Hidden markov model for the heart rate variability detection

Abstract

The signal processing is the approach which is applied to process digital signal data. The Electrocardiogram signals are applied to process the heart rate values. The heart rate variability detect has the three phases which are pre-processing, feature extraction and classification. In the previous approach, SVM classifier is applied for the heart rate variability detection. In this research work, Hidden Markov Model classifier is applied for the heart rate variability detection. The performance of proposed model is analyzed in terms of accuracy and execution time. The proposed algorithm improve result upto 8% as compared to existing approach in terms of accuracy.