Heart sound segmentation algorithm based on hidden markov cardiac cycle
Cardiovascular disease is one of the most deadly diseases in the world,and its early screening and accurate diagnosis have important social value for reducing morbidity and mortality.As an important physiological signal of human body,phonocardiogram(PCG)contains rich cardiac pathological information,can objectively reflect the health status of heart and cardiovascular system,and is an important data source for diagnosis of cardiovascular diseases.Heart sound segmentation is the basis of heart sound signal pathological analysis,and plays a decisive role in the extraction of pathological features and disease diagnosis.However,prevailing heart sound segmentation algorithms often need to cooperate with the synchronous input ECG signal to achieve accurate segmentation effect,and the segmentation effect of pure heart sound signal is not good.Therefore,a heart sound segmentation algorithm based on Hidden Markov Cardiac Cycle(HMCC)is proposed to achieve accurate segmentation of pure PCG signals.Firstly,baseline calibration and wavelet denoising are used to realize signal preprocessing.Secondly,the heart sound envelope extraction based on Hilbert transform is proposed,and the corresponding relationship between peak value and S1 and S2 is located by combining with the law of cardiac cycle.An improved hidden Markov algorithm is further proposed to update the initial state distribution of heart sounds and optimize the Viterbi algorithm to calculate the duration of heart sound interval.Through the same benchmark of 244 heart sound data and comparative experiments at home and abroad,the segmentation positioning with accuracy score of 97.23％and segmentation accuracy rate of 97.32％has been achieved,providing a high-quality segmentation data source for PCG-based intelligent auxiliary diagnosis and analysis of cardiovascular diseases.
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