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Probability hypothesis density (PHD) filtering, implemented using particle filters, is a Bayesian technique used to non-linearly track multiple objects. In this paper, we propose a new approach based on PHD particle filters (PHD-PF) to automatically track the number of magnetoencephalography (MEG) neural dipole sources and their unknown states. In(More)
Constructing statistical models of electrocardiogram (ECG) signals, whose parameters can be used for automated disease classification, is of great importance in precluding manual annotation and providing prompt diagnosis of cardiac diseases. ECG signals consist of several segments with different morphologies (namely the P wave, QRS complex and the T wave)(More)
The automatic classification of electrocardiogram (ECG) signals is of great clinical significance in eliminating the strenuous process of manually annotating ECG recordings. Although statistical models describing ECG signal dynamics currently exist, they depend considerably on a priori information and user-specified model parameters. Also, ECG beat(More)