[1] World Health Organization, Cardiovascular Diseases (cvds), World Health Organization,

Geneva, Switzerland, January 2021, http://www.who.int/mediacentre/factsheets/fs317/en/.

[2] Pławiak, P., Novel methodology of cardiac health recognition based on ECG signals and

evolutionary-neural system. Expert Syst. Appl. 92, 334-349, 2018.

[3] Luz, E.J.D.S., Schwartz,W.R., Cámara-Chávez, G., Menotti D., ECG-based heartbeat

classification for arrhythmia detection: A survey. Comput. Methods Programs Biomed., 127,

144-164, 2016.

[4] Lippincott Williams & Wilkins, ECG Interpretation Made Incredibly Easy. Wolters Kluwer

Health, 6th edition, 2018.

[5] Shirley A. Jones, ECG Mastery: Improving Your ECG Interpretation Skills. F.A. Davis

Company, 2nd edition, 2019.

[6] Krittanawong, et al., Machine learning prediction in cardiovascular diseases: a meta-analysis.

Sci. Rep., 10,16057, 2020.

[7] Taye G. T., Hwang H. J., Lim K. M., Application of convolutional neural network for predicting

the occurrence of ventricular tachyarrhythmia using heart rate variability features. Sci. Rep.

10,6769, 2020.

[8] Yingthawornsuk T., Temsang P., Cardiac arrhythmia classification using Hjorth descriptors.

Advances in Intelligent Systems and Computing, 807, 2019.

[9] Faust O., Lei N., Chew E., Ciaccio E. J., Acharya U. R. , A smart service platform for cost

efficient cardiac health monitoring. Int. J. Environ. Res. Public Health 17,6313, 2020.

[10] Ping Y., Chen C., automatic detection of atrial fibrillation based on CNN-LSTM and shortcut

connections. Healthcare 8,139, 2020.

[11] Sidrah L., Dashtipour K., Detection of atrial fibrillation using a machine learning approach.

Information. 11, 549, 2020.

[12] Nurmaini S., Tondas A. E., Robust detection of atrial fibrillation from short-term

electrocardiogram using convolutional neural networks. Future Gener. Comput. Syst. 113, 304–

317, 2020.

[13] Sharma R. R., Kumar A., Pachori R. B., Acharya U. R, Accurate automated detection of

congestive heart failure using eigenvalue decomposition-based features extracted from HRV

signals. Biocybern. Biomed. Eng. 39, 312–327, 2018.

[14] Li S., Multifractal detrended fluctuation analysis of congestive heart failure disease based on

constructed heartbeat sequence. IEEE Access, 8, 205244–205249, 2020.

[15] Ning W., Li S., Wei D., Guo L. Z., Chen, H. Automatic detection of congestive heart failure

based on a hybrid deep learning algorithm in the internet of medical things. IEEE Internet

Things, 8, 12550–12558, 2021.

[16] Porumb M., Iadanza E., Massaro S., Pecchia L., A convolutional neural network approach to

detect congestive heart failure. Biomed. Signal Process. Control 55,101597, 2020.

[17] Hadiyoso S., Rizal A., Electrocardiogram signal classification using higher-order complexity of

Hjorth descriptor. Adv. Sci. Lett. 23, 3972–3974, 2017.

[18] Yingthawornsuk T., Temsang P., Cardiac arrhythmia classification using Hjorth descriptors.

Advances in Intelligent Systems and Computing, 807, 2019.

[19] Zhao L., Liu C., Wei S., Shen Q., Zhou F., Li J., A new entropy-based atrial fibrillation

detection method for scanning wearable ECG recordings. Entropy, 20, 904, 2018.

[20] Tang S. C., Huang P. W., Hung C. S., Shan S. M., Lin Y. H., Shieh, J. S., et al., Identification of

atrial fibrillation by quantitative analyses of fingertip photoplethysmogram. Sci. Rep 7:45644,

2017.

[21] Hussain L., Awan I. A., Aziz W., Saeed S., Ali A., Zeeshan F., et al., Detecting congestive heart

failure by extracting multimodal features and employing machine learning techniques. Biomed

Res. Int., 4281243, 2020.

[22] Yoon K. H., Nam Y., Thap T., Jeong C., Kim N. H., Ko J. S., et al., Automatic detection of

congestive heart failure and atrial fibrillation with short RR interval time series. J. Electr. Eng.

Technol. 12, 346–355, 2017.

[23] Rizal A., Hadiyoso S., ECG signal classification using Hjorth descriptor. Proceedings of the Int.

Conf. Autom. Cogn. Sci. Opt. Micro Electro-Mechanical Syst. Inf. Technol. ICACOMIT 2015,

(Bandung), 87–90, 2015.

[24] Hadiyoso S., Rizal A., Electrocardiogram signal classification using higher-order complexity of

Hjorth descriptor. Adv. Sci. Lett. 23, 3972–3974, 2017.

[25] Yingthawornsuk T., Temsang P., Cardiac arrhythmia classification using Hjorth descriptors.

Advances in Intelligent Systems and Computing, 807, 2018.

[26] Chashmi A. J., Amirani M. C., An efficient and automatic ECG arrhythmia diagnosis system

using DWT and HOS features and entropy-based feature selection procedure. J. Electr.

Bioimpedance 10, 47–54, 2019.

[27] D. Kumar Atal, M. Singh, Arrhythmia classification with ECG signals based on the

optimization-enabled deep convolutional neural network. Computer Methods and Programs in

Biomedicine, 196,105607, 2020.

[28] J. Zheng, H. Chu, D. Struppa et al., Optimal multi-stage arrhythmia classification approach.

Scientific Reports,10(1), 2020.

[29] J. Ferretti, V. Randazzo, G. Cirrincione, E. Pasero, 1-D convolutional neural network for ECG

arrhythmia classification. Artificial Intelligence and Neural Systems. Smart Innovation, Systems

and Technologies,184, 2021.

[30] W. zhu, X. Chen, Y. Wang, L. Wang, Arrhythmia recognition and classification using ECG

morphology and segment feature analysis. IEEE/ACM Transactions on Computational Biology

and Bioinformatics, 16(1), 131–138, 2019.

[31] R. He, Y. Liu, K. Wang et al., Automatic cardiac arrhythmia classification using combination of

deep residual network and bidirectional LSTM. IEEE Access, 7, 102119–102135, 2019.

[32] J. Huang, B. Chen, B. Yao, W. He, ECG arrhythmia classification using STFT-based

spectrogram and convolutional neural network. IEEE Access, 7, 92871–92880, 2019.

[33] P. Wang, B. Hou, S. Shao, and R. Yan, ECG arrhythmias detection using auxiliary classifier

generative adversarial network and residual network,” IEEE Access, 7, 100910–100922, 2019.

[34] E. Izci, M. A. Ozdemir, R. Sadighzadeh, A. Akan, Arrhythmia detection on ECG signals by

using empirical mode decomposition. Proceedings of the Medical Technologies National

Congress (TIPTEKNO), 1–4, November 2018.

[35] S. L. Oh, E. Y. K. Ng, R. S. Tan, U. R. Acharya, Automated diagnosis of arrhythmia using

combination of CNN and LSTM techniques with variable length heart beats. Computers in

Biology and Medicine, 102, 278–287, 2018.

[36] M. S. Mathew, C. Kambhamettu, K. E. Barner, A novel application of deep learning for singlelead

ECG classification. Computers in Biology and Medicine, 99, 53–62, 2018.

[37] I. Ali, S. Ozdalili, Cardiac arrhythmia detection using deep learning. Procedia Computer

Science, 120, 268–275, 2017.

[38] S. Sahoo, B. Kanungo, S. Behera, and S. Sabut, Multiresolution wavelet transform based feature

extraction and ECG classification to detect cardiac abnormalities. Measurement, 108, 55–66,

2017.

[39] P. Li, Y. Wang, J. He, High-performance personalized heartbeat classification model for longterm

ECG signal. IEEE Transactions on Biomedical Engineering, 64(1), 78–86, 2017.

[40] S. Shadmand, B. Mashoufi, A new personalized ECG signal classification algorithm using

block-based neural network and particle swarm optimization. Biomedical Signal Processing and

Control, 25,12–23, 2016.

[41] L. S. C. de Oliveira, R. V. Andreao, and M. Sarcinelli Filho, Bayesian network with decision

threshold for heart beat classification. IEEE Latin America Transactions, 14 (3), 1103–1108,

2016.

[42] P. B. Sakhare, R. Ghongade, An approach for ECG beats classification using adaptive neuro

fuzzy inference system. Proceedings of the Annual IEEE India Conference (INDICON),1–6,

New Delhi, India, 2015.

[43] M. Vijayavanan, V. Rathikarani, P. Dhanalakshmi, Automatic classification of ECG signal for

heart disease diagnosis using morphological features. International Journal of Computer Science

Engineering and Technology (IJCSET), 5(4), 449–455, 2014.

[44] N. P. Joshi, P. S. Topannavar, Support vector machine based heartbeat classification.

Proceedings of the of 4th IRF International Conference, 140–144, New Delhi, India, April 2014.

[45] M. K. Das, S. Ari, ECG beats classification using mixture of features. International Scholarly

Research Notices, 2014, 178436, 2014.

[46] R. J. Martis, U. R. Acharya, L. C. Min, ECG beat classification using PCA, LDA, ICA and

discrete wavelet transform. Biomedical Signal Processing and Control, 8(5), 437–448, 2013.

[47] R. J. Martis, U. R. Acharya, C. M. Lim, K. M. Mandana, A. K. Ray, and C. Chakraborty,

Application of higher order cumulant features for cardiac health diagnosis using ECG signals.

International Journal of Neural Systems, 23(4), 1350014, 2013.

[48] V. S. R. Kumari, P. R. Kumar, Cardiac arrhythmia prediction using improved multilayer

perceptron neural network. Research and Development (IJECIERD), 3(4) , 73–80, 2013.

[49] V. K. Srivastava and D. Prasad, Dwt-based feature extraction from ECG signal. American

journal of Engineering Research (AJER), 2(3), 44–50, 2013.

[50] A. Khazaee, Heart beat classification using particle swarm optimization. International Journal of

Intelligent Systems and Applications, 5(6), 25–33, 2013.

[51] S. M. Jadhav, S. L. Nalbalwar, A. A. Ghatol, Artificial neural network models based cardiac

arrhythmia disease diagnosis from ECG signal data. International Journal of Computer

Applications, 44 (15), 2012.

[52] J.-S. Wang, W.-C. Chiang, Y.-T. C. Yang, Y.-L. Hsu, An effective ECG arrhythmia

classification algorithm. Bio-Inspired Computing and Applications, Springer, 6840, 545–550,

Berlin, Germany, 2012.

[53] C. Kamath, ECG beat classification using features extracted from teager energy functions in time

and frequency domains. IET Signal Processing, 5(6), 575–581, 2011.

[54] A. Dallali, A. Kachouri, and M. Samet, “Classification of cardiac arrhythmia using WT, HRV,

and fuzzy C-means clustering,” Signal Processing: An International Journal, vol. 5, no. 3, pp.

101–109, 2011.

[55] E. Zeraatkar, S. Kermani, A. Mehridehnavi, A. Aminzadeh, E. Zeraatkar, and H. Sanei,

“Arrhythmia detection based on Morphological and time-frequency Features of t-wave in

Electrocardiogram,” Journal of Medical Signals and Sensors, vol. 1, no. 2, pp. 99–106, 2011.

[56] Z. Slimane and A. Nait-Ali, “QRS complex detection using empirical mode decomposition,”

Digit Signal Process, 20 (4), 4, 1221–1228, 2010.

[57] M. Liu and Y. Kim, Classification of heart diseases based on ECG signals using long short-term

memory. Proceedings of the 2018 40th Annual International Conference of the IEEE

Engineering in Medicine and Biology Society (EMBC), 2707–2710, IEEE, Honolulu, HI, USA,

July 2018.

[58] R. D. Labati, E. Mooz, V. Piuri, R. Sassi, and F. Scotti, Deep ECG: convolutional neural

networks for ECG biometric recognition. Pattern Recognition Letters, 2018.

[59] J. Takalo-Mattila, J. Kiljander, J.-P. Soininen, Interpatient ECG classification using deep

convolutional neural networks. Proceedings of the 2018 21st Euromicro Conference on Digital

System Design (DSD), 421–425, IIEEE, Prague, Czech Republic, August 2018.

[60] M. Chen, G. Wang, P. Xie et al., Region aggregation network: improving convolutional neural

network for ECG characteristic detection. Proceedings of the 2018 40th Annual International

Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2559–2562,

IEEE, Honolulu, HI, USA, July 2018.

[61] J. Rubin, S. Parvaneh, A. Rahman, B. Conroy, and S. Babaeizadeh, Densely connected

convolutional networks for detection of atrial fibrillation from short single-lead ECG recordings.

Journal of Electrocardiology, 51(6), 18–21, 2018.

[62] Y. Xia, N. Wulan, K. Wang, H. Zhang, Detecting atrial fibrillation by deep convolutional neural

networks. Computers in Biology and Medicine, 93, 84–92, 2018.

[63] Z. Zhao, Y. Zhang, Y. Deng, and X. Zhang, ECG authentication system design incorporating a

convolutional neural network and generalized s-transformation. Computers in Biology and

Medicine, 102, 168–179, 2018.

[64] R. Kamaleswaran, R. Mahajan, O. Akbilgic, A robust deep convolutional neural network for the

classification of abnormal cardiac rhythm using single lead electrocardiograms of variable

length, Physiological Measurement, 39(3), 035006, 2018.

[65] X. Zhai and C. Tin, Automated ECG classification using dual heartbeat coupling based on

convolutional neural network. IEEE Access, 6, 27465–27472, 2018.

[66] Robby Rohmantri, Nico Surantha, Arrhythmia classification using 2-d convolutional neural

network. International journal of advanced computer science and applications (IJACSA), 11(4),

201-208, 2020.

[67] F. Andreotti, O. Carr, M. A. Pimentel, A. Mahdi, and M. De Vos, Comparing feature-based

classifiers and convolutional neural networks to detect arrhythmia from short segments of ECG.

Proceedings of the 2017 Computing in Cardiology (CINC), 1–4, IEEE, Rennes, France,

September 2017.

[68] O. Yildirim, P. Ozal, R.-S. Tan, and U. R. Acharya, Arrhythmia detection using deep

convolutional neural network with long duration ECG signals. Computers in Biology and

Medicine, 102, 411–420, 2018.

[69] U. R. Acharya, H. Fujita, S. L. Oh et al., Deep convolutional neural network for the automated

diagnosis of congestive heart failure using ECG signals. Applied Intelligence, 49(1), 16–27,

2019.

[70] D. Li, J. Zhang, Q. Zhang, and X. Wei, Classification of ECG signals based on 1d convolution

neural network, Proceedings of the 2017 IEEE 19th International Conference on e-health

Networking, Applications and Services (healthcom), 1–6, IEEE, Dalian, China, October 2017.

[71] B. Chandra, C. S. Sastry, S. Jana, and S. Patidar, Atrial fibrillation detection using convolutional

neural networks, Proceedings of the 2017 Computing in Cardiology (CINC), 1–4, IEEE, Rennes,

France, September 2017.

[72] B. Pourbabaee, M. J. Roshtkhari, and K. Khorasani, Deep convolutional neural networks and

learning ECG features for screening paroxysmal atrial fibrillation patients, IEEE Transactions on

Systems, Man, and Cybernetics: Systems, 48(12), 2095–2104, 2017.

[73] L. Wang and X. Zhou, Detection of congestive heart failure based on LSTM-based deep network

via short-term RR intervals. Sensors, 19(7), 7, 1502, 2019.

[74] G. Sayantan, P. Kien, and K. Kadambari, Classification of ECG beats using deep belief network

and active learning. Medical & Biological Engineering & Computing, 56 (10), 1887–1898, 2018.

[75] C. Zhang, G. Wang, J. Zhao, P. Gao, J. Lin, and H. Yang, Patient-specific ECG classification

based on recurrent neural networks and clustering technique. Proceedings of the 2017 13th

international conference on biomedical engineering (biomed), 63–67, IEEE, Innsbruck, Austria,

February 2017.

[76] V. Sujadevi, K. Soman, and R. Vinayakumar, Real-time detection of atrial fibrillation from short

time single lead ECG traces using recurrent neural networks. Proceedings of the International

Symposium on Intelligent Systems Technologies and Applications, 212–221, Springer, Manipal,

India, September 2017.

[77] J. Schmidhuber, “Deep learning in neural networks: an overview,” Neural Networks, 61, 85–

117, 2015.

[78] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no.

8, pp. 1735–1780, 1997.

[79] J. Schmidhuber, “Deep learning in neural networks: an overview,” Neural Networks, vol. 61, pp.

85–117, 2015.

[80] H. Nasser AlEisa, E. M. El-kenawy, A. Ali Alhussan, M. Saber, A. A. Abdelhamid et al.,

Transfer learning for chest x-rays diagnosis using dipper throated algorithm, Computers.

Materials & Continua, 73(2), 2371–2387, 2022.

[81] Mohamed Saber, Efficient Phase Recovery System. Indonesian Journal of Electrical Engineering

and Computer Science, 5 (1), 123-129, 2017.

[82] Mohamed Saber, A novel design and Implementation of FBMC transceiver for low power

applications. Indonesian Journal of Electrical Engineering and Informatics, 8(1), 83-93, 2020.