Diagnosis of pulmonary hypertension associated with congenital heart disease based on statistical features of the second heart sound_Journal of Biomedical Engineering

Authors：

YANG Xuankai ¹ , SUN Jing ¹ , YANG Hongbo ^2,3 , GUO Tao ^2,3 , PAN Jiahua ^2,3 ,  WANG Weilian ¹

1. School of Information Science and Engineering, Yunnan University, Kunming 650504, P. R. China;
2. Cardiovascular Hospital Affiliated to Kunming Medical University, Kunming 650102, P. R. China;
3. Fuwai Yunnan Cardiovascular Hospital, Kunming 650102, P. R. China;

Corresponding author：

WANG Weilian, Email: wlwang_47@126.com

Keywords：

Heart sound; Congenital heart disease; Pulmonary arterial hypertension; High-frequency components; Statistical features; Extreme gradient boosting algorithm

DOI：

10.7507/1001-5515.202304037

Video：

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Abstract Full text Figures/Tables Video References Cited by

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.

Citation： YANG Xuankai, SUN Jing, YANG Hongbo, GUO Tao, PAN Jiahua, WANG Weilian. Diagnosis of pulmonary hypertension associated with congenital heart disease based on statistical features of the second heart sound. Journal of Biomedical Engineering, 2024, 41(1): 41-50. doi: 10.7507/1001-5515.202304037 Copy

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2.	Abman S H, Mullen M P, Sleeper L A, et al. Characterisation of paediatric pulmonary hypertensive vascular disease from the PPHNet Registry. European Respiratory Journal, 2022, 59(1): 2003337.
3.	Alnajjar M K, Abu-naser S S. Heart sounds analysis and classification for cardiovascular diseases diagnosis using deep learning. International Journal of Academic Engineering Research, 2022, 6(1): 7-23.
4.	Dwivedi A K, Ranjan H, Menon A, et al. Noise reduction in ECG signal using combined ensemble empirical mode decomposition method with stationary wavelet transform. Circuits, Systems, and Signal Processing, 2021, 40: 827-844.
5.	Simonneau G, Montani D, Celermajer D S, et al. Haemodynamic definitions and updated clinical classification of pulmonary hypertension. European Respiratory Journal, 2019, 53(1): 1801913.
6.	Chen Y, Wei S, Zhang Y. Classification of heart sounds based on the combination of the modified frequency wavelet transform and convolutional neural network. Med Biol Eng Comput, 2020, 58(9): 2039-2047.
7.	Wang M, Wang J, Hu Y, et al. Detection of pulmonary hypertension with six training strategies based on deep learning technology. Computational Intelligence, 2022, 38(5): 1684-1706.
8.	Shukla S, Singh S K, Mitra D. An efficient heart sound segmentation approach using kurtosis and zero frequency filter features. Biomedical Signal Processing and Control, 2020, 57: 101762.
9.	Kui Haoran, Pan Jiahua, Zong Rong, et al. Heart sound classification based on log Mel-frequency spectral coefficients features and convolutional neural networks. Biomedical Signal Processing and Control, 2021, 69: 102893.
10.	Kaddoura T, Vadlamudi K, Kumar S, et al. Acoustic diagnosis of pulmonary hypertension: automated speech-recognition-inspired classification algorithm outperforms physicians. Scientific Reports, 2016, 6: 33182.
11.	Alqudah A M, Alquran H, Qasmieh I A. Classification of heart sound short records using bispectrum analysis approach images and deep learning. Network Modeling Analysis in Health Informatics and Bioinformatics, 2020, 9: 1-16.
12.	Ge B, Yang H, Ma P, et al. Detection of pulmonary arterial hypertension associated with congenital heart disease based on time–frequency domain and deep learning features. Biomedical Signal Processing and Control, 2023, 81: 104451.
13.	Juniati D, Khotimah C, Wardani D, et al. Fractal dimension to classify the heart sound recordings with KNN and fuzzy c-mean clustering methods//Proceedings of the Journal of Physics: Conference Series F, IOP Publishing, 2018, 953: 012202.
14.	Yildirim M. Automatic classification and diagnosis of heart valve diseases using heart sounds with MFCC and proposed deep model. Concurrency and Computation: Practice and Experience, 2022, 34(24): e7232.
15.	Mahesh Kumar S V, Dhinakar P, Nishanth R. Aortic stenosis detection using spectral statistical features of heart sound signals. EAI Endorsed Transactions on Pervasive Health and Technology, 2022, 8(4): e5.
16.	何沛光, 宋伟, 吴杰, 等. 基于分割的自适应特征提取诊断心音方法. 计算机科学与应用, 2021, 11(8): 2051-2063.
17.	Xu W, Yu K, Ye J, et al. Automatic pediatric congenital heart disease classification based on heart sound signal. Artificial Intelligence in Medicine, 2022, 126: 102257.
18.	Lahmiri S, Bekiros S. Complexity measures of high oscillations in phonocardiogram as biomarkers to distinguish between normal heart sound and pathological murmur. Chaos, Solitons & Fractals, 2022, 154: 111610.
19.	陈是扦, 彭志科, 周鹏. 信号分解及其在机械故障诊断中的应用研究综述. 机械工程学报, 2020, 56(17): 91-107.
20.	周克良, 刘亚亚. 新阈值小波变换的心音去噪. 计算机工程与设计, 2020, 41(9): 2476-2481.
21.	陈强强, 成建波, 张刚, 等. 短时间序列的复杂程度分析方法研究. 舰船电子工程, 2022, 42(4): 145-150.
22.	杨洋, 郭兴明, 郑伊能, 等. 基于ICEEMDAN-MSE的左室舒张功能障碍心音信号的识别研究. 仪器仪表学报, 2022, 43(1): 274-281.
23.	刘美君, 吴全玉, 丁胜, 等. 自适应噪声完备经验模态分解排列熵结合支持向量机的心音分类方法研究. 生物医学工程学杂志, 2022, 39(2): 311-319.
24.	李亚茹, 张宇来, 王佳晨. 面向超参数估计的贝叶斯优化方法综述. 计算机科学, 2022, 49(S1): 86-92.

1. Ruopp N F, Cockrill B A. Diagnosis and treatment of pulmonary arterial hypertension: a review. JAMA, 2022, 327(14): 1379-1391.
2. Abman S H, Mullen M P, Sleeper L A, et al. Characterisation of paediatric pulmonary hypertensive vascular disease from the PPHNet Registry. European Respiratory Journal, 2022, 59(1): 2003337.
3. Alnajjar M K, Abu-naser S S. Heart sounds analysis and classification for cardiovascular diseases diagnosis using deep learning. International Journal of Academic Engineering Research, 2022, 6(1): 7-23.
4. Dwivedi A K, Ranjan H, Menon A, et al. Noise reduction in ECG signal using combined ensemble empirical mode decomposition method with stationary wavelet transform. Circuits, Systems, and Signal Processing, 2021, 40: 827-844.
5. Simonneau G, Montani D, Celermajer D S, et al. Haemodynamic definitions and updated clinical classification of pulmonary hypertension. European Respiratory Journal, 2019, 53(1): 1801913.
6. Chen Y, Wei S, Zhang Y. Classification of heart sounds based on the combination of the modified frequency wavelet transform and convolutional neural network. Med Biol Eng Comput, 2020, 58(9): 2039-2047.
7. Wang M, Wang J, Hu Y, et al. Detection of pulmonary hypertension with six training strategies based on deep learning technology. Computational Intelligence, 2022, 38(5): 1684-1706.
8. Shukla S, Singh S K, Mitra D. An efficient heart sound segmentation approach using kurtosis and zero frequency filter features. Biomedical Signal Processing and Control, 2020, 57: 101762.
9. Kui Haoran, Pan Jiahua, Zong Rong, et al. Heart sound classification based on log Mel-frequency spectral coefficients features and convolutional neural networks. Biomedical Signal Processing and Control, 2021, 69: 102893.
10. Kaddoura T, Vadlamudi K, Kumar S, et al. Acoustic diagnosis of pulmonary hypertension: automated speech-recognition-inspired classification algorithm outperforms physicians. Scientific Reports, 2016, 6: 33182.
11. Alqudah A M, Alquran H, Qasmieh I A. Classification of heart sound short records using bispectrum analysis approach images and deep learning. Network Modeling Analysis in Health Informatics and Bioinformatics, 2020, 9: 1-16.
12. Ge B, Yang H, Ma P, et al. Detection of pulmonary arterial hypertension associated with congenital heart disease based on time–frequency domain and deep learning features. Biomedical Signal Processing and Control, 2023, 81: 104451.
13. Juniati D, Khotimah C, Wardani D, et al. Fractal dimension to classify the heart sound recordings with KNN and fuzzy c-mean clustering methods//Proceedings of the Journal of Physics: Conference Series F, IOP Publishing, 2018, 953: 012202.
14. Yildirim M. Automatic classification and diagnosis of heart valve diseases using heart sounds with MFCC and proposed deep model. Concurrency and Computation: Practice and Experience, 2022, 34(24): e7232.
15. Mahesh Kumar S V, Dhinakar P, Nishanth R. Aortic stenosis detection using spectral statistical features of heart sound signals. EAI Endorsed Transactions on Pervasive Health and Technology, 2022, 8(4): e5.
16. 何沛光, 宋伟, 吴杰, 等. 基于分割的自适应特征提取诊断心音方法. 计算机科学与应用, 2021, 11(8): 2051-2063.
17. Xu W, Yu K, Ye J, et al. Automatic pediatric congenital heart disease classification based on heart sound signal. Artificial Intelligence in Medicine, 2022, 126: 102257.
18. Lahmiri S, Bekiros S. Complexity measures of high oscillations in phonocardiogram as biomarkers to distinguish between normal heart sound and pathological murmur. Chaos, Solitons & Fractals, 2022, 154: 111610.
19. 陈是扦, 彭志科, 周鹏. 信号分解及其在机械故障诊断中的应用研究综述. 机械工程学报, 2020, 56(17): 91-107.
20. 周克良, 刘亚亚. 新阈值小波变换的心音去噪. 计算机工程与设计, 2020, 41(9): 2476-2481.
21. 陈强强, 成建波, 张刚, 等. 短时间序列的复杂程度分析方法研究. 舰船电子工程, 2022, 42(4): 145-150.
22. 杨洋, 郭兴明, 郑伊能, 等. 基于ICEEMDAN-MSE的左室舒张功能障碍心音信号的识别研究. 仪器仪表学报, 2022, 43(1): 274-281.
23. 刘美君, 吴全玉, 丁胜, 等. 自适应噪声完备经验模态分解排列熵结合支持向量机的心音分类方法研究. 生物医学工程学杂志, 2022, 39(2): 311-319.
24. 李亚茹, 张宇来, 王佳晨. 面向超参数估计的贝叶斯优化方法综述. 计算机科学, 2022, 49(S1): 86-92.

Journal of Biomedical Engineering

Diagnosis of pulmonary hypertension associated with congenital heart disease based on statistical features of the second heart sound

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