ECG Heartbeat Classification Using a Single Layer LSTM Model

Abstract

Cardiovascular diseases (CVDs) are the number one cause of death today. Therefore, the early detection of arrhythmia is very important for cardiac patients. This paper proposes the heartbeat classification algorithm using the electrocardiogram(ECG) signals. An ECG is a 1D signal that is the result of recording the electrical activity of the heart using an electrode. In this paper, a single-layer Tensorflow LSTM model has been proposed to classify a biological time-series consisting of normal and abnormal heartbeats. The method was evaluated using the publicly available Physio net's MIT-BIH Arrhythmia dataset. The dataset has been divided into training and testing data. As a result, the classifier achieved a 95% average accuracy. Compared with the other CNN and RNN models used for the heartbeat classification, the simulation result shows the proposed algorithm has higher accuracy.