A Deep Learning Approach for Diagnosing Long QT Syndrome Without Measuring QT Interval

For decades, ECG segmentation and QT interval measurement have been two fundamental steps in ECG-based diagnosis of the long QT syndrome (LQTS). However, due to the subjective nature of the definition of Q and T wave boundaries and confusion with an adjacent U wave, it suffers from a high degree of inter-and intra-analyst variability. In this paper, without measuring the QT interval and extracting the ECG waves, we propose a convolutional neural network which receives the raw ECG signal, and classifies every heartbeat as Normal or LQTS. The network is trained using a dataset of genotype-positive LQTS, and genotype-negative normal ECGs of family relatives. Experimental results reveal a high accuracy in diagnosing LQTS non-invasively, with a very low computational complexity, guaranteeing the clinical application of the proposed method.