Clinical Medicine Journal
Articles Information
Clinical Medicine Journal, Vol.3, No.3, Jun. 2017, Pub. Date: Nov. 1, 2017
Heart Diseases Detection Based on Analysis and Diagnosis of Electrocardiogram Using Wavelet Transform and Prediction of Future Complications
Pages: 15-29 Views: 411 Downloads: 177
[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)
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