[01] Kamel Hussein RAHOUMA, Electrical Engineering Department, Faculty of Engineering, Minia University, Minia, Egypt. [02] Rabab Hamed MUHAMMAD, Computer and System Engineering Department, Faculty of Engineering, Minia University, Minia, Egypt. [03] Hesham Fathi Ali HAMED, Electrical Engineering Department, Faculty of Engineering, Minia University, Minia, Egypt. [04] Mona AbdelBaset ABO-ELDAHAB, Electrical Engineering Department, Faculty of Engineering, Minia University, Minia, Egypt.

This paper aims to Analyze the heart electrocardiograph (ECG) to diagnose the heart performance and predict any future complications. The authors utilize the MIT BIH database to obtain the original data. The discrete wavelet transform (DWT) is used to decompose the ECG signal and to reconstruct it. The means of different performance measures of the heart performance (QRS, RR, ST, QT, PR) are tested for their significance before comparing them to the limits of normal performance. Any abnormal measure is used to diagnose the corresponding disease(s). Prediction techniques are used to expect any future complications regarding the abnormal measure(s). Two methods of prediction are applied: the Linear Prediction Method (LPM) and the Grid Partitioning and Fuzzy c-mean Clustering based on Neuro-Fuzzy prediction.

Electrocardiograph (ECG), Heart Diseases Diagnosis, Wavelet Transform, Linear Prediction Method, Grid Partitioning Method, Fuzzy c-mean (FCM), Neuro-Fuzzy (ANFIS)

[01] Hernández, A. I., Dumont, J., Altuve, M., Beuchée, A., & Carrault, G. (2012). Evolutionary Optimization of ECG Feature Extraction Methods: Applications to the Monitoring of Adult Myocardial Ischemia and Neonatal Apnea Bradycardia Events. In ECG Signal Processing, Classification and Interpretation (pp. 237-273). Springer London [02] Gupta, R., Mitra, M., & Bera, J. (2014). ECG Signal Analysis. In ECG Acquisition and Automated Remote Processing (pp. 15-49). Springer India. [03] Vidya, M. J., & Kavya, D. (2013). Analysis of ECG Signal Using Matlab for the Detection of Ischemia. International Journal of Innovative Research and Development ISSN 2278–0211, 2(4), 308-319. [04] Pal, S., & Mitra, M. (2010). Detection of ECG characteristic points using multiresolution wavelet analysis based selective coefficient method. Measurement, 43(2), 255-261. [05] Hamilton, P. S. (1996). A comparison of adaptive and nonadaptive filters for reduction of power line interference in the ECG. IEEE transactions on biomedical engineering, 43(1), 105-109. [06] Banerjee, S., Gupta, R., & Mitra, M. (2012). Delineation of ECG characteristic features using multiresolution wavelet analysis method. Measurement, 45(3), 474-487. [07] Lourenço, A., Silva, H., Leite, P., Lourenço, R., & Fred, A. L. (2012, February). Real Time Electrocardiogram Segmentation for Finger based ECG Biometrics. In Biosignals (pp. 49-54). [08] Trahanias, P. E. (1993). An approach to QRS complex detection using mathematical morphology. IEEE Transactions on Biomedical Engineering, 40(2), 201-205. [09] Benitez, D., Gaydecki, P. A., Zaidi, A., & Fitzpatrick, A. P. (2001). The use of the Hilbert transform in ECG signal analysis. Computers in biology and medicine, 31(5), 399-406. [10] Arzeno, N. M., Deng, Z. D., & Poon, C. S. (2008). Analysis of first-derivative based QRS detection algorithms. IEEE Transactions on Biomedical Engineering, 55(2), 478-484. [11] Karpagachelvi, S., Arthanari, M., & Sivakumar, M. (2010). ECG feature extraction techniques-a survey approach. arXiv preprint arXiv:1005.0957. [12] Vaneghi, F. M., Oladazimi, M., Shiman, F., Kordi, A., Safari, M. J., & Ibrahim, F. (2012, February). A comparative approach to ECG feature extraction methods. In Intelligent Systems, Modelling and Simulation (ISMS), 2012 Third International Conference on (pp. 252-256). IEEE. [13] De Sarkar, S. C., Chattopadhyay, S., & Pratihar, D. K. (2011). A comparative study of fuzzy c-means algorithm and entropy-based fuzzy clustering algorithms. [14] Volna, E., Kotyrba, M., & Habiballa, H. (2015). ECG Prediction Based on Classification via Neural Networks and Linguistic Fuzzy Logic Forecaster. The Scientific World Journal, 2015. [15] Mazomenos, E. B., Chen, T., Acharyya, A., Bhattacharya, A., Rosengarten, J., & Maharatna, K. (2012, March). A time-domain morphology and gradient based algorithm for ECG feature extraction. In Industrial Technology (ICIT), 2012 IEEE International Conference on (pp. 117-122). IEEE. [16] Li, C., Zheng, C., & Tai, C. (1995). Detection of ECG characteristic points using wavelet transforms. IEEE Transactions on biomedical Engineering, 42(1), 21-28. [17] Saxena, S. C., Kumar, V., & Hamde, S. T. (2002). Feature extraction from ECG signals using wavelet transforms for disease diagnostics. International Journal of Systems Science, 33(13), 1073-1085. [18] Martínez, J. P., Almeida, R., Olmos, S., Rocha, A. P., & Laguna, P. (2004). A wavelet-based ECG delineator: evaluation on standard databases. IEEE Transactions on biomedical engineering, 51(4), 570-581. [19] Martis, R. J., Chakraborty, C., & Ray, A. K. (2009, December). An integrated ECG feature extraction scheme using PCA and wavelet transform. In India Conference (INDICON), 2009 Annual IEEE (pp. 1-4). IEEE. [20] https://www.physionet.org/physiobank/database/mitdb [21] Khan, M. I., Hossain, M. B., & Uddin, A. N. (2013, May). Performance analysis of modified zero crossing counts method for heart arrhythmias detection and implementation in HDL. In Informatics, Electronics & Vision (ICIEV), 2013 International Conference on (pp. 1-6). IEEE. [22] Abo-Zahhad, M. (2011). ECG signal compression using discrete wavelet transform. Discrete Wavelet Transforms-Theory and Application, 143-168. [23] Vidya, K. S., Acharya, U. R., Ghista, D. N., Lim, W. J. E., Molinari, F., & Sankaranarayanan, M. (2015). Computer-aided diagnosis of diabetic subjects by heart rate variability signals using discrete wavelet transform method. Knowledge-based systems, 81, 56-64. [24] Griswold, N. C., Mathur, S. S., Yeary, M., & Spencer, R. G. (2000). Wavelet decomposition/reconstruction of images via direct products. Journal of Electronic Imaging, 9(1), 61-71. [25] Kabir, M. A., & Shahnaz, C. (2011, November). An ECG signal denoising method based on enhancement algorithms in EMD and wavelet domains. In TENCON 2011-2011 IEEE Region 10 Conference (pp. 284-287). IEEE [26] Kaur, M., & Kaur, G. (2015). Adaptive Wavelet Thresholding for Noise reduction in Electrocardiogram (ECG) Signals. International Journal of Computer Science and Network Security (IJCSNS), 15(4), 100.‏ [27] Olsson, U. (2000). Confidence intervals for the mean of a log-normal distribution. Swedish University of Agricultural Sciences. [28] http://www.practicalclinicalskills.com/ekg-reference-guide [29] Mannurmath, J. C., & Raveendra, M. (2014). MATLAB Based ECG Signal Classification. International Journal of Science, Engineering and Technology Research (IJSETR), 3(7). [30] Dallali, A., Kachouri, A., & Samet, M. (2011). Fuzzy c-means clustering, Neural Network, wt, and Hrv for classification of cardiac arrhythmia. ARPN Journal of Engineering and Applied Sciences, 6(10), 2011. [31] Viswanath, C., & Valliyammai, C. (2012, March). CPU load prediction using ANFIS for grid computing. In Advances in Engineering, Science and Management (ICAESM), 2012 International Conference on (pp. 343-348). IEEE. [32] Abhinav-Vishwa, M. K., Lal, S. D., & Vardwaj, P. (2011). Clasification of arrhythmic ECG data using machine learning techniques. International Journal of Interactive Multimedia and Artificial Intelligence, 1(4). [33] Loong, J. C., Subari, K. S., Besar, R., & Abdullah, M. K. (2010). A new approach to ECG biometric systems: a comparative study between LPC and WPD systems. World Academy of Science, Engineering and Technology, 68(20), 759-764. [34] Jin, X. B., Wang, J. F., Zhang, H. Y., & Cao, L. H. (2013). ANFIS model for time series prediction. In Applied Mechanics and Materials (Vol. 385, pp. 1411-1414). Trans Tech Publications. [35] Nazmy, T. M., El-Messiry, H., & Al-Bokhity, B. (2010, March). Adaptive neuro-fuzzy inference system for classification of ECG signals. In Informatics and Systems (INFOS), 2010 The 7th International Conference on (pp. 1-6). IEEE. [36] Kaur, R. P., & Klair, A. S. Empirical Study of Grid partitioning and Subtractive Clustering based NF Systems for the Evaluation of Fault Proneness in Open source Software Systems. [37] Au, W. H. (2008). A Distributed Algorithm for Mining Fuzzy Association Rules in Traditional Databases. In Handbook of Research on Fuzzy Information Processing in Databases (pp. 685-705). IGI Global. [38] Schwämmle, V., & Jensen, O. N. (2010). A simple and fast method to determine the parameters for fuzzy c–means cluster analysis. Bioinformatics, 26(22), 2841-2848.‏ [39] Peterkova, A., & Stremy, M (2015). The raw ECG signal processing and the detection of QRS complex.