Computational Framework for Prediction of Cardiac Disorders by analyzing ECG signals Using Machine Learning Technique

The clinical diagnosis of heart disorders relies heavily on electrocardiograms (ECGs). Numerous abnormalities in heart are being identified with a record of heart signal throughout intervals. This paper presents a novel computational framework for detecting heart disorders by analyzing the ECG signals using machine learning technology. Monitoring and diagnosing ECGs signals in daily life are appearing recently due to an increase in healthcare equipment. Monitoring ECG signals is a crucial area of research because it enables early detection of catastrophic heart problems in people. Since conventional signal identification only considers one reference beat for identifying ECG signals, each individual's detection rate varies. In this paper, field-programmable gate array (FPGA) is employed to speed up ECG signal diagnosis and measure appropriate outcome to demonstrate that suggested ECG diagnosis algorithm is appropriate for hardware acceleration. The ECG diagnosis algorithm rapidly determine reference beats that change depending on person and analyze each person's signal executed at FPGA in real-time. In this paper, Noise removal from input ECG data set is performed by adaptive filter technique and base line wander is also removed. Machine learning in ECG classification is done by Artificial Neural Network (ANN) that allows to use less energy while still providing accurate classification. MATLAB software is employed to carry out this work and corresponding outputs are obtained for ECG classification.