[Diagnosis of pulmonary hypertension associated with congenital heart disease based on statistical features of the second heart sound].
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi
; 41(1): 41-50, 2024 Feb 25.
Article
em Zh
| MEDLINE
| ID: mdl-38403603
ABSTRACT
Aiming at the problems of obscure clinical auscultation features of pulmonary hypertension associated with congenital heart disease and the complexity of existing machine-aided diagnostic algorithms, an algorithm based on the statistical characteristics of the high-frequency components of the second heart sound signal is proposed. Firstly, an endpoint detection adaptive segmentation method is employed to extract the second heart sounds. Subsequently, the high-frequency component of the heart sound is decomposed using the discrete wavelet transform. Statistical features including the Hurst exponent, Lempel-Ziv information and sample entropy are extracted from this component. Finally, the extracted features are utilized to train an extreme gradient boosting algorithm (XGBoost) classifier, which achieves an accuracy of 80.45% in triple classification. Notably, this method eliminates the need for a noise reduction algorithm, allows for swift feature extraction, and achieves effective multi-classification using only three features. It is promising for early screening of pulmonary hypertension associated with congenital heart disease.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Ruídos Cardíacos
/
Cardiopatias Congênitas
/
Hipertensão Pulmonar
Limite:
Humans
Idioma:
Zh
Revista:
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi
Assunto da revista:
ENGENHARIA BIOMEDICA
Ano de publicação:
2024
Tipo de documento:
Article