SpEC: A system for patient specific ECG beat classification using deep residual network

• Autorzy: Allam Jaya Prakash , Samantray Saunak , Ari Samit

Identyfikatory

DOI

10.1016/j.bbe.2020.08.001

• Języki publikacji: EN

Abstrakty

EN

Electrocardiogram (ECG) is a non-invasive technique used to detect various cardiac disorders. One of the major causes of cardiac arrest is an arrhythmia. Furthermore, ECG beat classification is essential to detect life-threatening cardiac arrhythmias. The major limitations of the traditional ECG beat classification systems are the requirement of an extensive training dataset to train the model and inconsistent performance for the detection of ventricular and supraventricular ectopic (V and S) beats. To overcome these limitations, a system denoted as SpEC is proposed in this work based on Stockwell transform (ST) and two-dimensional residual network (2D-ResNet) for improvement of ECG beat classification technique with a limited amount of training data. ST, which is used to represent the ECG signal into a time-frequency domain, provides frequency invariant amplitude response and dynamic resolution. The resultant ST images are applied as input to the proposed 2D-ResNet to classify five different types of ECG beats in a patient-specific way as recommended by the Association for the Advancement of Medical Instrumentation (AAMI). The proposed SpEC system achieved an overall accuracy (Acc) of 99.73%, sensitivity (Sen) = 98.84%, Specificity (Spe) = 99.50%, Positive predictivity (Ppr) = 98.20% on MIT-BIH arrhythmia database, and shows an overall Acc of 89.87% on real-time acquired ECG dataset with classification time of single ECG beat image = 0.2365 (s) in detecting of five arrhythmia classes. The proposed method shows better performance on both the database compared to the earlier reported state-of-art techniques.

Słowa kluczowe

EN

cardiac arrhythmia; convolutional neural network; electrocardiogram; residual network; S-transform

PL

arytmia serca; sieć neuronowa konwolucyjna; elektrokardiogram

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2020
• Tom: Vol. 40, no. 4
• Strony: 1446--1457
• Opis fizyczny: Bibliogr. 43 poz., rys., tab.

Twórcy

autor

Allam Jaya Prakash

• Department of Electronics and Communication Engineering, National Institute of Technology Rourkela, Odisha 769008, India

autor

Samantray Saunak

• Department of ETC, IIIT Bhubaneswar, Odisha 751003, India

autor

Ari Samit

• Department of Electronics and Communication Engineering, National Institute of Technology Rourkela, Odisha 769008, India

Bibliografia

• [1] Das MK, Ari S. Patient-specific ECG beat classification technique. Healthcare Technol Lett 2014;1(3):98–103.
• [2] Alvarado AS, Lakshminarayan C, Principe JC. Time-based compression and classification of heartbeats. IEEE Trans Biomed Eng 2012;59(6):1641–8.
• [3] Cardiovascular diseases (CVDS). [Online]. Available: https://www.who.int/en/news-room/fact-sheets/detail/ cardiovascular-diseases-(cvds).
• [4] Moody GB, Mark RG. The impact of the MIT-BIH arrhythmia database. IEEE Eng Med Biol Mag 2001;20(3):45–50.
• [5] Ye C, Vijaya Kumar BVK, Coimbra MT. Heartbeat classification using morphological and dynamic features of ECG signals. IEEE Trans Biomed Eng 2012;59(10):2930–41.
• [6] Thomas M, Das MK, Ari S. Automatic ecg arrhythmia classification using dual tree complex wavelet based features. AEU Int J Electron Commun 2015;69(4):715–21.
• [7] Özbay Y, Ceylan R, Karlik B. Integration of type-2 fuzzy clustering and wavelet transform in a neural network based ECG classifier. Expert Syst Appl 2011;38(1):1004–10.
• [8] zhu W, Chen X, Wang Y, Wang L. Arrhythmia recognition and classification using ECG morphology and segment feature analysis. IEEE/ACM Trans Comput Biol Bioinform 2019;16(1):131–8.
• [9] Khadra L, Al-Fahoum AS, Binajjaj S. A quantitative analysis approach for cardiac arrhythmia classification using higher order spectral techniques. IEEE Trans Biomed Eng 2005;52 (11):1840–5.
• [10] Andreao RV, Dorizzi B, Boudy J. Ecg signal analysis through hidden Markov models. IEEE Trans Biomed Eng 2006;53 (8):1541–9.
• [11] Inan OT, Giovangrandi L, Kovacs GTA. Robust neural-network-based classification of premature ventricular contractions using wavelet transform and timing interval features. IEEE Trans Biomed Eng 2006;53(12):2507–15.
• [12] Jiang W, Kong SG. Block-based neural networks for personalized ECG signal classification. IEEE Trans Neural Networks 2007;18(6):1750–61.
• [13] Wen C, Yeh M, Chang K. Ecg beat classification using greyart network. IET Signal Proc 2007;1(1):19–28.
• [14] Melgani F, Bazi Y. Evolutionary computation approach to ecg signal classification. Scitepress, 01. 2008. pp. 19–24.
• [15] Ince T, Kiranyaz S, Gabbouj M. A generic and robust system for automated patient-specific classification of ECG signals. IEEE Trans Biomed Eng 2009;56(5):1415–26.
• [16] Kampouraki A, Manis G, Nikou C. Heartbeat time series classification with support vector machines. IEEE Trans Inform Technol Biomed 2009;13(4):512–8.
• [17] Faezipour M, Saeed A, Bulusu SC, Nourani M, Minn H, Tamil L. A patient-adaptive profiling scheme for ECG beat classification. IEEE Trans Inform Technol Biomed 2010;14 (5):1153–65.
• [18] Banerjee S, Mitra M. Application of cross wavelet transform for ECG pattern analysis and classification. IEEE Trans Instrum Meas 2014;63(2):326–33.
• [19] Kiranyaz S, Ince T, Gabbouj M. Real-time patient-specific ECG classification by 1-d convolutional neural networks. IEEE Trans Biomed Eng 2016;63(3):664–75.
• [20] Oh SL, Ng EY, Tan RS, Acharya UR. Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats. Comput Biol Med 2018;102:278–87.
• [21] Yildirim Özal. A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification. Comput Biol Med 2018;96:189–202.
• [22] Jadhav SM, Nalbalwar SL, Ghatol AA. Ecg arrhythmia classification using modular neural network model. 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES). 2010. pp. 62–6.
• [23] Prakash AJ, Ari S. A system for automatic cardiac arrhythmia recognition using electrocardiogram signal. In: Pal K, Kraatz H-B, Khasnobish A, Bag S, Banerjee I, Kuruganti U, editors. Bioelectronics and Medical Devices, ser. Woodhead Publishing Series in Electronic and Optical Materials. Woodhead Publishing; 2019. p. 891–911 [chapter 35].
• [24] Coast DA, Stern RM, Cano GG, Briller SA. An approach to cardiac arrhythmia analysis using hidden Markov models. IEEE Trans Biomed Eng 1990;37(9):826–36.
• [25] de Chazal P, Reilly RB. A patient-adapting heartbeat classifier using ECG morphology and heartbeat interval features. IEEE Trans Biomed Eng 2006;53(12):2535–43.
• [26] Plawiak P, Acharya UR. Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals. Neural Comput Appl 2019.
• [27] Kandala RNVPS, Dhuli R, Plawiak P, Naik GR, Moeinzadeh H, Gargiulo GD, et al. Towards real-time heartbeat classification: Evaluation of nonlinear morphological features and voting method. Sensors 2019;19(23):5079.
• [28] Plawiak P, Acharya UR. Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals. Neural Comput Appl 2019.
• [29] Plawiak P. Novel genetic ensembles of classifiers applied to myocardium dysfunction recognition based on ECG signals. Swarm Evol Comput 2018;39:192–208.
• [30] Li D, Zhang J, Zhang Q, Wei X. Classification of ECG signals based on 1d convolution neural network. 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom). 2017. pp. 1–6.
• [31] Jun TJ, Nguyen HM, Kang D, Kim D, Kim D, Kim Y. ECG arrhythmia classification using a 2-d convolutional neural network; 2018 [Online]. Available: arXiv:1804.06812.
• [32] Xia Y, Xie Y. A novel wearable electrocardiogram classification system using convolutional neural networks and active learning. IEEE Access PP, 01; 2019.
• [33] Pan SJ, Yang Q. A survey on transfer learning. IEEE Trans Knowl Data Eng 2010;22(10):1345–59.
• [34] Huang H, Liu J, Zhu Q, Wang R, Hu G. A new hierarchical method for inter-patient heartbeat classification using random projections and RR intervals. Biol Med Eng OnLine 2014;13(1):90.
• [35] Stockwell RG, Mansinha L, Lowe RP. Localization of the complex spectrum: the s transform. IEEE Trans Signal Process 1996;44(4):998–1001.
• [36] Pan J, Tompkins WJ. A real-time QRS detection algorithm. IEEE Trans Biomed Eng BME 1985;32(3):230–6.
• [37] Huang J, Chen B, Yao B, He W. ecg arrhythmia classification using STFT-based spectrogram and convolutional neural network. IEEE Access 2019;7:92871–80.
• [38] He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June. 2016. pp. 770–8.
• [39] Xu SS, Mak M, Cheung C. Towards end-to-end ECG classification with raw signal extraction and deep neural networks. IEEE J Biomed Health Inform 2019;23(4):1574–84.
• [40] Xia Y, Xie Y. A novel wearable electrocardiogram classification system using convolutional neural networks and active learning. IEEE Access 2019;7:7989–8001.
• [41] Xu X, Liu H. Ecg heartbeat classification using convolutional neural networks. IEEE Access 2020;8:8614–9.
• [42] Llamedo M, Martinez JP. An automatic patient-adapted ECG heartbeat classifier allowing expert assistance. IEEE Trans Biomed Eng 2012;59(8):2312–20.
• [43] Plawiak P. Novel methodology of cardiac health recognition based on ECG signals and evolutionary-neural system. Expert Syst Appl 2018;92:334–49.