Segmentation of heart sound signals based on duration hidden Markov model.
10.7507/1001-5515.201911061
- Author:
Haoran KUI
1
;
Jiahua PAN
2
;
Rong ZONG
1
;
Hongbo YANG
2
;
Wei SU
1
;
Weilian WANG
1
Author Information
1. School of Information Science and Engineering, Yunnan University, Kunming 650504, P.R.China.
2. Yunnan Fuwai Cardiovascular Disease Hospital, Kunming 650102, P.R.China.
- Publication Type:Journal Article
- Keywords:
Gaussian mixture distribution;
Viterbi algorithm;
autocorrelation estimation;
duration hidden Markov model;
heart sound segmentation
- MeSH:
Algorithms;
Electrocardiography;
Heart Sounds;
Markov Chains;
Normal Distribution
- From:
Journal of Biomedical Engineering
2020;37(5):765-774
- CountryChina
- Language:Chinese
-
Abstract:
Heart sound segmentation is a key step before heart sound classification. It refers to the processing of the acquired heart sound signal that separates the cardiac cycle into systolic and diastolic, etc. To solve the accuracy limitation of heart sound segmentation without relying on electrocardiogram, an algorithm based on the duration hidden Markov model (DHMM) was proposed. Firstly, the heart sound samples were positionally labeled. Then autocorrelation estimation method was used to estimate cardiac cycle duration, and Gaussian mixture distribution was used to model the duration of sample-state. Next, the hidden Markov model (HMM) was optimized in the training set and the DHMM was established. Finally, the Viterbi algorithm was used to track back the state of heart sounds to obtain S
