Yang Xuankai, Sun Jing, Yang Hongbo, Guo Tao, Pan Jiahua, Wang Weilian
School of Information Science and Engineering, Yunnan University, Kunming 650504, P. R. China.
Cardiovascular Hospital Affiliated to Kunming Medical University, Kunming 650102, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2024 Feb 25;41(1):41-50. doi: 10.7507/1001-5515.202304037.
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.
针对先天性心脏病相关性肺动脉高压临床听诊特征不明显以及现有机器辅助诊断算法复杂的问题，提出了一种基于第二心音信号高频成分统计特征的算法。首先，采用端点检测自适应分割方法提取第二心音。随后，利用离散小波变换对心音的高频成分进行分解。从该成分中提取包括赫斯特指数、莱姆佩尔-齐夫信息和样本熵在内的统计特征。最后，利用提取的特征训练极端梯度提升算法（XGBoost）分类器，该分类器在三分类中准确率达到80.45%。值得注意的是，该方法无需降噪算法，能够快速提取特征，仅用三个特征就实现了有效的多分类。它在先天性心脏病相关性肺动脉高压的早期筛查方面具有广阔前景。
