Heart Rate Variability Analysis Method Based on KNN Classification
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Complex fluctuations of heart rate variability (HRV) reflect the autonomic regulation of the heart. At present,the characteristics of different HRV signals are not obvious and the classification accuracy is not high which restricts their practical use. Therefore, this paper proposes a signal pattern recognition algorithm based on KNN classification for HRV signal analysis and recognition. HRV characteristic matrix is formed by HRV characteristic values in frequency domain which is extracted by AR spectrum analysis, time domain characteristic values and nonlinear characteristic values. Then the matrix is processed by the K-nearest neighbor classification algorithm. In order to verify the accuracy of the algorithm, four types of ECG data in MIT-BIH database are used to train and detect the algorithm in this paper. The proposed algorithm’s accuracy of HRV classification and recognition can be up to 93.5%, which is higher than other classification recognition algorithms of the same type. These results provide a new method for extraction and classification the eigenvalues of heart rate variability.
KeywordsHeart rate variability AR spectrum analysis K-neighbor classification algorithm
This research was supported by innovation team Science foundation of Engineering University of PAP (KYTD201905).
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