An automatic detection method using Empirical Mode Decomposition and statistical parameters achieved 96% sensitivity for detecting atrial fibrillation in ECG signals from the MIT-BIH database.
Automatic detection of different cardiac abnormalities is an emerging field of study in assistive diagnosis technology for ca rdiac diseases. Atrial fibrillation (AF) is a kind of arrhythmia which increases risk of heart attack especially to the older people. Detection of AF at the early stage may cause prevention of serious stoke. This paper presents a method of automatic detection of AF by using higher order statistical moments of ECG signal in Empirical Mode Decomposition (EMD) domain. The proposed technique operates in two stages. First stage consists of decomposition of denoised ECG into intrinsic mode functions (IMF) and derives the statistical parameters like variance and standard deviation for classification from each IMF. In the second s tage these features are used by a supervised classifier to distinguish between normal and AF ECG rhythms. The performance of this method is tested with the MIT-BIH arrhythmia data base. Possibility to use different IMFs is also tested and 96% sensitivity is achieved in IMF 4.
Maji et al. (Tue,) studied this question.