Automatic classification of heartbeats

We report improvement in the detection of a class of heart arrhythmias based on electrocardiogram signals (ECG). The detection is performed using a 4 dimensional feature vector obtained by applying an iterative feature selection method used in conjunction with artificial neural networks. The feature set includes the pre-RR interval, which is a primary measure that cardiologists use in a clinical setting. A transformation applied to the pre-RR interval reduced the false positive rate. Our solution as opposed to existing literature does not rely on high-dimensional features such as wavelets, signal amplitudes which do not have direct relationship to heart function and difficult to interpret. Also we avoid obtaining patient specific labeled recordings. Furthermore, we propose semi-parametric classifiers as opposed to restrictive parametric linear discriminant analysis and its variants, which are a mainstay in ECG classification. Extensive experiments from the MIT-BIH databases demonstrate superior performance by our methods.