Diagnosis of left atrial and left ventricular hypertrophies using a deep neural network

This paper presents the results of the application of a convolutional neural network to diagnose left atrial and left ventricular hypertrophies by analyzing 12-lead electrocardiograms (ECG). During the study, a new unique dataset containing 64 thousand ECG records was collected and processed. Labels for the two classes under consideration, left ventricular hypertrophy and left atrial hypertrophy, were generated from the accompanying medical reports. A set of signals and obtained labels were used to train a deep convolutional neural network with residual blocks; the resulting model is capable of detecting left ventricular hypertrophy with F-score more than 0.82 and left atrial hypertrophy with F1-score over 0.78. In addition, the search for optimal neural network architecture was carried out and the experimental evaluation of the effect of including patient metadata into the model and signal preprocessing was conducted. Besides, the paper provides a comparative analysis of the difficulty of detecting left ventricular and left atrial hypertrophies in relation to the other two frequently occurring heart activity disorders, namely atrial fibrillation and left bundle branch block.