A MULTIDIMENSIONAL AND MULTI-VIEW FEATURE FUSED HYBRID DEEP LEARNING MODEL FOR ARRHYTHMIA DETECTION FROM ELECTROCARDIOGRAM SIGNALS

Abstract

Arrhythmia is a highly prevalent chronic cardiac disorder in senior citizens and is related to the high severity including cardiovascular accidents, heart failure and myocardial ischemia. The ability to correctly detect and categorize arrhythmia rhythms from ECG readings is critical for lowering death rates.From this view-point, One Dimensional- Convolutional Neural Network (1DCNN) with priority model integrated voting mechanism is developed for arrhythmia classification. But, this model needs vast number of ECG signals and takes more time to train the model because of using cross-validation. Also, it lacks multidimensional and multi-view data abstraction which degrades the accuracy of recognizing arrhythmia from similar ECG signals. To resolve this, Arrhythmia Diseases Detection Network (ArddNet) model is proposed to recognize arrhythmia disorders efficiently from similar ECG signals. In this model, the ECG signal database is collected and pre-processed to remove the noisy signals. The noiseless ECG signals are classified into Regular (R), Supraventricular ectopic beat (S), Ventricular ectopic beat (V), Fusion beat (F) and Unknown beat (U) based on heart specialist labeling.Initially, the statistical and dynamic characteristics of the raw ECG signal are computed to obtain handcrafted features.Then,representation learning technique is utilized to identify time-invariant salient features using the pre-processed ECG signals. The sequence residual learning is used composed of 1DCNNand Variable Scale CNN (VSCNN) to capture the temporal features. The obtained handcrafted features and the deep features (time-invariant salient features and temporal features) are fed into the Bidirectional Long Sort Term Memory (BiLSTM) to get a new feature representation i.e., Multidimensional and Multi-view Feature Representation (MMFR) of the ECG signal. Moreover, this feature vector is fed to the softmax function for classifying arrhythmia and its types precisely. Finally, the experimental results illustrate that the ArddNet on MIT-BIH and Arrhythmia Data Set achieves an accuracy of 93.09% and 92.84%, respectively than the other classical deep learning models for arrhythmia identification.