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
en Zh
| WPRIM
| ID: wpr-879203
Biblioteca responsable:
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
Palabras clave
Texto completo:
1
Base de datos:
WPRIM
Asunto principal:
Algoritmos
/
Distribución Normal
/
Cadenas de Markov
/
Ruidos Cardíacos
/
Electrocardiografía
Tipo de estudio:
Health_economic_evaluation
Idioma:
Zh
Revista:
Journal of Biomedical Engineering
Año:
2020
Tipo del documento:
Article