A Hybrid CNN-LSTM Architecture for Detection of Coronary Artery Disease from ECG

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.