A Hybrid CNN-LSTM Architecture for Detection of Coronary Artery Disease from ECG
Rohan Banerjee, Avik Ghose, Kayapanda Mandana
Abstract
Coronary Artery Disease (CAD) causes significant global mortality. The recent development in artificial intelligence shows the feasibility of early non-invasive screening of several life- threatening cardiovascular diseases. However, such approaches have been less prolific in diagnosis of CAD due to lack of clinically known definite bio-marker. In this paper, we propose a novel neural network architecture that effectively combines two non-specific CAD markers, 1) anomalous morphology of Electrocardiogram (ECG) waveform and 2) abnormal Heart Rate Variability (HRV). A Convolutional Neural Network (CNN) structure is defined for extraction of morphological ECG features. Another composite structure is defined based on Long Short-Term Memory (LSTM) and a set of hand crafted statistical features for measuring the extent of HRV. The two independent bio-markers are subsequently combined in a hybrid CNN-LSTM architecture for classification of CAD. The proposed approach is evaluated on two datasets, a corpus, selected from the MIMIC II waveform dataset and a partially noisy in-house dataset, recorded using a low-cost ECG sensor. Results show that overall classification accuracy of 93% and 88% are achieved on the two datasets, which outperform the existing approaches.