Classification of electrocardiogram (ECG) data using deep learning methods

Abstract

Classification is one of the most widely used techniques in healthcare, especially concerning diagnosing cardiac disorders. Arrhythmia is a disorder of the heartbeat rate or rhythm, which may occur sporadically in daily life. Electrocardiogram (ECG) is an important diagnostic tool for analysing cardiac tissues and structures. It includes information about the heart structure and the function of its electrical conduction system. Since manual analysis of heartbeat rate is time-consuming and prone to errors, automatic recognition of arrhythmias using ECG signals has become an increasingly popular research focus in recent years. Current ECG analysis systems in literature generally have implemented well known machine learning algorithms. Due to the advent of powerful parallel computing hardware and the big data technologies, deep learning has also become a widely preferred technique in healthcare applications. In our study, we use ECG data in MIT–BIH Arrhythmia Database to develop a Convolutional Neural Networks (CNN) which is a deep feed-forward neural network type. The parameter tuned/optimized version of the proposed algorithm on top of the reduced feature dimension is more efficient than state of the art in terms of accuracy. Finally, we also compare the results of the proposed algorithm with Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and we provide the corresponding results in related sections.