Atrial Fibrillation detection using Discrete Wavelet Transform

Abstract

Many heart diseases can be identified and cured at very early stage by studying the changes in the features of electrocardiogram (ECG) signal. In this paper, an algorithm to detect Atrial Fibrillation (AF) in the ECG signal is developed. For correct detection of AF, pre-processing and feature extraction of ECG signal shall be performed before it detects AF. After considering the ECG signal from the database, in the preprocessing stage de-noising of the ECG signal is carried out in order to obtain a clean ECG signal. After pre-processing, before feature extraction R peak detection is carried out for the signal. Since R peak is having the highest amplitude, and therefore it is detected in the first round and subsequently location of other peaks of the ECG signals are performed. After completing preprocessing and feature extraction using Discrete Wavelet Transform (DWT) applied based on inverted T wave logic and ST-segment elevation. Our classification algorithm was demonstrated to successfully acquire, analyze and interpret ECGs for the presence of AF indicating its potential to support m-Health diagnosis, monitoring, and management of therapy in AF patients.