ANN-based Classifier for PAF Prediction

Abstract

paper we propose an artificial neural network as a based classifier for prediction of Paroxysmal Atrial Fibrillation (PAF). PAF is a really life threatening disease and it is the result of irregular and repeated depolarization of the atria. We used PAF prediction data base which include 30-min. period of 100 ECG recorded signals. We divide the 30-min preceding the PAF into 6 periods with 5-min each. In each suggested period we get the classification result using ANN. The results show that we can predict the PAF accurately in 5-min & 20-min prior the PAF. In these two periods, the measured sensitivity, specificity, positive predictivity and accuracy show better and significant results comparable to the other periods. Also the results outperform the obtained results in the same field in the literature. 1. INRODUCTION Classification of ECG signal is an important area in biomedical signal processing. Paroxysmal atrial fibrillation (PAF) of the heart muscle is defined as short duration episodes of AF lasting from 2min. to less than 7 days, while chronic AF is defined as lasting more than 7 days. The main reason for this is not the immediate effect of the onset of atrial fibrillation over the patient's health (AF detection) but the long-term effects: increase in heart muscle fatigue, increase in thromboembolic and stroke events due to the formation of blood clots and an irregular onset that makes it hard to detect on normal ECG tests. Thus it is necessary for cardiologists to benefit from a robust and precise tool that could predict the onset of such events, in order to prevent them by defibrillation, drug treatment and anti- tachycardia pacing techniques. Chronic AF is usually preceded by (PAF). Therefore, in addition to use anti-arrhythmic drugs, the physicians are trying to develop pacing devices in order to surpass the onset of AF. The automated method to predict the onset PAF is interesting topic to help treating this problem. During recent years several researchers proposed many techniques to predict the onset of PAF. Useful reviews describing different techniques for PAF or chronic AF prediction, from technical to clinical points of view (1-4). The Computers in Cardiology Challenge 2001 revealed a maximum obtained accuracy of about 80% (5-7). Hariton, et al., (8) proposed a new method for PAF automatic prediction based on heart rate variability (HRV) metrics and morphologic variability (MV), and (HRV+MV) decision rule, the obtained specificity and sensitivity are between (83.93%- 89.29%), (84.51%-89,44%) respectively. Artificial neural network (ANN) in recent years has proved to be an advanced tool in solving classification (9-11), wavelets proved usefulness in feature extraction from non-stationary signal like ECG (12-13). In general, these above prediction models are able to detect the transition to PAF events with accuracies of 70-90%, by means of records of at least tens of minutes and rather complex analysis procedures. In the present work, two set of features are extracted: Feature set-1 (FS-1), directly from ECG signal and feature set-2 (FS-2) with the aid of continuous wavelet transform(CWT), which converts the time domain signal to time-frequency domain where several features can be carefully extracted, the extracted features are then applied to ANN to classify the normal object from that one who suffers from PAF.