ECG segmentation in a body sensor network using Hidden Markov Models

A novel approach for segmenting ECG signal in a body sensor network employing hidden Markov modeling (HMM) technique is presented. The parameter adaptation in traditional HMM methods is conservative and slow to respond to these beat interval changes. Inadequate and slow parameter adaptation is largely responsible for the low positive predictivity rate. To solve the problem, we introduce an active HMM parameter adaptation and ECG segmentation algorithm. Body sensor networks are used to pre-segment the raw ECG data by performing QRS detection. Instead of one single generic HMM, multiple individualized HMMs are used. Each HMM is only responsible for extracting the characteristic waveforms of the ECG signals with similar temporal features from the same group, so that the temporal parameter adaptation can be naturally achieved.