[1] | W.A. Zoghbi, T. Duncan, E. Antman, et al. Sustainable development goals and the future of cardiovascular health: a statement from the global cardiovascular disease taskforce. J. Am. Heart Assoc., 3 (5) (2014), pp. 1-2. e000504. |
[2] | U.R. Acharya, S.L. Oh, Y. Hagiwara, et al. A deep convolutional neural network model to classify heartbeats. Comput. Biol. Med., 89 (2017), pp. 389-396. View articleGoogle Scholar. |
[3] | M.R. Homaeinezhad, S.A. Atyabi, E. Tavakkoli, H.N. Toosi, A. Ghaffari, R. Ebrahimpour. ECG arrhythmia recognition via a neuro-SVM-KNN hybrid classifier with virtual QRS image-based geometrical features. Expert Syst. Appl., 39 (2) (2012), pp. 2047-2058. View articleGoogle Scholar. |
[4] | A. Mert, N. Kilic, A. Akan. ECG signal classification using ensemble decision tree. J. Trends Dev. Mach. Assoc. Technol., 16 (1) (2012), pp. 179-182. |
[5] | F. Charfi, A. Kraiem. Comparative study of ECG classification performance using decision tree algorithms. Int. J. E-Health Med. C., 3 (4) (2012), pp. 102-120. |
[6] | I. Saini, D. Singh, A. Khosla. QRS detection using K-nearest neighbor algorithm (KNN) andevaluation on standard ECG databases. J. Adv. Res., 4 (4) (2013), pp. 331-344. View articleGoogle Scholar. |
[7] | I. Saini, D. Singh, A. Khosla. Delineation of ECG wave components using K-nearest neighbor (KNN) algorithm: ECG wave delineation using KNN. Proc. of 10th Intl. Conf. on Information Technology: New Generations, Las Vegas, USA (2013), pp. 712-717. |
[8] | N. Kohli, N.K. Verma. Arrhythmia classification using SVM with selected features. Int. J. Eng. Sci. Technol., 3 (8) (2011), pp. 122-131. |
[9] | S. Celin, K. Vasanth. ECG signal classification using various machine learning techniques. J. Med. Syst., 42 (12) (2018), pp. 241:1-11. |
[10] | R.G. Kumar, Y.S. Kumaraswamy. Investigating cardiac arrhythmia in ECG using random forest classification. Int. J. Comput. Appl., 37 (4) (2012), pp. 31-34. |
[11] | A.P. Razi, Z. Einalou, M. Manthouri. Sleep apnea classification using random forest via ECG. Sleep Vigil., 5 (1) (2021), pp. 141-146. |
[12] | S.K. Pandey, R.R. Janghel. Automatic arrhythmia recognition from electrocardiogram signals using different feature methods with long short-term memory network model. Signal Image Video P., 14 (6) (2020), pp. 1255-1263. |
[13] | B. Tutuko, S. Nurmaini, A.E. Tondas, et al. AFibNet: an implementation of atrial fibrillation detection with convolutional neural network. BMC Med. Inform. Decis., 21 (1) (2021), pp. 26:1-17. |
[14] | E. Izci, M.A. Ozdemir, M. Degirmenci, A. Akan. Cardiac arrhythmia detection from 2D ECG images by using deep learning technique. Proc. of Medical Technologies Congress, Izmir, Turkey (2019), pp. 1-4. |
[15] | S. Singh, S.K. Pandey, U. Pawar, R.R. Janghel. Classification of ECG arrhythmia using recurrent neural networks. Procedia Comput. Sci., 132 (2018), pp. 1290-1297. View articleGoogle Scholar. |
[16] | J. Zhang, A.-P. Liu, M. Gao, X. Chen, X. Zhang, X. Chen. ECG-based multi-class arrhythmia detection using spatio-temporal attention-based convolutional recurrent neural network. Artif. Intell. Med., 106 (2020), pp. 1-9. 101856. |
[17] | M. Deshmane, S. Madhe. ECG based biometric human identi?cation using convolutional neural network in smart health applications. Proc. of 4th Intl. Conf. on Computing Communication Control and Automation, Pune, India (2018), pp. 1-6. |
[18] | A. Batra, V. Jawa. Classi?cation of arrhythmia using conjunction of machine learning algorithms and ECG diagnostic criteria. Int. J. Biology and Biomedicine, 1 (2016), pp. 1-7. |
[19] | N. Singh, P. Singh. Cardiac arrhythmia classi?cation using machine learning techniques. K. Ray, S.N. Sharan, S. Rawat, S.K. Jain, S. Srivastava, A. Bandyopadhyay (Eds.), Engineering Vibration, Communication and Information Processing, Springer, Singapore (2019), pp. 469-480. |
[20] | A. Gupta, A. Banerjee, D. Babaria, K. Lotlikar, H. Raut. Prediction and classi?cation of cardiac arrhythmia. S. Shakya, V.E. Balas, S. Kamolphiwong, K.-L. Du (Eds.), Sentimental Analysis and Deep Learning, Springer, Singapore (2022), pp. 527-538. |
[21] | S. Sakib, M.M. Fouda, Z.M. Fadlullah, N. Nasser, W. Alasmary. A proof-of-concept of ultra-edge smart IoT sensor: a continuous and lightweight arrhythmia monitoring approach. IEEE Access, 9 (2021), pp. 26093-26106. |
[22] | S. Hiriyannaiah, G.M. Siddesh, M.H.M. Kiran, K.G. Srinivasa. A comparative study and analysis of LSTM deep neural networks for heartbeats classi?cation. Health Technol.-Ger., 11 (3) (2021), pp. 663-671. |
[23] | S. Shin, M.-G. Kang, G.-J. Zhang, J. Jung, Y.T. Kim. Lightweight ensemble network for detecting heart disease using ECG signals. Appl. Sci., 12 (7) (2022), pp. 1-18. 3291. |
[24] | M. Hammad, A.M. Iliyasu, A. Subasi, E.S.L. Ho, A.A. Abd El-Latif. A multitier deep learning model for arrhythmia detection. IEEE T. Instrum. Meas., 70 (2020), pp. 1-9. 2502809. |
[25] | M. Hammad, S. Meshoul, P. Dziwiński, P. P?awiak, I.A. Elgendy. Efficient lightweight multimodel deep fusion based on ECG for arrhythmia classification. Sensors, 22 (23) (2022), pp. 1-14. 9347. |
[26] | D. Wang, Y.-J. Si, W.-Y. Yang, G. Zhang, J. Li. A novel electrocardiogram biometric identi?cation method based on temporal-frequency autoencoding. Electronics, 8 (6) (2019), pp. 1-24. 667. |
[27] | M. Hammad, S.A. Chelloug, R. Alkanhel, et al. Automated detection of myocardial infarction and heart conduction disorders based on feature selection and a deep learning model. Sensors, 22 (17) (2022), pp. 1-14. 6503. |
[28] | M.N. Dar, M.U. Akram, A. Usman, S.A. Khan. ECG biometric identi?cation for general population using multiresolution analysis of DWT based features. Proc. of 2nd Intl. Conf. on Information Security and Cyber Forensics (2015), pp. 5-10. Cape Town, South Africa. |
[29] | D. Bratton, J. Kennedy. De?ning a standard for particle swarm optimization. Proc. of IEEE Swarm Intelligence Symposium (2007), pp. 120-127. Honolulu, USA. |
[30] | R. Bharti, A. Khamparia, M. Shabaz, G. Dhiman, S. Pande, P. Singh. Prediction of heart disease using a combination of machine learning and deep learning. Comput. Intel. Neurosc., 2021 (2021), pp. 1-11. 8387680. |
[31] | M. Kumari, P. Ahlawat. DCPM: an effective and robust approach for diabetes classi?cation and prediction. Int. J. Inf. Technol., 13 (3) (2021), pp. 1079-1088. |
[32] | P. Biswas, T. Samanta. Anomaly detection using ensemble random forest in wireless sensor network. Int. J. Inf. Technol., 13 (5) (2021), pp. 2043-2052. |
[33] | D.K. Atal, M. Singh. Arrhythmia classification with ECG signals based on the optimization-enabled deep convolutional neural network. Comput. Meth. Prog. Bio., 196 (2020), pp. 1-19. 105607. |
[34] | J. Zhang, A.-P. Liu, D. Liang, X. Chen, M. Gao. Interpatient ECG heartbeat classification with an adversarial convolutional neural network. J. Healthc. Eng., 2021 (2021), pp. 1-11. 9946596. |
[35] | W.H. Jung, S.G. Lee. An arrhythmia classification method in utilizing the weighted KNN and the fitness rule. IRBM, 38 (3) (2017), pp. 138-148. View articleGoogle Scholar. |
[36] | M.-Z. Wu, Y.-D. Lu, W.-L. Yang, S.Y. Wong. A study on arrhythmia via ECG signal classification using the convolutional neural network. Front. Comput. Neurosc., 14 (2021), pp. 1-10. 564015. |
[37] | S.K. Pandey, R.R. Janghel, V. Vani. Patient specific machine learning models for ECG signal classification. Procedia Comput. Sci., 167 (2020), pp. 2181-2190. View articleGoogle Scholar. |
[38] | M. Sharma, R.-S. Tan, U.R. Acharya. Automated heartbeat classification and detection of arrhythmia using optimal orthogonal wavelet filters. Inform. Med. Unlocked, 16 (2019), pp. 1-12. 100221. |
[39] | V. Mondéjar-Guerra, J. Novo, J. Rouco, M.G. Penedo, M. Ortega. Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers. Biomed. Signal Proces., 47 (2019), pp. 41-48. View articleGoogle Scholar. |
[40] | M.M. Farag. A tiny matched filter-based CNN for inter-patient ECG classification and arrhythmia detection at the edge. Sensors, 23 (3) (2023), pp. 1-23. 1365. |
[41] | T. Wang, C.-H. Lu, W. Ju, C. Liu. Imbalanced heartbeat classification using EasyEnsemble technique and global heartbeat information. Biomed. Signal Proces., 71 (2022), pp. 1-8. 103105. View articleGoogle Scholar. |
[42] | Y.-R. Jin, J.-L. Liu, Y.-Q. Liu, et al. A novel interpretable method based on dual-level attentional deep neural network for actual multilabel arrhythmia detection. IEEE T. Instrum. Meas., 71 (2021), pp. 1-11. 2500311. |
[43] | M. Zubair, S. Woo, S. Lim, D. Kim. Deep representation learning with sample generation and augmented attention module for imbalanced ECG classification. IEEE J. Biomed. Health (2023), 10.1109/JBHI.2023.3325540. |
[44] | Y. Xia, Y.-Q. Xiong, K.-Q. Wang. A transformer model blended with CNN and denoising autoencoder for inter-patient ECG arrhythmia classification. Biomed. Signal Proces., 86 (Part C) (2023), pp. 1-15. 105271. |