ID163_Atrial features-based prediction of sinus tachycardia using LSTM-RNN model

Abstract

Sinus Tachycardia (ST) reveals pathological dysfunctions and differentiates distinct arrhythmias. The progression of Atrial Fibrillation (AF) from paroxysmal to persistent is frequently associated with tachycardias. Moreover, the most effective AF detection methods suggest incorporating both atrial and R-R interval features. Therefore, the study aims to use the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) model to investigate the influence of atrial characteristics on predicting tachycardia. Electrocardiograms (ECGs) from 10 healthy volunteers 26 ± 3.4 years (4 females) were recorded for Sinus Rhythm (SR) and ST conditions along with 10 AF data. For ST, the 5-day follow-up recording was performed with each volunteer. ECG recordings were performed for a duration of 10 s. Atrial features, along with R-R interval and Heart Rate (HR), were utilized as inputs for the developed LSTM-RNN multivariate time series forecasting model. The features were statistically analyzed before training the LSTM-RNN model. The correlation is positive and significant between HR and atrial amplitude (p