Segmentation of heart sound signals based on duration hidden Markov model / 生物医学工程学杂志
Journal of Biomedical Engineering
; (6): 765-774, 2020.
Article
em Zh
| WPRIM
| ID: wpr-879203
Biblioteca responsável:
WPRO
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
Palavras-chave
Texto completo:
1
Índice:
WPRIM
Assunto principal:
Algoritmos
/
Distribuição Normal
/
Cadeias de Markov
/
Ruídos Cardíacos
/
Eletrocardiografia
Tipo de estudo:
Health_economic_evaluation
Idioma:
Zh
Revista:
Journal of Biomedical Engineering
Ano de publicação:
2020
Tipo de documento:
Article