FPGA Implementation of Artificial Neural Network (ANN) for ECG Signal Classification

Abstract

The heart is one of the crucial parts of the human being. The graphical recording of the cardiac cycle produced by an Electrocardiograph is called an Electrocardiogram (ECG) signal. To predict the occurrence of an arrhythmia, an electrocardiogram (ECG) is generally used by doctors to identify the condition of the patient. Hence, to accurately detect the abnormalities of the heart in advance and classify those diseases without human involvement many machine learning algorithms are used. The MIT-BIH Arrhythmia database is being used to classify the beat classification performance. This paper presents the hardware implementation of a classifier using an Artificial Neural Network (ANN) to classify four abnormalities (Normal beat, Supraventricular ectopic beat, Ventricular ectopic beat, Fusion beat) of heartbeat with high accuracy. To an appropriate input vector for the classifier, several preprocessing stages have been applied. Discrete Wavelet Transform (DWT) is used to extract the features from the ECG signal. To implement this work, Xilinx Artix-7 NESYS 4 DDR FPGA board is used. This model got 86% testing accuracy in simulation and 85.6% in hardware.