Auxiliary Screening for HCM with HFpEF 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 HCM with HFpEF 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 HCM with HFpEF 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 HCM with HFpEF Utilizing Smartphone-Acquired Heart Sound Analysis[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250830

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Auxiliary Screening for HCM with HFpEF 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), Young Elite Scientists Sponsorship Program by CAST (2023QNRC001)

Received Date: 2025-08-29
Accepted Date: 2025-12-17
Rev Recd Date: 2025-11-17

Available Online: 2025-12-25

Abstract

Abstract

Objective Heart failure with preserved ejection fraction (HFpEF) is highly prevalent in patients with hypertrophic cardiomyopathy (HCM), and its early identification is critical for improving disease management. However, early screening of HFpEF is challenging due to non-specific symptoms, complex diagnostic procedures, and high follow-up costs. Smartphones, with their wide accessibility, low cost, and portability, offer a feasible approach for assisting cardiac sound-based screening. This study utilizes smartphone-recorded heart sounds from HCM patients to develop and train an ensemble learning classification model, aiming to achieve early detection and dynamic self-monitoring of HFpEF in the HCM population. Methods The proposed HFpEF screening framework consists of three main components: preprocessing, feature extraction, and model training and fusion (ensemble learning) (Fig. 1). In the preprocessing stage, heart sounds collected via smartphones undergo bandpass filtering and wavelet denoising to ensure signal quality, followed by segmentation into individual cardiac cycles. During feature extraction, Mel-frequency cepstral coefficients (MFCCs) and short-time Fourier transform (STFT) time-frequency spectra are computed (Fig. 3). For classification, a Stacking ensemble strategy is adopted: base learners including a support vector machine (SVM) and a convolutional neural network (CNN) are trained, and their predicted probabilities are combined to form a new feature space. A logistic regression (LR) meta learner is then trained on these features to identify HFpEF in HCM patients. Results and Discussions The classification results of the three models on the same patient-level independent test set are as follows. The SVM base learner achieved an AUC of 0.800, with an accuracy, sensitivity, and specificity of 0.766, 0.659, and 0.865, respectively (Table 5). The CNN base learner attained an AUC of 0.850, with corresponding metrics of 0.789, 0.622, and 0.944 (Table 5). In contrast, the ensemble-based LR classifier demonstrated superior performance, reaching an AUC of 0.900, with accuracy, sensitivity, and specificity of 0.813, 0.768, and 0.854, respectively (Table 5). Compared to the base learners, the ensemble model shows significantly improved overall performance after probability-based feature fusion (Fig. 5). When compared to existing clinical HFpEF risk scores, the proposed method exhibits superior predictive performance and stronger dynamic monitoring capability, making it particularly suitable for risk stratification and follow-up warning in home settings. Relative to professional heart sound acquisition devices, the smartphone-based approach offers greater accessibility and cost-effectiveness, proving more applicable for auxiliary HFpEF screening in high-risk HCM populations. Conclusions This study addresses the challenges in clinical screening of HFpEF in HCM patients by proposing a smartphone-based heart sound acquisition system combined with an ensemble learning prediction model, resulting in a highly accessible and easily implementable auxiliary screening pipeline. This work is the first to validate the effectiveness of a smartphone-based heart sound analysis method and its feasibility for initial HFpEF screening in HCM patients. It has the potential to serve as an economical auxiliary tool for early HFpEF detection, providing a non-invasive, convenient, and efficient screening strategy for HCM patients with comorbid HFpEF.

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References

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