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Research Article

Arrhythmia detection in inter-patient ECG signals using entropy rate features and RR intervals with CNN architecture

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Received 05 Feb 2024, Accepted 27 Jun 2024, Published online: 17 Jul 2024

References

  • Acharya UR, Fujita H, Adam M, Oh SL, Hong TJ, Sudarshan VK, Koh JEW. 2016. Automated characterization of arrhythmias using nonlinear features from tachycardia ECG beats. 2016 IEEE international conference on systems, man, and cybernetics (SMC), p. 000533–000538. IEEE. doi: 10.1109/SMC.2016.7844294.
  • Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adam M, Gertych A, Tan RS. 2017. A deep convolutional neural network model to classify heartbeats. Comput Biol Med. 89:389–396. doi: 10.1016/j.compbiomed.2017.08.022.
  • Afonso VX, Tompkins WJ, Nguyen TQ, Luo S. 1999. ECG beat detection using filter banks. IEEE Trans Biomed Eng. 46(2):192–202. doi: 10.1109/10.740882.
  • Al Rahhal MM, Bazi Y, AlHichri H, Alajlan N, Melgani F, Yager RR. 2016. Deep learning approach for active classification of electrocardiogram signals. Inf Sci. 345:340–354. doi: 10.1016/j.ins.2016.01.082.
  • AlMahamdy M, Riley HB. 2014. Performance study of different denoising methods for ECG signals. Procedia Comput Sci. 37:325–332. doi: 10.1016/j.procs.2014.08.048.
  • ANSI-AAMI. 1998–2008. Testing and reporting performance results of cardiac rhythm and ST segment measurement algorithms. American National Standards Institute, Inc. (ANSI), Association for the Advancement of Medical Instrumentation (AAMI), ANSI/AAMI/ISO.
  • Cai J, Zhou G, Dong M, Hu X, Liu G, Ni W. 2021. Real-time arrhythmia classification algorithm using time-domain ECG feature based on FFNN and CNN. Math Probl Eng. 2021:6648432.
  • Chandrakar B, Yadav O, Chandra V. 2013. A survey of noise removal techniques for ECG signals. Int J Adv Res Comput Commun Eng. 2:1354–1357.
  • Chen S, Hua W, Li Z, Li J, Gao X. 2017. Heartbeat classification using projected and dynamic features of ECG signal. Biomed Signal Process Control. 31:165–173. doi: 10.1016/j.bspc.2016.07.010.
  • Cover TM, Thomas JA. 2006. Elements of information theory. 2nd ed. Hoboken (NJ): Wiley-Interscience.
  • Dang H, Sun M, Zhang G, Zhou X, Chang Q, Xu X. A novel deep convolutional neural network for arrhythmia classification. Proceedings of the 2019 International Conference on Advanced Mechatronic Systems (ICAMechS), Shiga, Japan, 26–28 August 2019. Piscataway (NJ): IEEE; 2019; p. 7–32. doi: 10.1109/ICAMechS.2019.8861645.
  • de Chazal P, O'Dwyer M, Reilly RB. 2004. Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Trans Biomed Eng. 51(7):1196–1206. doi: 10.1109/TBME.2004.827359.
  • Degirmenci M, Ozdemir MA, Izci E, Akan A. 2021. Arrhythmic heartbeat classification using 2D convolutional neural networks. IRBM. 43:422–433.
  • Elhaj FA, Salim N, Harris AR, Swee TT, Ahmed T. 2016. Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals. Comput Methods Programs Biomed. 127:52–63. doi: 10.1016/j.cmpb.2015.12.024.
  • Farag MM. 2023. A tiny matched filter-based CNN for inter-patient ECG classification and arrhythmia detection at the edge. Sensors. 23(3):1365. doi: 10.3390/s23031365.
  • Fujita H, Cimr D. 2019. Computer aided detection for fibrillations and flutters using deep convolutional neural network. Inf. Sci. 486:231–239. doi: 10.1016/j.ins.2019.02.065.
  • Fujita H, Cimr D. 2019. Decision support system for arrhythmia prediction using convolutional neural network structure without preprocessing. Appl Intell. 49(9):3383–3391. doi: 10.1007/s10489-019-01461-0.
  • Garcia G, Moreira G, Menotti D, Luz E. 2017. Inter-patient ECG heartbeat classification with temporal VCG optimized by PSO. Sci Rep. 7(1):10543. doi: 10.1038/s41598-017-09837-3.
  • Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE. 2000. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation. 101:e215–e220.
  • Gu X, Hu J, Zhang L, Ding J, Yan F. 2020. An improved method with high anti-interference ability for R peak detection in wearable devices. IRBM. 41(3):172–183. doi: 10.1016/j.irbm.2020.01.002.
  • Gupta V, Mittal M, Mittal V. 2020. Chaos theory: an emerging tool for arrhythmia detection. Sens Imaging. 21(1):22. doi: 10.1007/s11220-020-0272-9.
  • Gupta V, Mittal M. 2019. QRS complex detection using STFT, chaos analysis, and PCA in standard and real-time ECG datasets. J Inst Eng India Ser B. 100(5):489–497. doi: 10.1007/s40031-019-00398-9.
  • He J, Rong J, Sun L, Wang H, Zhang Y, Ma J. 2020. A framework for cardiac arrhythmia detection from IoT-based ECGs. World Wide Web. 23(5):2835–2850. doi: 10.1007/s11280-019-00776-9.
  • He J, Rong J, Sun L, Wang H, Zhang Y. 2020. An advanced two-step DNN-based framework for arrhythmia detection. Lect Notes Comput Sci. 12085:422–434.
  • Hsieh C-H, Li Y-S, Hwang B-J, Hsiao C-H. 2020. Detection of atrial fibrillation using 1D convolutional neural network. Sensors. 20(7):2136. doi: 10.3390/s20072136.
  • Jiang W, SG, Kong S, G. 2007. Block-based neural networks for personalized ECG signal classification. IEEE Trans Biomed Eng. 18(6):1750–1761.
  • Jung W-H, Lee S-G. 2017. An arrhythmia classification method in utilizing the weighted KNN and the fitness rule. IRBM. 38(3):138–148. doi: 10.1016/j.irbm.2017.04.002.
  • Kachuee M, Fazeli S, Sarrafzadeh M. 2018. ECG heartbeat classification: a deep transferable representation. 2018 IEEE International Conference on Healthcare Informatics (ICHI). IEEE; p. 443–444. doi: 10.1109/ICHI.2018.00092.
  • Kang H, Zhang X. 2019. The influence of parameter selection for Renyi phase permutation entropy on abnormal change detection. IEEE International Conference on Signal, Information and Data Processing (ICSIDP), p. 1–6.
  • Kutlu Y, Kuntalp D. 2012. Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients. Comput Methods Programs Biomed. 105(3):257–267. doi: 10.1016/j.cmpb.2011.10.002.
  • Lagerholm M, Peterson C, Braccini G, Edenbrandt L, Sörnmo L. 2000. Clustering ECG complexes using Hermite functions and self-organizing maps. IEEE Trans Biomed Eng. 47(7):838–848. doi: 10.1109/10.846677.
  • Li T, Zhou M. 2016. ECG classification using wavelet packet entropy and random forests. Entropy. 18(8):285. doi: 10.3390/e18080285.
  • Lin CC, Yang CM. 2014. Heartbeat classification using normalized RR intervals and morphological features. Math Probl Eng. 2014:1–11. doi: 10.1155/2014/712474.
  • Llamedo M, Martinez JP. 2011. Heartbeat classification using feature selection driven by database generalization criteria. IEEE Trans Biomed Eng. 58(3):616–625. doi: 10.1109/TBME.2010.2068048.
  • Luo K, Li J, Wang Z, Cuschieri A. 2017. Patient-specific deep architectural model for ECG classification. J Healthc Eng. 2017:4108720–4108713. (). doi: 10.1155/2017/4108720.
  • Luz EJS, Schwartz WR, Cámara-Chávez G, Menotti D. 2016. ECG-based heartbeat classification for arrhythmia detection: a survey. Comput Methods Programs Biomed. 127:144–164. doi: 10.1016/j.cmpb.2015.12.008.
  • Martis RJ, Acharya UR, Adeli H, Prasad H, Tan JH, Chua KC, Too CL, Yeo SWJ, Tong L. 2014. Computer aided diagnosis of atrial arrhythmia using dimensionality reduction methods on transform domain representation. Biomed Signal Process Control. 13(1):295–305. doi: 10.1016/j.bspc.2014.04.001.
  • Martis RJ, Acharya UR, Lim CM, Suri JS. 2013. Characterization of ECG beats from cardiac arrhythmia using discrete cosine transform in PCA framework. Knowl Based Syst. 45:76–82. doi: 10.1016/j.knosys.2013.02.007.
  • Martis RJ, Acharya UR, Prasad H, Chua CK, Lim CM, Suri JS. 2013. Application of higher order statistics for atrial arrhythmia classification. Biomed Signal Process Control. 8(6):888–900. doi: 10.1016/j.bspc.2013.08.008.
  • Moody GB, Mark RG. 2001. The impact of the MIT-BIH arrhythmia database. IEEE Eng Med Biol Mag. 20(3):45–50. doi: 10.1109/51.932724.
  • Oh SL, Ng EY, San Tan R, Acharya UR. 2018. Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats. Comput Biol Med. 102:278–287. doi: 10.1016/j.compbiomed.2018.06.002.
  • Oh SL, Ng EY, San Tan R, Acharya UR. 2019. Automated beat-wise arrhythmia diagnosis using modified U-net on extended electrocardiographic recordings with heterogeneous arrhythmia types. Comput Biol Med. 105:92–101. doi: 10.1016/j.compbiomed.2018.12.012.
  • Osowski S, Hoai LT, Markiewicz T. 2004. Support vector machine-based expert system for reliable heartbeat recognition. IEEE Trans Biomed Eng. 51(4):582–589. doi: 10.1109/TBME.2004.824138.
  • Pan J, Tompkins WJ. 1985. A real-time QRS detection algorithm. IEEE Trans Biomed Eng. 32(3):230–236. BMEdoi: 10.1109/TBME.1985.325532.
  • Pandey SK, Janghel RR. 2019. Automatic detection of arrhythmia from imbalanced ECG database using CNN model with SMOTE. Australas Phys Eng Sci Med. 42(4):1129–1139. doi: 10.1007/s13246-019-00815-9.
  • Pham B-T, Le PT, Tai T-C, Hsu Y-C, Li Y-H, Wang J-C. 2023. Electrocardiogram heartbeat classification for arrhythmias and myocardial infarction. Sensors. 23(6):2993. doi: 10.3390/s23062993.
  • Raj S, Ray KC. 2018. A personalized arrhythmia monitoring platform. Sci Rep. 8(1):11395. doi: 10.1038/s41598-018-29690-2.
  • Rekik S, Ellouze N. 2017. Enhanced and optimal algorithm for QRS detection. IRBM. 38(1):56–61. doi: 10.1016/j.irbm.2016.11.004.
  • Rodríguez J, Goñi A, Illarramendi A. 2005. Real-time classification of ECGs on a PDA. IEEE Trans Inf Technol Biomed. 9(1):23–34. doi: 10.1109/titb.2004.838369.
  • Sahoo S, Dash M, Behera S, Sabut S. 2020. Machine learning approach to detect cardiac arrhythmias in ECG signals: a survey. IRBM. 41(4):185–194. doi: 10.1016/j.irbm.2019.12.001.
  • Scarciglia A, Catrambone V, Bonanno C, Valenza G. 2021. Quantifying partition-based Kolmogorov-Sinai entropy on heart rate variability: a young vs. elderly study. 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), p. 5469–5472.
  • Sellami A, Hwang H. 2019. A robust deep convolutional neural network with batch-weighted loss for heartbeat classification. Expert Syst Appl. 122:75–84. doi: 10.1016/j.eswa.2018.12.037.
  • Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. 2017. Grad-CAM: visual Explanations from Deep Networks via Gradient-Based Localization. 2017 IEEE International Conference on Computer Vision (ICCV); p. 618–626. doi: 10.1109/ICCV.2017.74.
  • Shaker AM, Tantawi M, Shedeed HA, Tolba MF. 2020. Generalization of convolutional neural networks for ECG classification using generative adversarial networks. IEEE Access. 8:35592–35605. doi: 10.1109/ACCESS.2020.2974712.
  • Shannon CE. 1948. A mathematical theory of communication. Bell Syst Tech J. 27(3):379–423. doi: 10.1002/j.1538-7305.1948.tb01338.x.
  • Shi H, Wang H, Huang Y, Zhao L, Qin C, Liu C. 2019. A hierarchical method based on weighted extreme gradient boosting in ECG heartbeat classification. Comput Methods Programs Biomed. 171:1–10. doi: 10.1016/j.cmpb.2019.02.005.
  • Singh P, Shahnawazuddin S, Pradhan G. 2018. An efficient ECG denoising technique based on non-local means estimation and modified empirical mode decomposition. Circuits Syst Signal Process. 37(10):4527–4547. doi: 10.1007/s00034-018-0777-9.
  • Slonim M, Slonim T, Ovsyscher E. 1993. The use of simple fir filters for filtering of ECG signals and a new method for post-filter signal reconstruction. Proceedings of Computers in Cardiology Conference. doi: 10.1109/CIC.1993.378347.
  • Sun Q, Wang Q, Ji B, Wu W, Huang W, Wang C. 2020. The cardiodynamicsgram based early detection of myocardial ischemia using the Lempel-Ziv complexity. IEEE Access. 8:207894–207904. doi: 10.1109/ACCESS.2020.3038210.
  • Übeyli ED. 2009. Combining recurrent neural networks with eigenvector methods for classification of ECG beats. Digit Signal Process. 19(2):320–329. doi: 10.1016/j.dsp.2008.09.002.
  • Wang G, Chen M, Ding Z, Li J, Yang H, Zhang P. 2021. Inter-patient ECG arrhythmia heartbeat classification based on unsupervised domain adaptation. Neurocomputing. 454:339–349. doi: 10.1016/j.neucom.2021.04.104.
  • Wang T, Lu C, Sun Y, Yang M, Liu C, Ou C. 2021. Automatic ECG classification using continuous wavelet transform and convolutional neural network. Entropy. 23(1):119. doi: 10.3390/e23010119.
  • Wang T, Lu CH, M, Yang M, Hong F, Liu C. 2020. A hybrid method for heartbeat classification via convolutional neural networks, multilayer perceptrons and focal loss. PeerJ Comput Sci. 6:e324. doi: 10.7717/peerj-cs.324.
  • West BJ, Zhang R, Sanders AW, Miniyar S, Zuckerman JH, Levine BD. 1999. Fractal. Fluctuations in cardiac time series. Physica A. 270(3–4):552–566. doi: 10.1016/s0378-4371(99)00175-2.
  • World Health Organization. Cardiovascular diseases World Health Organization. Geneva: WHO; 2021.
  • Xu SS, Mak MW, Cheung CC. 2018. Towards end-to-end ECG classification with raw signal extraction and deep neural networks. IEEE J Biomed Health Inform. 23(4):1574–1584. doi: 10.1109/JBHI.2018.2871510.
  • Ye C, Kumar BV, Coimbra MT. 2012. Heartbeat classification using morphological and dynamic features of ECG signals. IEEE Trans Biomed Eng. 59(10):2930–2941. doi: 10.1109/TBME.2012.2213253.
  • Yildirim Ö, Baloglu UB, Tan RS, Ciaccio EJ, Acharya UR. 2019. A new approach for arrhythmia classification using deep coded features and LSTM networks. Comput. Methods Programs Biomed. 176:121–133. doi: 10.1016/j.cmpb.2019.05.004.
  • Yıldırım Ö, Pławiak P, Tan R-S, Acharya UR. 2018. Arrhythmia detection using deep convolutional neural network with long duration ECG signals. Comput Biol Med. 102:411–420. doi: 10.1016/j.compbiomed.2018.09.009.
  • Yildirim Ö. 2018. A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification. Comput. Biol. Med. 96:189–202. doi: 10.1016/j.compbiomed.2018.03.016.
  • Zahid MU, Kiranyaz S, Gabbouj M. 2022. Global ECG classification by self-operational neural networks with feature injection. IEEE Trans Biomed Eng. 70(1):205–215. doi: 10.1109/TBME.2022.3187874.
  • Zhai X, Tin C. 2018. Automated ECG classification using dual heartbeat coupling based on convolutional neural network. IEEE Access. 6:27465–27472. doi: 10.1109/ACCESS.2018.2833841.
  • Zhang J, Liu A, Liang D, Chen X, Gao M. 2021. Interpatient ECG heartbeat classification with an adversarial convolutional neural network. J Healthc Eng. 2021:9946596.
  • Zhang Z, Dong J, Luo X, Choi KS, Wu X. 2014. Heartbeat classification using disease-specific feature selection. Comput Biol Med. 46:79–89. doi: 10.1016/j.compbiomed.2013.11.019.
  • Zubair M, Yoon C. 2022. Cost-Sensitive Learning for Anomaly Detection in Imbalanced ECG Data Using Convolutional Neural Networks. Sensors. 22(11):4075. doi: 10.3390/s22114075.

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