Detection of Atrial Flutter using PRSA

Abstract

Automatic detection of different cardiac abnormalities is an emerging field of study in assistive diagnosis technology for cardiac diseases. A study on the feasibility of automatic detection of Atrial Flutter (AFL) based on time and frequency domain features has been presented in this paper to prevent the serious heart failure by detecting it at early stage. The proposed algorithm is developed based on feature subsets of a set of statistical time-frequency-domain parameters by using phase rectified signal average (PRSA) method. Classification of the abnormality using the derived features has been performed with the help of two class clustering method by Support Vector Machine (SVM). This classifier is tested on 382 and 587 numbers of AFL and normal cardiac cycles respectively taken from MIT-BIH Arrhythmia database. Satisfactory result is obtained as the 96% sensitivity and 98% specificity is observed.