Efficient algorithm for classification of electrocardiogram beats based on artificial bee colony-based least-squares support vector machines classifier

Automated electrocardiogram (ECG) beat classification is an important component of heart monitoring systems used for raising an emergency call during sudden cardiac disorder of patients. An automatic ECG beat classifier is proposed to exploit the bandwidth features that are exclusively extracted from analytic intrinsic mode functions (IMFs). The proposed methodology employs artificial bee colony (ABC) algorithm-based least-squares support vector machines (LSSVM) classifier to classify ECG beat types using radial basis function kernel. Simulation results illustrate that proposed classifier gives the best results with second IMF. This novelty lies in its unique combination of ABC with LSSVM classifier that efficiently exploits bandwidth features for automatic classification of ECG beats.