Detection and Classification of Arrhythmia

Abstract

    Correct functioning of the heart is essential because organisms can only live provided that their heart is functioning properly. Hence, the heart must at all costs, operate properly. To check whether the heart is functioning properly, an electrocardiogram (ECG) test is performed. ECG or EKG is a test to continuously monitor the rate of the heartbeat and based on that determine the degree of correctness in the functionality of the heart. Aberrations in the heartbeat rhythms can lead to arrhythmia, which is a class of cardiovascular diseases (CVDs). An ECG signal is made up of the PQRST wave. Analysis of the PQRST reference points can help determine the arrhythmia type. Hence, dynamic analysis of ECGs is the number one priority because it will help in providing immediate and robust medical attention to the patients suffering from arrhythmia. This paper discusses microcontroller Arduino Uno along with AD8232 to capture ECG signals, which is then processed by a Convolution Neural Network (CNN) model to detect and classify the type of arrhythmia. This research article helps the reader to develop the technique capable of reading ECG signals and classifying the arrhythmia type, if it is detected in the ECG signal.