Ergodic Hidden Markov Model을 이용한 연속심음분류에 관한 연구
Classification of Continuous Heart Sound Signals Using the Ergodic Hidden Markov Model
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Abstract
Recently, hidden Markov models (HMMs) have been found to be very effective in classifying heart sound signals. For the classification based on the HMM, the continuous cyclic heart sound signal needs to be manually segmented to obtain isolated cycles of the signal. However, the manual segmentation will be practically inadequate in real environments. Although, there have been some research efforts for the automatic segmentation, the segmentation errors seem to be inevitable and will result in performance degradation in the classification. To solve the problem of the segmentation, we propose to use the ergodic HMM for the classification of the continuous heart sound signal. In the classification experiments, the proposed method performed successfully with an accuracy of about 99(%) requiring no segmentation information. (Journal of Korean Society of Medical Informatics 13-1,35-41, 2007)
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