[Detection method of early heart valve diseases based on heart sound features].
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi
; 40(6): 1160-1167, 2023 Dec 25.
Article
en Zh
| MEDLINE
| ID: mdl-38151939
ABSTRACT
Heart valve disease (HVD) is one of the common cardiovascular diseases. Heart sound is an important physiological signal for diagnosing HVDs. This paper proposed a model based on combination of basic component features and envelope autocorrelation features to detect early HVDs. Initially, heart sound signals lasting 5 minutes were denoised by empirical mode decomposition (EMD) algorithm and segmented. Then the basic component features and envelope autocorrelation features of heart sound segments were extracted to construct heart sound feature set. Then the max-relevance and min-redundancy (MRMR) algorithm was utilized to select the optimal mixed feature subset. Finally, decision tree, support vector machine (SVM) and k-nearest neighbor (KNN) classifiers were trained to detect the early HVDs from the normal heart sounds and obtained the best accuracy of 99.9% in clinical database. Normal valve, abnormal semilunar valve and abnormal atrioventricular valve heart sounds were classified and the best accuracy was 99.8%. Moreover, normal valve, single-valve abnormal and multi-valve abnormal heart sounds were classified and the best accuracy was 98.2%. In public database, this method also obtained the good overall accuracy. The result demonstrated this proposed method had important value for the clinical diagnosis of early HVDs.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Ruidos Cardíacos
/
Enfermedades de las Válvulas Cardíacas
Límite:
Humans
Idioma:
Zh
Revista:
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi
Asunto de la revista:
ENGENHARIA BIOMEDICA
Año:
2023
Tipo del documento:
Article