Signal detection based on atrial fibrillation detection algorithms using RR interval measurements

Abstract

Atrial Fibrillation (AF) is the most well-known type of heart disease, which can lead to consequences such as stroke, heart failure, and other health issues. Current methods involve performing large-area ablation without knowing the exact location of key parts. The technology's dependability can be used as a target for catheter ablation of atrial fibrillation. The goal of the study is to provide a method for detecting AF that may be utilised in medical practice as a screening tool. The essential objectives for the discovery strategy's configuration are to develop a MATLAB software program that can analyze the complexity of an ordinary ECG signal and an AF ECG signal. The Discrete Wavelet Transform (DWT) is utilized to preprocess the ECG signal. The R peaks and RR Interval of the ECG signal can currently accomplish this. In this study, detection of AF is based on the RR Interval Measurements which are coefficient of variance (CV) and normalised root mean square successive difference (nRMSSD). The threshold value for both RR Interval Measurements for detecting an AF signal is 0.1. As a result, 56.52% of the MIT-BIH Atrial Fibrillation Database and 31.81% of MIT-BIH Arrhythmia Database are identified as AF signals because these signals reach the threshold.