A Review Study for Electrocardiogram Signal Classification
Keywords:Artificial neural network, Convolution neural network, Discrete wavelet transform, Support vector machine, K-nearest neighbor
An electrocardiogram (ECG) signal is a recording of the electrical activity generated by the heart. The analysis of the ECG signal has been interested in more than a decade to build a model to make automatic ECG classification. The main goal of this work is to study and review an overview of utilizing the classification methods that have been recently used such as Artificial Neural Network, Convolution Neural Network (CNN), discrete wavelet transform, Support Vector Machine (SVM), and K-Nearest Neighbor. Efficient comparisons are shown in the result in terms of classification methods, features extraction technique, dataset, contribution, and some other aspects. The result also shows that the CNN has been most widely used for ECG classification as it can obtain a higher success rate than the rest of the classification approaches.
 M. S. Al-Ani. “Electrocardiogram waveform classification based on P-QRS-T wave recognition”. UHD Journal of Science and Technology, vol. 2, no. 2, pp. 7-14, 2018.
 M. Al-Ani. “A rule-based expert system for automated ecg diagnosis”. International Journal of Advances in Engineering and Technology, vol. 6, no. 4, 1480-1492, 2014.
 M. S. Al-Ani and A. A. Rawi. “ECG Beat diagnosis approach for ECG printout based on expert system”. International Journal of Emerging Technology and Advanced Engineering, vol. 3, no. 4, pp. 797-807, 2013.
 S. H. Jambukia, V. K. Dabhi and H. B. Prajapati. “Classification of ECG Signals Using Machine Learning Techniques: A Survey”. In: Conference Proceeding 2015 International Conference on Advances in Computer Engineering and Applications, pp. 714-721, 2015.
 J. Li, Y. Si, T. Xu and S. Jiang. “Deep convolutional neural network based ecg classification system using information fusion and onehot encoding techniques”. Mathematical Problems Engineering, vol. 2018, p. 7354081, 2018.
 D. Sung, J. Kim, M. Koh and K. Park. “ECG Authentication in postexercise situation ECG authentication in post-exercise situation”. Conference Proceeding IEEE Engineering Medical Biology Socirty, vol. 1, pp. 446-449, 2017.
 M. Lakshmi, D. Prasad and D. Prakash. “Survey on EEG signal processing methods”. International Journal of Advanced Research in Computer Science, vol. 4, no. 1, pp. 84-91, 2014.
 R. Chaturvedi and Y. Yadav. “A survey on compression techniques”. International Journal of Advanced Research in Computer and Communication Engineering, vol. 2, no. 9, pp. 3511-3513, 2013.
 H. Y. Lin, S. Y. Liang, Y. L. Ho, Y. H. Lin and H. P. Ma. “Discretewavelet-transform-based noise removal and feature extraction for ECG signals”. IRBM, vol. 35, no. 6, pp. 351-361, 2014.
 M. Hammad, S. Zhang and K. Wang. “A novel two-dimensional ECG feature extraction and classification algorithm based on convolution neural network for human authentication”. Future Generation Computer Systems, vol. 101, pp. 180-196, 2019.
 J. Juang. “Proceedings of the 3rd International Conference on Intelligent Technologies and Engineering Systems (ICITES2014)”. Vol. 345. In: Lecture Notes in Electrical Engineering, pp. 545-555, 2016.
 A. Giorgio, M. Rizzi and C. Guaragnella. “Efficient detection of ventricular late potentials on ECG signals based on wavelet denoising and SVM classification”. Information, vol. 10, no. 11, p. 328, 2019.
 F. A. R. Sánchez and J. A. G. Cervera. “ECG classification using artificial neural networks”. Journal of Physics: Conference Series, vol. 1221, no. 1, pp. 1-6, 2019.
 S. V. Deshmukh and O. Dehzangi. “ECG-Based Driver Distraction Identification Using Wavelet Packet Transform and Discriminative Kernel-Based Features”. 2017 IEEE International Conference on Smart Computing, 2017.
 S. K. Berkaya, A. K. Uysal, E. S. Gunal, S. Ergin, S. Gunal and M. B. Gulmezoglu. “A survey on ECG analysis”. Biomedical Signal Processing and Control, vol. 43, pp. 216-235, 2018.
 N. A. Polytechnic. “Automated identification of shockable and non-shockable life-threatening ventricular arrhythmias using convolutional neural network”. Future Generation Computer Systems the International Journal of Escience, vol. 79, p. 952, 2017.
 F. A. Elhaj, N. Salim, T. Ahmed, A. R. Harris and T. T. Swee. “Hybrid Classification of Bayesian and Extreme Learning Machine for Heartbeat Classification of Arrhythmia Detection”. In: 6th ICT
 P. Li, K. L. Chan, S. Fu and S. M. Krishnan. “An abnormal ECG beat detection approach for long-term monitoring of heart patients based on hybrid kernel machine ensemble”. Lecture Notes in Computer Science, Vol. 354. 1Springer, Berlin, pp. 346-355, 2005.
 S. Shadmand and B. Mashoufi. “A new personalized ECG signal classification algorithm using block-based neural network and particle swarm optimization”. Biomedical Signal Processing and Control, vol. 25, pp. 12-23, 2016.
 J. Mateo, A. M. Torres, A. Aparicio and J. L. Santos. “An efficient method for ECG beat classification and correction of ectopic beats”. Computers and Electrical Engineering, vol. 53, pp. 219- 229, 2016.
 C. Kamalakannan, L. P. Suresh, S. S. Dash and B. K. Panigrahi. “Power Electronics and Renewable Energy Systems: Proceedings of ICPERES 2014. Vol. 326. Lecture Notes in Electrical Engineering, pp. 1537-1544, 2014.
 C. K. and B. S. “A survey on various machine learning approaches for ECG analysis”. International Journal of Computer Applications, vol. 163, no. 9, pp. 25-33, 2017.
 S. Z. Islam, S. Z. Islam, R. Jidin and M. A. M. Ali. “Performance Study of Adaptive Filtering Algorithms for Noise Cancellation of ECG Signal”. Vol. 4. In: ICICS 2009 Conference Proceeding 7th International Conference Information, Communication Signal Process, 2009.
 M. A. Rahman, M. M. Milu, A. Anjum, A. B. Siddik, M. H. Sifat, M. R. Chowdhury, F. Khanam, M. Ahmad. “A statistical designing approach to MATLAB based functions for the ECG signal preprocessing”. The Iran Journal of Computer Science, vol. 2, no. 3, pp. 167-178, 2019.
 M. T. Almalchy, V. Ciobanu and N. Popescu. “Noise removal from ECG signal based on filtering techniques”. Proceeding 2019 22nd International Conference Control Systems Computer Science, pp.176-181, 2019.
 G. H. Choi, E. S. Bak and S. B. Pan. “User identification system using 2D resized spectrogram features of ECG”. IEEE Access, vol.7, pp. 34862-34873, 2019.
 A. Lay-Ekuakille, M. A. Ugwiri, C. Liguori and P. K. Mvemba. “Enhanced methods for extracting characteristic features from ECG”. IEEE International Symposium on Medical Measurements and Applications, pp. 1-5, 2019.
 J. Oster, J. Behar, O. Sayadi, S. Nemati, A. E. W. Johnson and G. D. Clifford. “Semisupervised ECG ventricular beat classification with novelty detection based on switching kalman filters”. IEEE Transactions on Biomedical Engineering, vol. 62, no. 9, pp. 2125-2134, 2015.
 S. Karpagachelvi. “ECG feature extraction techniques a survey approach”. International Journal of Computer Science and Information Security, vol. 8, no. 1, pp. 76-80, 2010.
 S. Nasehi and H. Pourghassem. “Seizure detection algorithms based on analysis of EEG and ECG signals: A survey”. Neurophysiology, vol. 44, no. 2, pp. 174-186, 2012.
 S. M. J. Jalali, M. Karimi, A. Khosravi and S. Nahavandi. “An efficient neuroevolution approach for heart disease detection”. Conference Proceeding IEEE International Conference System Man Cybernetics, pp. 3771-3776, 2019.
 D. Carrera, B. Rossi, P. Fragneto and G. Boracchi. “Online anomaly detection for long-term ECG monitoring using wearable devices”. Pattern Recognition, vol. 88, pp. 482-492, 2019.
 E. K. Wang, X. Zhang and L. Pan. “Automatic classification of CAD ECG signals with SDAE and bidirectional long short-term network”. IEEE Access, vol. 7, pp. 182873-182880, 2019.
 N. Omer, Y. Granot, M. Kähönen, R. Lehtinen, T. Nieminen, K. Nikus. “Blinded analysis of an exercise ECG database using high frequency QRS analysis”. Vol. 44. In: 2017 Computing in Cardiology, pp. 1-4, 2017.
 I. Karagoz. “Cmbebih 2019”. Vol. 73. In: IFMBE Proceeding C, pp. 159-163, 2019.
 S. Lata and R. Kumar. “Disease classification using ECG signals based on R-peak analysis with ABC and ANN”. The International Journal of Electronics, Communications, and Measurement Engineering, vol. 8, no. 2, pp. 67-86, 2019.
 A. Delrieu, M. Hoël, C. T. Phua and G. Lissorgues. “Multi physiological signs model to enhance accuracy of ECG peaks detection”. IFMBE Proceeding, vol. 61, pp. 58-61, 2017.
 K. C. J. Chen, Y. S. Ni and J. Y. Wang. “Electrocardiogram Diagnosis Using Wavelet-Based Artificial Neural Network”. In: 2016 IEEE 5th Globel Conference Consumer Electronics GCCE 2016, pp. 5-6, 2016.
 M. Boussaa, I. Atouf, M. Atibi and A. Bennis. “ECG Signals Classification Using MFCC Coefficients and ANN Classifier”. Proceeding 2016 International Conference Electronics Information Technology, pp. 480-484, 2016.
 S. Savalia, E. Acosta and V. Emamian. “Classification of cardiovascular disease using feature extraction and artificial neural networks”. Journal of Biosciences and Medicines, vol. 5, no. 11, pp. 64-79, 2017.
 M. Wess, P. D. S. Manoj and A. Jantsch. “Neural Network Based ECG Anomaly Detection on FPGA and Trade-off Analysis. In: Proceedings IEEE International Symposium on Circuits and Systems, 2017.
 S. Pandey and R. R. Janghel. “Classification of ECG arrhythmia using recurrent neural networks ECG arrhythmia classification using artificial neural networks”. Procedia Computer Science, vol. 8, pp. 1290-1297, 2018.
 G. Sannino and G. De Pietro. “A deep learning approach for ECGbased heartbeat classification for arrhythmia detection”. Future Generation Computer Systems, vol. 86, pp. 446-455, 2018.
 T. Debnath, M. Hasan and T. Biswas. “Analysis of ECG Signal and Classification of Heart Abnormalities Using Artificial Neural Network”. In: Proceeding 9th International Conference Electrical and Computer Engineering, pp. 353-356, 2017.
 F. Y. O. Abdalla, L. Wu, H. Ullah, G. Ren, A. Noor and Y. Zhao. “ECG arrhythmia classification using artificial intelligence and nonlinear and nonstationary decomposition”. Signal, Image Video Process, vol. 13, no. 7, pp. 1283-1291, 2019.
 Z. K. Abdul. “Kurdish speaker identification based on one dimensional convolu- tional neural network”. Computational Methods for Differential Equations, vol. 7, no. 4, pp. 566-572, 2019.
 D. Li, J. Zhang, Q. Zhang and X. Wei. “Classification of ECG signals based on 1D convolution neural network”. In: 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services, Healthcom, pp. 1-6, 2017.
 M. Zubair, J. Kim and C. Yoon. “An automated ECG beat classification system using convolutional neural networks”. In: 2016 6th International Conference on IT Convergence and Security, 2016.
 W. Yin, X. Yang, L. Zhang and E. Oki. “ECG monitoring system integrated with IR-UWB radar based on CNN”. IEEE Access, vol. 4, pp. 6344-6351, 2016.
 S. L. Oh, N. A. Polytechnic, N. A. Polytechnic, Y. Hagiwara and J. H. Tan. “A deep convolutional neural network model to classify heartbeats”. Computers in Biology and Medicine, vol. 89, pp. 389-396, 2017.
 X. Zhai and C. Tin. “Automated ECG classification using dual heartbeat coupling based on convolutional neural network”. IEEE Access, vol. 6, pp. 27465-27472, 2018.
 J. Zhang, J. Tian, Y. Cao, Y. Yang and X. Xu. “Deep time frequency representation and progressive decision fusion for ECG classification”. Knowledge-Based Systems, vol. 190, p. 105402, 2020.
 J. Wang. “A deep learning approach for atrial fibrillation signals classification based on convolutional and modified Elman neural network”. Future Generation Computer Systems, vol. 102, pp. 670-679, 2020.
 Q. Yao, R. Wang, X. Fan, J. Liu and Y. Li. “Multi-class arrhythmia detection from 12-lead varied-length ECG using attention-based time-incremental convolutional neural network”. Information Fusion, vol. 53, no. 1, pp. 174-182, 2020.
 H. Limaye and V. V. Deshmukh. “ECG noise sources and various noise removal techniques: A survey”. International Journal of Application or Innovation in Engineering and Management, vol. 5, no. 2, pp. 86-92, 2016.
 S. L. Joshi. “A Survey on ECG Signal Denoising Techniques 2013 International Conference on Communication Systems and Network Technologies A Survey on ECG Signal DenoisingTechniques”, 2013.
 H. El-Saadawy, M. Tantawi, H. A. Shedeed and M. F. Tolba. “Electrocardiogram (ECG) classification based on dynamic beats segmentation”. The ACM International Conference Proceeding Series, pp. 75-80, 2016.
 T. R. Naveen, K. V. Reddy, A. Ranjan and S. Baskaran. “Detection of abnormal ECG signal using DWT feature extraction and CNN”. International Research Journal of Engineering and Technology, vol. 6, no. 3, pp. 5175-5180, 2019.
 U. Desai, R. J. Martis, C. G. Nayak, K. Sarika and G. Seshikala. “Machine Intelligent Diagnosis of ECG for Arrhythmia Classification Using DWT, ICA and SVM Techniques. 12th IEEE International Conference Electronic Energy, Environmental Research Communications, pp. 2-5, 2016.
 S. Saraswat, G. Srivastava and S. Shukla. “Decomposition of ECG Signals using Discrete Wavelet Transform for Wolff Parkinson White Syndrome Patients”. In: Proceedings 2016 International Conference on Micro-Electronics and Telecommunication Engineering, pp. 361-365, 2016.
 E. Alickovic and A. Subasi. “Medical decision support system for diagnosis of heart arrhythmia using DWT and random forests classifier”. The Journal of Medical Systems, vol. 40, no. 4, pp. 1-12, 2016.
 G. Pan, Z. Xin, S. Shi and D. Jin. “Arrhythmia classification based on wavelet transformation and random forests”. Multimedia Tools and Applications Journal, vol. 77, no. 17, pp. 21905-21922, 2018.
 S. Sahoo, B. Kanungo, S. Behera and S. Sabut. “Multiresolution wavelet transform based feature extraction and ECG classification to detect cardiac abnormalities Multiresolution wavelet transform based feature extraction and ECG classification to detect cardiac abnormalities”. Measurement, vol. 17, no. 1, pp. 55-66, 2017.
 M. Barstuğan and R. Ceylan. “The effect of dictionary learning on weight update of AdaBoost and ECG classification”. Journal of King Saud University, vol. 30, pp.1-9, 2018.
 T. Marasović and V. Papić. “A comparative study of FFT, DCT, and DWT for efficient arrhytmia classification in RP-RF framework”. International Journal of E-Health and Medical Communications, vol. 9, no. 1, pp. 35–49, 2018.
 Y. Zhang, Y. Zhang, B. Lo and W. Xu. “Wearable ECG signal processing for automated cardiac arrhythmia classification using CFASE-based feature selection”. Expert System, vol. 37, no. 1, pp.1-13, 2020.
 P. Kora, C. U. Kumari, K. Swaraja and K. Meenakshi. “Atrial Fibrillation detection using Discrete Wavelet Transform. In: Proceedings of 2019 3rd IEEE International Conference on Electrical, Computer and Communication Technologies, pp. 1-3, 2019.
 S. Raj and K. C. Ray. “ECG signal analysis using DCT-Based DOST and PSO Optimized SVM”. IEEE Transactions on Automatic Control, vol. 66, no. 3, pp. 470-478, 2017.
 R. Banerjee, A. Ghose and S. Khandelwal. “A Novel Recurrent Neural Network Architecture for Classification of Atrial Fibrillation Using Single-lead ECG. In: European Signal Processing Conference, pp. 1-5, 2019.
 H. Khorrami and M. Moavenian. “A comparative study of DWT, CWT and DCT transformations in ECG arrhythmias classification”. Expert Systems With Applications, vol. 37, no. 8, pp. 5751-5757, 2010.
 V. Mygdalis, A. Tefas and I. Pitas. “Exploiting multiplex data relationships in support vector machines”. Pattern Recognition, vol. 85, pp. 70-77, 2019.
 F. A. Elhaj, N. Salim, A. R. Harris, T. T. Swee and T. Ahmed. “Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals”. Computer Methods and Programs in Biomedicine, vol. 127, pp. 52-63, 2016.
 V. R. Arjunan. “ECG signal classification based on statistical features with SVM classification”. International Journal of Advances in Signal and Image Sciences, vol. 2, no. 1, p. 5, 2016.
 R. Smíšek, J. Hejč, M. Ronzhina, A. Němcová, L. Maršánová, J. Chmelík, K. Jana. SVM Based ECG classification using rhythm and morphology features, cluster analysis and multilevel noise estimation”. Computing in Cardiolology, vol. 44, pp. 1-4, 2017.
 W. F. Wang, C. Y. Yang and Y. F. Wu. “SVM-based classification method to identify alcohol consumption using ECG and PPG monitoring”. Personal and Ubiquitous Computing, vol. 22, no. 2, pp. 275-287, 2018.
 C. Venkatesan, P. Karthigaikumar, A. Paul, S. Satheeskumaran and R. Kumar. “ECG signal preprocessing and SVM classifierbased abnormality detection in remote healthcare applications”. IEEE Access, vol. 6, pp. 9767-9773, 2018.
 J. Liu, S. Song, G. Sun and Y. Fu. “Classification of ECG arrhythmia Using CNN, SVM and LDA”. Vol. 11633. In: International Conference on Artificial Intelligence and Security, pp. 191-201, 2019.
 J. Zhai and A. Barreto. “Stress Detection in Computer Users Based on Digital Signal Processing of Noninvasive Physiological Variables”. In: Proceedings of the 28th IEEE EMBS Annual International Conference, pp. 1355-1358, 2007.
 V. Gupta and M. Mittal. “KNN and PCA classifier with Autoregressive modelling during different ECG signal interpretation”. Procedia Computer Science, vol. 125, pp. 18-24, 2018.
 N. Flores, R. L. Avitia, M. A. Reyna and C. García. “Readily available ECG databases”. Journal of Electrocardiology, vol. 51, no. 6, pp. 1095-1097, 2018.
 R. P. Narwaria, S. Verma and P. K. Singhal. “Removal of baseline wander and power line interference from ECG signal a survey approach”. International Journal of Information and Electronics Engineering, vol. 3, no. 1, pp. 107-111, 2011.
 N. K. Dewangan and S. P. Shukla. “A survey on ECG signal feature extraction and analysis techniques”. International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, vol. 3, no. 6, pp. 12-19, 2015.
 I. Saini. “Analysis ECG data compression techniques a survey approach”. The International Journal of Emerging Technology and Advanced Engineering, vol. 3, no. 2, pp. 544-548, 2013.
 M. M. Baig, H. Gholamhosseini and M. J. Connolly. “A comprehensive survey of wearable and wireless ECG monitoring systems for older adults”. Medical and Biological Engineering and Computing, vol. 51, no. 5, pp. 485-495, 2013.
 S. Faziludeen and P. Sankaran. “ECG beat classification using evidential K-nearest neighbours”. Procedia Computer Science, vol. 89, pp. 499-505, 2016.
 F. Bouaziz, D. Boutana and H. Oulhadj. “Diagnostic of ECG Arrhythmia Using Wavelet Analysis and K-Nearest Neighbor Algorithm”. In: Proceedings of the 2018 International Conference on Applied Smart Systems, pp. 1-6, 2019.
 T. Khatibi and N. Rabinezhadsadatmahaleh. “Proposing feature engineering method based on deep learning and K-NNs for ECG beat classification and arrhythmia detection”. Physical and Engineering Sciences in Medicine, vol. 43, pp. 1-20, 2019.