Improving the speed and accuracy of arrhythmia classification based on morphological features of ECG signal
الموضوعات : Majlesi Journal of Telecommunication Devices
Kamran Dehgany habib abadi
1
(Department of electrical Engineering, Islamic Azad University, Najaf abad Branch, Isfahan, Iran)
Mohammad Yousefi
2
(Najafabad Branch, Islamic Azad University)
الکلمات المفتاحية: Morphology, Classification, Evaluation, eCG, HEART, Feature extraction,
ملخص المقالة :
ECG cardiac signals play a crucial role in determining heart disease. Somehow, by knowing the heart rate on the ECG, one can distinguish the type of arrhythmia and the type of disease. Arrhythmias are a type of heart disease that affects the normal functioning of the heart. The electrical activity of the heart is shown at the peaks of P, QRS, T, and the ST and PR sections. In this study, an effective method for identifying cardiac arrhythmias based on morphological features is presented. The extracted features are classified using SVM and KNN classification and random forest RF. Accuracy, sensitivity, positive predictive rate, negative predictive rate as well as execution time were used to evaluate the proposed method. The results show the superiority of the proposed method.
[1] G. Mardanian, N. Behzadfar, “A new method for detection of breast cancer in mammography images using a firefly algorithm”, Journal of Intelligent Procedures in Electrical Technology, Vol. 10, No, 40, pp. 23-32, 2020.
[2] F. Shyasi, M.R. Yousefi, “The study of pain types, its inhibitory methods and tens effect on pain”, Journal of Intelligent Procedures in Electrical Technology, Vol. 12, No. 45, pp. 17-33, 2021.
[3] J. M. Rantanen, S. Riahi, E. B. Schmidt, M. B. Johansen, P. Søgaard, and J. H. Christensen, "Arrhythmias in Patients on Maintenance Dialysis: A Cross-sectional Study," American Journal of Kidney Diseases, Vol. 75, No. 2, pp. 214-224, 2020.
[4] S. K. Pandey, V. R. Sodum, R. R. Janghel, and A. Raj, "ECG Arrhythmia Detection with Machine Learning Algorithms," in Data Engineering and Communication Technology: Springer, 2020, pp. 409-417.
[5] A. Tamizi, M. Ataei, M. Yazdchi, “Improving Diagnosis of Heart Disease by Analyzing Chaotic Indices of ECG Signals”, Journal of Intelligent Procedures in Electrical Technology, Vol. 3, No. 10, pp. 19-26, 2013.
[6] S. Moein, Z. Beheshti, “Improvement of ECG Signal Noise Removal Using Recursive Kalman Filter”, Journal of Intelligent Procedures in Electrical Technology, Vol. 2, No. 5, pp. 43-48, 2011.
[7] C.-H. Lin, "Frequency-domain features for ECG beat discrimination using grey relational analysis-based classifier," Computers & Mathematics with Applications, Vol. 55, No. 4, pp. 680-690, 2008.
[8] R. J. Martis, U. R. Acharya, C. M. Lim, and J. S. Suri, "Characterization of ECG beats from cardiac arrhythmia using discrete cosine transform in PCA framework," Knowledge-Based Systems, Vol. 45, pp. 76-82, 2013.
[9] R. G. Afkhami, G. Azarnia, and M. A. Tinati, "Cardiac arrhythmia classification using statistical and mixture modeling features of ECG signals," Pattern Recognition Letters, Vol. 70, pp. 45-51, 2016.
[10] S. Palaniappan and R. Awang, "Intelligent heart disease prediction system using data mining techniques," in 2008 IEEE/ACS international conference on computer systems and applications, 2008, pp. 108-115: IEEE.
[11] A. Rajkumar and G. S. Reena, "Diagnosis of heartdisease using datamining algorithm," Global journal of computer science and technology, Vol. 10, No. 10, pp. 38-43, 2010.
[12] M. A. Ma'Sum, W. Jatmiko, and H. Suhartanto, "Enhanced tele ECG system using hadoop framework to deal with big data processing," in 2016 International Workshop on Big Data and Information Security (IWBIS), 2016, pp. 121-126: IEEE.
[13] H. Lassoued and R. Ketata, "ECG Decision Support System based on feedforward Neural Networks," 2018.
[14] H. Li, D. Yuan, X. Ma, D. Cui, and L. Cao, "Genetic algorithm for the optimization of features and neural networks in ECG signals classification," Scientific reports, Vol. 7, p. 41011, 2017.
[15] J. O’Brien, "Using Hidden Markov Models and Spark to Mine ECG Data," 2019.
[16] F. Celesti et al., "Big data analytics in genomics: The point on Deep Learning solutions," in 2017 IEEE Symposium on Computers and Communications (ISCC), 2017, pp. 306-309: IEEE.
[17] F. I. Alarsan and M. Younes, "Analysis and classification of heart diseases using heartbeat features and machine learning algorithms," Journal of Big Data, Vol. 6, No. 1, pp. 1-15, 2019.
[18] R. U. Khan, T. Hussain, H. Quddus, A. Haider, A. Adnan, and Z. Mehmood, "An Intelligent Real-time Heart Diseases Diagnosis Algorithm," in 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), 2019, pp. 1-6: IEEE.
[19] M. O. G. Nayeem, M. N. Wan, and M. K. Hasan, "Prediction of Disease Level Using Multilayer Perceptron of Artificial Neural Network for Patient Monitoring," International Journal of Soft Computing and Engineering (IJSCE), 2015.
[20] J. Huang, B. Chen, B. Yao, and W. He, "ECG arrhythmia classification using STFT-based spectrogram and convolutional neural network," IEEE Access, Vol. 7, pp. 92871-92880, 2019.
[21] H. He, Y. Tan, and J. Xing, "Unsupervised classification of 12-lead ECG signals using wavelet tensor decomposition and two-dimensional Gaussian spectral clustering," Knowledge-Based Systems, Vol. 163, pp. 392-403, 2019.
[22] A. Diker, E. Avci, E. Tanyildizi, and M. Gedikpinar, "A novel ECG signal classification method using DEA-ELM," Medical Hypotheses, Vol. 136, pp. 109515, 2020.
[23] N. M. M. Nascimento, L. B. Marinho, S. A. Peixoto, J. P. do Vale Madeiro, V. H. C. de Albuquerque, and P. P. Rebouças Filho, "Heart arrhythmia classification based on statistical moments and structural co-occurrence," Circuits, Systems, and Signal Processing, Vol. 39, No. 2, pp. 631-650, 2020.
[24] M. Thiyagaraj and G. Suseendran, "Enhanced Prediction of Heart Disease Using Particle Swarm Optimization and Rough Sets with Transductive Support Vector Machines Classifier," in Data Management, Analytics and Innovation: Springer, 2020, pp. 141-152.
[25] B. A. Tama, S. Im, and S. Lee, "Improving an Intelligent Detection System for Coronary Heart Disease Using a Two-Tier Classifier Ensemble," BioMed Research International, 2020.
[26] A. Baccouche, B. Garcia-Zapirain, C. Castillo Olea, and A. Elmaghraby, "Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico," Information, Vol. 11, No. 4, p. 207, 2020.
[27] A. Dutta, T. Batabyal, M. Basu, and S. T. Acton, "An Efficient Convolutional Neural Network for Coronary Heart Disease Prediction," Expert Systems with Applications, p. 113408, 2020.
[28] A. Paithane and D. Bormane, "Electrocardiogram signal analysis using empirical mode decomposition and Hilbert spectrum," in 2015 International Conference on Pervasive Computing (ICPC), 2015, pp. 1-4: IEEE.
[29] M. B. Hossain, S. K. Bashar, A. J. Walkey, D. D. McManus, and K. H. Chon, "An Accurate QRS Complex and P Wave Detection in ECG Signals Using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Approach," IEEE Access, Vol. 7, pp. 128869-128880, 2019.
[30] M. Murugappan, L. Murugesan, S. Jerritta, and H. Adeli, "Sudden Cardiac Arrest (SCA) Prediction Using ECG Morphological Features," Arabian Journal for Science and Engineering, pp. 1-15, 2020.
[31] Y. Ji, S. Zhang, and W. Xiao, "Electrocardiogram classification based on faster regions with convolutional neural network," Sensors, Vol. 19, No. 11, p. 2558, 2019.
[32] J. F. Saenz-Cogollo and M. Agelli, "Investigating Feature Selection and Random Forests for Inter-Patient Heartbeat Classification," Algorithms, Vol. 13, No. 4, p. 75, 2020.
