Automatic continuous ECG monitoring system for over-drug detection in Brugada Syndrome

This paper is concerned with the automatic control of drug administration in patients suffering from Brugada Syndrome (BS). Drugs such as flecainide, procainamide, ajmaline and pilsicainide should be administrated under carefully controlled electrocardiogram (ECG) monitoring given that the treatment must be stopped if some ECG disturbing conditions appear. These conditions are, among others the development of premature ventricular contraction (PVC), atrial fibrillation (AF) and the widening of the QRS wave. The proposed system can detect these abnormalities by using a pattern recognition approach based on Hidden Markov Models (HMM) with features extracted from three scales of the Wavelet Transform (WT). Performances higher than 98% were reached regarding the classification of normal and abnormal pulses. The system was trained and tested mainly in data from the standard MIT-BIH arrhythmia database.