Hybrid LSTM models-based detection of left ventricular hypertrophy in electrocardiogram signals

Abstract

This research investigates various deep learning techniques to automatically classify Left Ventricular Hypertrophy (LVH) from electrocardiogram (ECG) signals. LVH frequently results from persistently high blood pressure, causing the heart pump harder and thicken the ventricular walls. It is associated with an increased risk of heart attacks, heart failure, stroke, and sudden cardiac death. The significance of this research lies in the early and precise detection of LVH, facilitating timely interventions and ultimately improving patient health. The non-invasive nature of ECG monitoring, integrated with the efficiency of deep learning models, contributes to faster and more accessible to enhance diagnostic accuracy and efficiency in identifying LVH. The objective of this research is to assess and compare the performance of GRU3Net, Double-Bilayer LSTM, and Conv2LSTM, Dual-LSTM models in the classification of Left Ventricular Hypertrophy (LVH) based on electrocardiogram (ECG) signals, utilizing a dataset sourced from the PTB Diagnostic ECG Database. The implemented deep learning models yielded noteworthy results. Specifically, the GRU3Net model achieved a high accuracy of 96.1%, showcasing an optimal configuration for overall accuracy. The Double-Bilayer LSTM model followed with an accuracy of 91.7%. However, a decline in accuracy was observed in both the Dual-LSTM and Conv2LSTM models, with the former registering an accuracy of 90.8% and the latter decreasing further to 87.3%.