Automated Heart Arrhythmia Classification From Electrocardiographic Data Using Deep Neural Networks

Abstract

Arrhythmia is the medical condition where heart beats in an irregular pattern. Arrhythmia is one of the common sources of the Cardio Vascular diseases. To survive from the arrhythmia, the keys are early detection and timely treatment. ECG stands as a diagnostic tool for the detection of the arrhythmia. Human intervention to the ECG is error prone as well as tedious. With the help of the development of the technology, cost effective automated arrhythmia detection framework can be deployed. There are many machine learning as well as deep learning models which can effectively differentiate among various types of heartbeats. Various deep learning models has shown that there is an ease way for predicting arrhythmia which do not require feature engineering and is effective. In order to build automated heartbeat classification model several factors has to be considered which includes data quality, heartbeat segmentation range, data imbalance problem, intra and inter-patients variations and identification of supraventricular ectopic heartbeats from normal heartbeats. This thesis incorporates all of these challenges. In this method, a hybrid method of neural network was deployed. Features were extracted by the two CNNs having two filter sizes. RNN which is BiLSTM was used to classify the ECG signals. Dual channel CNN was used to extract both the temporal as well as frequency patterns. The extracted features were added with the RR information before giving the input to the RNN that are mainly done to classify between the S-type and N-type heartbeats. In particular, BiLSTM learns and extracts hidden temporal dependency between the heartbeats by processing the input RR interval sequence in both the directions. Instead of using raw individual RR-intervals, mutual-connected temporal information provides stronger and more stable support for identifying the S-type heartbeats. The loss used is the focal loss to handle the class imbalance. The results prove that the research of heartbeat classification presented in this thesis brings practical ideas and solutions to the arrhythmia detection. Accuracy of the model presented in this thesis was 93%.