[1] Zoghbi W.A., Duncan T., Antman E. et al. Sustainable development goals and the future of cardiovascular health: A statement from the global cardiovascular disease taskforce. J. Am. Heart Assoc., 3, e000504:1-2(2014).

[2] Acharya U.R., Oh S.L., Hagiwara Y. et al. A deep convolutional neural network model to classify heartbeats. Comput. Biol. Med., 89, 389-396(2017).

[3] Homaeinezhad M.R., Atyabi S.A., Tavakkoli E., Toosi H.N., Ghaffari A., Ebrahimpour R.. ECG arrhythmia recognition via a neuro-SVM-KNN hybrid classifier with virtual QRS image-based geometrical features. Expert Syst. Appl., 39, 2047-2058(2012).

[4] Mert A., Kilic N., Akan A.. ECG signal classification using ensemble decision tree. J. Trends Dev. Mach. Assoc. Technol., 16, 179-182(2012).

[5] Charfi F., Kraiem A.. Comparative study of ECG classification performance using decision tree algorithms. Int. J. E-Health Med. C., 3, 102-120(2012).

[6] Saini I., Singh D., Khosla A.. QRS detection using K-nearest neighbor algorithm (KNN) and evaluation on standard ECG databases. J. Adv. Res., 4, 331-344(2013).

[7] I. Saini, D. Singh, A. Khosla, Delineation of ECG wave components using Knearest neighb (KNN) algithm: ECG wave delineation using KNN, in: Proc. of 10th Intl. Conf. on Infmation Technology: New Generations, Las Vegas, USA, 2013, pp. 712–717.

[8] N. Kohli, N.K. Verma, Arrhythmia classification using SVM with ed features, Int. J. Eng. Sci. Technol. 3 (8) (2011) 122–131.

[9] Celin S., Vasanth K.. ECG signal classification using various machine learning techniques. J. Med. Syst., 42, 241:1-11(2018).

[10] Kumar R.G., Kumaraswamy Y.S.. Investigating cardiac arrhythmia in ECG using random forest classification. Int. J. Comput. Appl., 37, 31-34(2012).

[11] Razi A.P., Einalou Z., Manthouri M.. Sleep apnea classification using random forest via ECG. Sleep Vigil., 5, 141-146(2021).

[12] Pandey S.K., Janghel R.R.. Automatic arrhythmia recognition from electrocardiogram signals using different feature methods with long short-term memory network model. Signal Image Video P., 14, 1255-1263(2020).

[13] Tutuko B., Nurmaini S., Tondas A.E. et al. AFibNet: An implementation of atrial fibrillation detection with convolutional neural network. BMC Med. Inform. Decis., 21, 216:1-17(2021).

[14] E. Izci, M.A. Ozdemir, M. Degirmenci, A. Akan, Cardiac arrhythmia detection from 2D ECG images by using deep learning technique, in: Proc. of Medical Technologies Congress, Izmir, Turkey, 2019, pp. 1–4.

[15] Singh S., Pandey S.K., Pawar U., Janghel R.R.. Classification of ECG arrhythmia using recurrent neural networks. Procedia Comput. Sci., 132, 1290-1297(2018).

[16] Zhang J., Liu A.-P., Gao M., Chen X., Zhang X., Chen X.. ECG-based multi-class arrhythmia detection using spatio-temporal attention-based convolutional recurrent neural network. Artif. Intell. Med., 106, 101856:1-9(2020).

[17] M. Deshmane, S. Madhe, ECG based biometric human identification using convolutional neural wk in smart health applications, in: Proc. of 4th Intl. Conf. on Computing Communication Control Automation, Pune, India, 2018, pp. 1–6.

[18] A. Batra, V. Jawa, Classification of arrhythmia using conjunction of machine learning algithms ECG diagnostic criteria, Int. J. Biology Biomedicine 1 (2016) 1–7.

[19] N. Singh, P. Singh, Cardiac arrhythmia classification using machine learning techniques, in: K. Ray, S.N. Sharan, S. Rawat, S.K. Jain, S. Srivastava, A. Byopadhyay (Eds.), Engineering Vibration, Communication Infmation Processing, Springer, Singape, 2019, pp. 469–480.

[20] A. Gupta, A. Banerjee, D. Babaria, K. Lotlikar, H. Raut, Prediction classification of cardiac arrhythmia, in: S. Shakya, V.E. Balas, S. Kamolphiwong, K.L. Du (Eds.), Sentimental Analysis Deep Learning, Springer, Singape, 2022, pp. 527–538.

[21] Sakib S., Fouda M.M., Fadlullah Z.M., Nasser N., Alasmary W.. A proof-of-concept of ultra-edge smart IoT sensor: A continuous and lightweight arrhythmia monitoring approach. IEEE Access, 9, 26093-26106(2021).

[22] Hiriyannaiah S., Siddesh G.M., Kiran M.H.M., Srinivasa K.G.. A comparative study and analysis of LSTM deep neural networks for heartbeats classification. Health Technol.-Ger., 11, 663-671(2021).

[23] Shin S., Kang M.-G., Zhang G.-J., Jung J., Kim Y.T.. Lightweight ensemble network for detecting heart disease using ECG signals. Appl. Sci., 12, 3291:1-18(2022).

[24] Hammad M., Iliyasu A.M., Subasi A., Ho E.S.L., Abd El-Latif A.A.. A multitier deep learning model for arrhythmia detection. IEEE T. Instrum. Meas., 70, 2502809:1-9(2020).

[25] Hammad M., Meshoul S., Dziwiński P., Pławiak P., Elgendy I.A.. Efficient lightweight multimodel deep fusion based on ECG for arrhythmia classification. Sensors, 22, 9347:1-14(2022).

[26] Wang D., Si Y.-J., Yang W.-Y., Zhang G., Li J.. A novel electrocardiogram biometric identification method based on temporal-frequency autoencoding. Electronics, 8, 667:1-24(2019).

[27] Hammad M., Chelloug S.A., Alkanhel R. et al. Automated detection of myocardial infarction and heart conduction disorders based on feature selection and a deep learning model. Sensors, 22, 6503:1-14(2022).

[28] M.N. Dar, M.U. Akram, A. Usman, S.A. Khan, ECG biometric identification f general population using multiresolution analysis of DWT based features, in: Proc. of 2nd Intl. Conf. on Infmation Security Cyber Fensics, Cape Town, South Africa, 2015, pp. 5–10.

[29] D. Bratton, J. Kennedy, Defining a stard f particle swarm optimization, in: Proc. of IEEE Swarm Intelligence Symposium, Honolulu, USA, 2007, pp. 120–127.

[30] Bharti R., Khamparia A., Shabaz M., Dhiman G., Pande S., Singh P.. Prediction of heart disease using a combination of machine learning and deep learning. Comput. Intel. Neurosc., 2021, 8387680:1-11(2021).

[31] Kumari M., Ahlawat P.. DCPM: An effective and robust approach for diabetes classification and prediction. Int. J. Inf. Technol., 13, 1079-1088(2021).

[32] Biswas P., Samanta T.. Anomaly detection using ensemble random forest in wireless sensor network. Int. J. Inf. Technol., 13, 2043-2052(2021).

[33] Atal D.K., Singh M.. Arrhythmia classification with ECG signals based on the optimization-enabled deep convolutional neural network. Comput. Meth. Prog. Bio., 196, 105607:1-19(2020).

[34] Zhang J., Liu A.-P., Liang D., Chen X., Gao M.. Interpatient ECG heartbeat classification with an adversarial convolutional neural network. J. Healthc. Eng., 2021, 9946596:1-11(2021).

[35] Jung W.H., Lee S.G.. An arrhythmia classification method in utilizing the weighted KNN and the fitness rule. IRBM, 38, 138-148(2017).

[36] Wu M.-Z., Lu Y.-D., Yang W.-L., Wong S.Y.. A study on arrhythmia via ECG signal classification using the convolutional neural network. Front. Comput. Neurosc., 14, 564015:1-10(2021).

[37] Pandey S.K., Janghel R.R., Vani V.. Patient specific machine learning models for ECG signal classification. Procedia Comput. Sci., 167, 2181-2190(2020).

[38] Sharma M., Tan R.-S., Acharya U.R.. Automated heartbeat classification and detection of arrhythmia using optimal orthogonal wavelet filters. Inform. Med. Unlocked, 16, 100221:1-12(2019).

[39] Mondéjar-Guerra V., Novo J., Rouco J., Penedo M.G., Ortega M.. Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers. Biomed. Signal Proces., 47, 41-48(2019).

[40] Farag M.M.. A tiny matched filter-based CNN for inter-patient ECG classification and arrhythmia detection at the edge. Sensors, 23, 1365:1-23(2023).

[41] Wang T., Lu C.-H., Ju W., Liu C.. Imbalanced heartbeat classification using EasyEnsemble technique and global heartbeat information. Biomed. Signal Proces., 71, 103105:1-8(2022).

[42] Jin Y.-R., Liu J.-L., Liu Y.-Q. et al. A novel interpretable method based on dual-level attentional deep neural network for actual multilabel arrhythmia detection. IEEE T. Instrum. Meas., 71, 2500311:1-11(2021).

[43] M. Zubair, S. Woo, S. Lim, D. Kim, Deep representation learning with sample generation augmented attention module f imbalanced ECG classification, IEEE J. Biomed. Health (Oct. 2023), doi: 10.1109JBHI.2023.3325540.

[44] Xia Y., Xiong Y.-Q., Wang K.-Q.. A transformer model blended with CNN and denoising autoencoder for inter-patient ECG arrhythmia classification. Biomed. Signal Proces., 86, 105271:1-15(2023).