Auxiliary Screening for Hypertrophic Cardiomyopathy With Heart Failure with Preserved Ejection Fraction Utilizing Smartphone-Acquired Heart Sound Analysis

DONG Xianpeng; MENG Xiangbin; ZHANG Kuo; FANG Guanchen; GAI Weihao; WANG Wenyao; WANG Jingjia; GAO Jun; PAN Junjun; TANG Zhenchao; SONG Zhen

doi:10.11999/JEIT250830

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DONG Xianpeng, MENG Xiangbin, ZHANG Kuo, FANG Guanchen, GAI Weihao, WANG Wenyao, WANG Jingjia, GAO Jun, PAN Junjun, TANG Zhenchao, SONG Zhen. Auxiliary Screening for Hypertrophic Cardiomyopathy With Heart Failure with Preserved Ejection Fraction Utilizing Smartphone-Acquired Heart Sound Analysis[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250830

Citation:

DONG Xianpeng, MENG Xiangbin, ZHANG Kuo, FANG Guanchen, GAI Weihao, WANG Wenyao, WANG Jingjia, GAO Jun, PAN Junjun, TANG Zhenchao, SONG Zhen. Auxiliary Screening for Hypertrophic Cardiomyopathy With Heart Failure with Preserved Ejection Fraction Utilizing Smartphone-Acquired Heart Sound Analysis[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250830

Citation:

DONG Xianpeng, MENG Xiangbin, ZHANG Kuo, FANG Guanchen, GAI Weihao, WANG Wenyao, WANG Jingjia, GAO Jun, PAN Junjun, TANG Zhenchao, SONG Zhen. Auxiliary Screening for Hypertrophic Cardiomyopathy With Heart Failure with Preserved Ejection Fraction Utilizing Smartphone-Acquired Heart Sound Analysis[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250830

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Auxiliary Screening for Hypertrophic Cardiomyopathy With Heart Failure with Preserved Ejection Fraction Utilizing Smartphone-Acquired Heart Sound Analysis

doi: 10.11999/JEIT250830 cstr: 32379.14.JEIT250830

1.
School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
2.
Department of Network Intelligence, Peng Cheng Laboratory, Shenzhen 518055, China
3.
Department of Cardiovascular Medicine, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100037, China
4.
School of Software and Microelectronics, Peking University, Beijing 102600, China
5.
Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
6.
Department of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Beijing 100191, China
7.
School of Computer Science and Engineering, Beihang University, Beijing 100191, China
8.
School of Engineering Medicine, Beihang University, Beijing 100191, China
9.
Key Laboratory of Big Data-Based Precision Medicine, Ministry of Industry and Information Technology, Beihang University, Beijing 100191, China

Funds: The National Natural Science Foundation of China(32571731, 82172067, 82272068), Beijing Municipal Natural Science Foundation(L256013), The Young Elite Scientists Sponsorship Program by CAST (2023QNRC001)

Received Date: 2025-09-01
Accepted Date: 2025-12-17
Rev Recd Date: 2025-12-16

Available Online: 2025-12-25

Abstract

Abstract

Objective Heart Failure with preserved Ejection Fraction (HFpEF) is highly prevalent among patients with Hypertrophic CardioMyopathy (HCM), and early identification is critical for improving disease management. However, early screening for HFpEF remains challenging because symptoms are non-specific, diagnostic procedures are complex, and follow-up costs are high. Smartphones, owing to their wide accessibility, low cost, and portability, provide a feasible means to support heart sound-based screening. In this study, smartphone-acquired heart sounds from patients with HCM are used to develop and train an ensemble learning classification model for early detection and dynamic self-monitoring of HFpEF in the HCM population. Methods The proposed HFpEF screening framework consists of three components: preprocessing, feature extraction, and model training and fusion based on ensemble learning (Fig. 1). During preprocessing, smartphone-acquired heart sounds are subjected to bandpass filtering and wavelet denoising to improve signal quality, followed by segmentation into individual cardiac cycles. For feature extraction, Mel-Frequency Cepstral Coefficients (MFCCs) and Short-Time Fourier Transform (STFT) time-frequency spectra are calculated (Fig. 3). For classification, a stacking ensemble strategy is applied. Base learners, including a Support Vector Machine (SVM) and a Convolutional Neural Network (CNN), are trained, and their predicted probabilities are combined to construct a new feature space. A Logistic Regression (LR) meta-learner is then trained on this feature space to identify HFpEF in patients with HCM. Results and Discussions The classification performance of the three models is evaluated using the same patient-level independent test set. The SVM base learner achieves an Area Under the Curve (AUC) of 0.800, with an accuracy of 0.766, sensitivity of 0.659, and specificity of 0.865 (Table 5). The CNN base learner attains an AUC of 0.850, with an accuracy of 0.789, sensitivity of 0.622, and specificity of 0.944 (Table 5). By comparison, the ensemble-based LR classifier demonstrates superior performance, reaching an AUC of 0.900, with an accuracy of 0.813, sensitivity of 0.768, and specificity of 0.854 (Table 5). Relative to the base learners, the ensemble model exhibits a significant overall performance improvement after probability-based feature fusion (Fig. 5). Compared with existing clinical HFpEF risk scores, the proposed method shows higher predictive performance and stronger dynamic monitoring capability, supporting its suitability for risk stratification and follow-up warning in home settings. Compared with professional heart sound acquisition devices, the smartphone-acquired approach provides greater accessibility and cost efficiency, supporting its application in auxiliary HFpEF screening for high-risk HCM populations. Conclusions The challenges of clinical HFpEF screening in patients with HCM are addressed by proposing a smartphone-acquired heart sound analysis approach combined with an ensemble learning prediction model, resulting in an accessible and easily implemented auxiliary screening pipeline. The effectiveness of smartphone-based heart sound analysis for initial HFpEF screening in patients with HCM is validated, demonstrating its feasibility as an economical auxiliary tool for early HFpEF detection. This approach provides a non-invasive, convenient, and efficient screening strategy for patients with HCM complicated by HFpEF.

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References(31)

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