Machine Learning-based ECG Classification using Wavelet Scattered Features
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Abstract
Cardiac abnormalities are one of the leading causes of mortality and morbidity among the population. Changes in the morphology and rhythm of the cardiac signals associated with cardiac abnormalities need to be identified and classified. Advances in artificial intelligence paves the way for precise classification. the preprocessed ECG signal segments undergo wavelet scattering to extract the low variance features with reduced dimension are rearranged and the key features are selected using Minimum Redundancy and Maximum Relevance (MRMR) feature selection algorithm chosen by comparatively analyzing different feature selection algorithms and the selected features are fed to the machine learning models. Classification of ECG signals is comparatively analyzed using different Machine Learning models such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision tree, and Artificial Neural Network (ANN) models with 10-fold cross-validation. the performance is improved by optimizing each model by tuning the hyperparameters. Among the twenty models, the cubic SVM model achieves the highest accuracy of 99.84 percent.
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[1]
S. J. K K and S. R.S, “Machine Learning-based ECG Classification using Wavelet Scattered Features”, AJSE, vol. 23, no. 2, pp. 168 - 176, Aug. 2024.
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References
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[28] Bruni, Vittoria, Maria Lucia Cardinali, and Domenico Vitulano. ,"An MDL-Based Wavelet Scattering Features Selection for Signal Classification," Axioms,vol. 11, no. 8, 2022, doi: 10.3390/axioms11080376.
[29] Mohonta, Shadhon Chandra, Mohammod Abdul Motin, and Dinesh Kant Kumar. ,"Electrocardiogram based arrhythmia classification using wavelet transform with deep learning model," Sensing and Bio-Sensing Research, vol.37, Aug. 2022, doi: 10.1016/j.sbsr.2022.100502.
[30] chandrasekar, Aditya, Dhanush D. Shekar, Abhishek C. Hiremath, and Krishnan Chemmangat. ,"Detection of arrhythmia from electrocardiogram signals using a novel gaussian assisted signal smoothing and pattern recognition," Biomedical Signal Processing and Control, vol. 73, no.6, Mar. 2022, doi: 10.1016/j.bspc.2021.103469.
[31] Normawati, Dwi, and Dewi Pramudi Ismi. ,"K-fold cross validation for selection of cardiovascular disease diagnosis features by applying rule-based datamining," Signal and Image Processing Letters, vol. 1, no. 2, pp. 62-72, Jul 2019, doi: 10.1016/j.bspc.2021.103469.
[32] Sabeenian, R. S.; Sree Janani, K. K., "Transfer Learning-Based Electrocardiogram Classification Using Wavelet Scattered Features," Biomedical and Biotechnology Research Journal (BBRJ), vol. 7, no. 1, pp. 52-59, Jan–Mar 2023, doi : 10.4103/bbrj.bbrj_341_22.
[2] MedlinePlus , "Heart Failure", Bethesda (MD): National Library of Medicine (US), Aug. 17, 2022, [online], Available: https://medlineplus.gov/heartfailure.html
[3] Tsunakawa H, Miyamoto N, Kawabata M, Mashima S. ,"Electrocardiogram in heart failure,"Nihon rinsho. Japanese Journal of Clinical Medicine, vol. 51, no.5, pp.1222-1232. May.1993, PMID: 8331790.
[4] Rahul, Jagdeep, and Lakhan Dev Sharma. ,"Artificial intelligence-based approach for atrial fibrillation detection using normalised and short-duration time-frequency ECG," Biomedical Signal Processing and Control, vol. 71 part B, Jan. 2022, doi:10.1016/j.bspc.2021.103270
[5] Ramkumar, M., R. Sarath Kumar, A. Manjunathan, M. Mathankumar, and Jenopaul Pauliah. ,"Auto-encoder and bidirectional long short-term memory based automated arrhythmia classification for ECG signal," Biomedical Signal Processing and Control, vol.77, Aug. 2022, doi:10.1016/j.bspc.2022.103826
[6] Li, Hongqiang, Zifeng Lin, Zhixuan An, Shasha Zuo, Wei Zhu, Zhen Zhang, Yuxin Mu, Lu Cao, and Juan Daniel Prades García. ,"Automatic electrocardiogram detection and classification using bidirectional long short-term memory network improved by Bayesian optimization," Biomedical Signal Processing and Control, vol. 73 , Mar. 2022, doi:10.1016/j.bspc.2021.103424.
[7] Nasimi, Fahimeh, Mohammad Reza Khayyambashi, and Naser Movahhedinia. ,"Redundancy cancellation of compressed measurements by QRS complex alignment," Plos one, vol. 17, no. 2, Feb. 2022,doi: 10.1371/journal.pone.0262219
[8] Alharbey, R. A., S. Alsubhi, K. Daqrouq, and A. Alkhateeb. ,"The continuous wavelet transform using for natural ECG signal arrhythmias detection by statistical parameters," Alexandria Engineering Journal, vol. 61, no. 12, pp. 9243-9248, 2022,
[9] Liu, Zhishuai, Guihua Yao, Qing Zhang, Junpu Zhang, and Xueying Zeng. ,"Wavelet scattering transform for ECG beat classification," Computational and Mathematical Methods in Medicine, vol. 2020, 11 pages, Oct. 2020, doi:10.1155/2020/3215681.
[10] Rahul, Jagdeep, and Lakhan Dev Sharma. ,"Automatic cardiac arrhythmia classification based on hybrid 1-D CNN and Bi-LSTM model," Biocybernetics and Biomedical Engineering, vol. 42, no. 1, pp.312-324, 2022.
[11] Han, Jingyu, Guangpeng Sun, Xinhai Song, Jing Zhao, Jin Zhang, and Yi Mao. ,"Detecting ECG abnormalities using an ensemble framework enhanced by Bayesian belief network," Biomedical Signal Processing and Control, vol. 72, part A, Feb. 2022, doi:10.1016/j.bspc.2021.103320.
[12] Liu, Jia, Chi Zhang, Yongjie Zhu, Tapani Ristaniemi, Tiina Parviainen, and Fengyu Cong. ,"Automated detection and localization system of myocardial infarction in single-beat ECG using Dual-Q TQWT and wavelet packet tensor decomposition," Computer methods and programs in biomedicine, vol. 184, Feb. 2020, doi: 10.1016/j.cmpb.2019.105120.
[13] Chandrasekar, Aditya, Dhanush D. Shekar, Abhishek C. Hiremath, and Krishnan Chemmangat. ,"Detection of arrhythmia from electrocardiogram signals using a novel gaussian assisted signal smoothing and pattern recognition," Biomedical Signal Processing and Control, vol. 73, Mar. 2022, doi: 10.1016/j.bspc.2021.103469.
[14] Mandal, Saurav, Pulak Mondal, and Anisha Halder Roy. ,"Detection of Ventricular Arrhythmia by using Heart rate variability signal and ECG beat image," Biomedical Signal Processing and Control, vol.68, Jul.2021, doi: 10.1016/j.bspc.2021.102692.
[15] Kim, Jin-Kook, Sunghoon Jung, Jinwon Park, and Sung Won Han. ,"Arrhythmia detection model using modified DenseNet for comprehensible Grad-CAM visualization," Biomedical Signal Processing and Control, vol. 73, Mar. 2022, doi:10.1016/j.bspc.2021.103408.
[16] Kim, Yun Kwan, Minji Lee, Hee Seok Song, and Seong-Whan Lee. ,"Automatic Cardiac Arrhythmia Classification Using Residual Network Combined with Long Short-term Memory," IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-17, 2022, doi: 10.1109/TIM.2022.3181276. 2022.
[17] Tao, Yanyun, Zuoyong Li, Chaochen Gu, Bin Jiang, and Yuzhen Zhang. ,"ECG-based expert-knowledge attention network to tachyarrhythmia recognition," Biomedical Signal Processing and Control,vol.76, Jul. 2022, doi: 10.1016/j.bspc.2022.103649.
[18] Sahoo S, Kanungo B, Behera S, Sabut S. ,"Multiresolution wavelet transform based feature extraction and ECG classification to detect cardiac abnormalities,", Measurement , vol. 108, pp. 55–66, Oct. 2017, doi: 10.1016/j.measurement.2017.05.022.
[19] Aa. Rahul, Jagdeep, Marpe Sora, Lakhan Dev Sharma, and Vijay Kumar Bohat. ,"An improved cardiac arrhythmia classification using an R.R. interval-based approach," Biocybernetics and Biomedical Engineering, vol. 41, no. 2, pp. 656-666, May. 2021, doi: 10.1016/j.bbe.2021.04.004.
[20] Pandey, Saroj Kumar, Rekh Ram Janghel, and Vyom Vani. ,"Patient specific machine learning models for ECG signal classification," Procedia Computer Science, vol. 167, pp.2181-2190,2020, doi: 10.1016/j.procs.2020.03.269.
[21] Wang, Jibin, Ping Wang, and Suping Wang. ,"Automated detection of atrial fibrillation in ECG signals based on wavelet packet transform and correlation function of random process," Biomedical Signal Processing and Control vol. 55, Jan. 2020, doi: 10.1016/j.bspc.2019.101662.
[22] Murawwat, Sadia, Hafiz M. Asif, Sana Ijaz, Muhammad Imran Malik, and Kaamran Raahemifar. ,"Denoising and classification of arrhythmia using MEMD and ANN," Alexandria Engineering Journal, vol. 61, no. 4 pp.2807-2823, Apr. 2022, doi: 10.1016/j.aej.2021.08.014.
[23] Kusuma, S., and K. R. Jothi. ,"ECG signals-based automated diagnosis of congestive heart failure using Deep CNN and LSTM architecture," Biocybernetics and Biomedical Engineering,vol. 42, no. 1,pp. 247-257, Jan-Mar.2022, doi: 10.1016/j.bbe.2022.02.003.
[24] Oppelt, Maximilian P., Maximilian Riehl, Felix P. Kemeth, and Jan Steffan. , 2020, "Combining scatter transform and deep neural networks for multilabel electrocardiogram signal classification," 2020 Computing in Cardiology, IEEE conference, Available: https://cinc.org/archives/2020/pdf/CinC2020-133.pdf
[25] Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220, Jun. 2000, doi: 10.1161/01.cir.101.23.e215
[26] Moody GB, Mark RG. The impact of the MIT-BIH Arrhythmia Database. IEEE Eng in Med and Biol, vol. 20, no.3, pp. 45-50, May-June 2001, doi: 10.1109/51.932724
[27] Baim DS, Colucci WS, Monrad ES, Smith HS, Wright RF, Lanoue A, Gauthier DF, Ransil BJ, Grossman W, Braunwald E. "Survival of patients with severe congestive heart failure treated with oral milrinon," J Am Coll Cardiol. vol. 7, no. 3, pp. 661-70, Mar. 1986, doi: 10.1016/s0735-1097(86)80478-8.
[28] Bruni, Vittoria, Maria Lucia Cardinali, and Domenico Vitulano. ,"An MDL-Based Wavelet Scattering Features Selection for Signal Classification," Axioms,vol. 11, no. 8, 2022, doi: 10.3390/axioms11080376.
[29] Mohonta, Shadhon Chandra, Mohammod Abdul Motin, and Dinesh Kant Kumar. ,"Electrocardiogram based arrhythmia classification using wavelet transform with deep learning model," Sensing and Bio-Sensing Research, vol.37, Aug. 2022, doi: 10.1016/j.sbsr.2022.100502.
[30] chandrasekar, Aditya, Dhanush D. Shekar, Abhishek C. Hiremath, and Krishnan Chemmangat. ,"Detection of arrhythmia from electrocardiogram signals using a novel gaussian assisted signal smoothing and pattern recognition," Biomedical Signal Processing and Control, vol. 73, no.6, Mar. 2022, doi: 10.1016/j.bspc.2021.103469.
[31] Normawati, Dwi, and Dewi Pramudi Ismi. ,"K-fold cross validation for selection of cardiovascular disease diagnosis features by applying rule-based datamining," Signal and Image Processing Letters, vol. 1, no. 2, pp. 62-72, Jul 2019, doi: 10.1016/j.bspc.2021.103469.
[32] Sabeenian, R. S.; Sree Janani, K. K., "Transfer Learning-Based Electrocardiogram Classification Using Wavelet Scattered Features," Biomedical and Biotechnology Research Journal (BBRJ), vol. 7, no. 1, pp. 52-59, Jan–Mar 2023, doi : 10.4103/bbrj.bbrj_341_22.