR19 publication-title: IEEE Trans InfTechnol Biomed doi: 10.1109/TITB.2008.923147 – year: 2020 ident: 371_CR2 publication-title: AHA Circ doi: 10.1161/CIRCULATIONAHA.120.046941 – volume: 8 start-page: 1 year: 2015 ident: 371_CR37 publication-title: ComputSciDiscov – ident: 371_CR13 doi: 10.1109/IITSI.2010.85 – volume: 45 start-page: 76 year: 2013 ident: 371_CR14 publication-title: Knowl-Based Syst doi: 10.1016/j.knosys.2013.02.007 – volume: 2013 start-page: 1 year: 2013 ident: 371_CR11 publication-title: Int Scholar Res Notices doi: 10.1155/2013/261917 – ident: 371_CR29 doi: 10.1007/978-3-030-58669-0_12 – year: 2005 ident: 371_CR40 publication-title: PhysiolMeasur doi: 10.1088/0967-3334/26/5/R01 – volume: 14 start-page: 1 issue: 8 year: 2018 ident: 371_CR39 publication-title: IEEE J Biomed Health Inform – volume: 123 start-page: 103866 year: 2020 ident: 371_CR49 publication-title: ComputBiol Med doi: 10.1016/j.compbiomed.2020.103866 – volume: 33 start-page: 1239 year: 2019 ident: 371_CR5 publication-title: Int J Adapt Control Signal Process doi: 10.1002/acs.2762 – volume: 6 start-page: 27465 year: 2018 ident: 371_CR31 publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2833841 – ident: 371_CR10 doi: 10.1109/ICSCCW.2009.5379457 – ident: 371_CR47 – volume: 45 start-page: 474 issue: 3 year: 2012 ident: 371_CR38 publication-title: Measurement doi: 10.1016/j.measurement.2011.10.025 – volume: 89 start-page: 389 year: 2017 ident: 371_CR21 publication-title: ComputBiol Med doi: 10.1016/j.compbiomed.2017.08.022 – volume: 38 start-page: 3151 issue: 3 year: 2020 ident: 371_CR36 publication-title: J Intell Fuzzy Syst doi: 10.3233/JIFS-191135 – ident: 371_CR3 doi: 10.1201/9781315364094-33 – ident: 371_CR34 doi: 10.1109/CVPR.2017.195 – volume: 193 start-page: 105479 year: 2020 ident: 371_CR50 publication-title: Comput Methods Programs Biomed doi: 10.1016/j.cmpb.2020.105479 – volume: 9 start-page: 23 issue: 1 year: 1962 ident: 371_CR8 publication-title: IRE Trans Biomed Electron doi: 10.1109/TBMEL.1962.4322946 – volume: 6 start-page: 16529 year: 2018 ident: 371_CR27 publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2807700 – volume: 7 start-page: 7989 year: 2019 ident: 371_CR22 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2890865 – volume: 23 start-page: 462 issue: 3 year: 1952 ident: 371_CR44 publication-title: Ann Math Stat doi: 10.1214/aoms/1177729392 – volume: 8 start-page: 8614 year: 2020 ident: 371_CR52 publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2964749 – ident: 371_CR48 – ident: 371_CR24 doi: 10.1109/ICASSP40776.2020.9054749 – volume: 2 start-page: 5 issue: 3 year: 2020 ident: 371_CR53 publication-title: IEEE SensLett doi: 10.1109/LSENS.2020.3006756 – volume: 125 start-page: 365 issue: 1 year: 2020 ident: 371_CR35 publication-title: Comput Model EngSci doi: 10.32604/cmes.2020.010798 – volume: 44 start-page: 1 year: 2017 ident: 371_CR28 publication-title: ComputCardiol – volume: 27 start-page: 2289 issue: 5 year: 2014 ident: 371_CR4 publication-title: J Intell Fuzzy Syst doi: 10.3233/IFS-141192 – volume: 39 start-page: 11792 issue: 14 year: 2012 ident: 371_CR12 publication-title: Expert SystAppl doi: 10.1016/j.eswa.2012.04.072 – volume: 37 start-page: 1 year: 2017 ident: 371_CR6 publication-title: Med Image Anal doi: 10.1016/j.media.2017.01.004 – volume-title: Hands-on machine learning with Scikit-Learn & TensorFlow year: 2018 ident: 371_CR42 – volume: 13 start-page: 1 issue: 1 year: 2014 ident: 371_CR7 publication-title: Biomed Eng Online doi: 10.1186/1475-925X-13-90 – volume: 51 start-page: 1196 issue: 7 year: 2004 ident: 371_CR46 publication-title: IEEE Trans Biomed Eng doi: 10.1109/TBME.2004.827359 – ident: 371_CR41 – volume: 12 start-page: 431 issue: 4 year: 2018 ident: 371_CR16 publication-title: IET Signal Proc doi: 10.1049/iet-spr.2017.0296 – year: 2021 ident: 371_CR33 publication-title: Mach Vis Appl doi: 10.1007/s00138-020-01128-8 – volume: 8 start-page: 71189 year: 2020 ident: 371_CR51 publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2987930 – volume: 22 start-page: 744 issue: 6 year: 2018 ident: 371_CR23 publication-title: IEEE J Biomed Health Inform doi: 10.1109/JBHI.2018.2858789 – volume: 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| SubjectTerms | Algorithms Arrhythmia Artificial neural networks Cardiac arrhythmia Classification CNN Complexity Computational Intelligence Data Structures and Information Theory Deep learning Depthwise separable convolution ECG Engineering Feature maps Global optimization Original Original Article Wavelet transform |
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| Title | O-WCNN: an optimized integration of spatial and spectral feature map for arrhythmia classification |
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