Next Article in Journal
Development of 3D-Stacked 1Megapixel Dual-Time-Gated SPAD Image Sensor with Simultaneous Dual Image Output Architecture for Efficient Sensor Fusion
Previous Article in Journal
Context-Aware Alerting in Elderly Care Facilities: A Hybrid Framework Integrating LLM Reasoning with Rule-Based Logic
Previous Article in Special Issue
Sit-and-Reach Pose Detection Based on Self-Train Method and Ghost-ST-GCN
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sensors
Volume 25
Issue 21
10.3390/s25216562
sensors-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

A Robust and Efficient Workflow for Heart Valve Disease Detection from PCG Signals: Integrating WCNN, MFCC Optimization, and Signal Quality Evaluation

by
Shin-Chi Lai
1,2,
Yen-Ching Chang
3,
Ying-Hsiu Hung
4,
Szu-Ting Wang
5,
Yao-Feng Liang
4,
Li-Chuan Hsu
3,
Ming-Hwa Sheu
3,* and
Chuan-Yu Chang
6
1
Department of Automation Engineering, National Formosa University, Yunlin 632301, Taiwan
2
Smart Machinery and Intelligent Manufacturing Research Center, National Formosa University, Yunlin 632301, Taiwan
3
Department of Electronics Engineering, National Yunlin University of Science and Technology, Yunlin 64002, Taiwan
4
Doctor’s Program of Smart Industry Technology Research and Design, National Formosa University, Yunlin 632301, Taiwan
5
Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung 413310, Taiwan
6
Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin 64002, Taiwan
*
Author to whom correspondence should be addressed.
Sensors 2025, 25(21), 6562; https://doi.org/10.3390/s25216562 (registering DOI)
Submission received: 31 August 2025 / Revised: 20 October 2025 / Accepted: 22 October 2025 / Published: 24 October 2025
(This article belongs to the Special Issue AI-Based Automated Recognition and Detection in Healthcare)
Versions Notes

Abstract

This study proposes a comprehensive and computationally efficient system for the recognition of heart valve diseases (HVDs) in phonocardiogram (PCG) signals, emphasizing an end-to-end workflow suitable for real-world deployment. The core of the system is a lightweight weighted convolutional neural network (WCNN) featuring a key weighting calculation (KWC) layer, which enhances noise robustness by adaptively weighting feature map channels based on global average pooling. The proposed system incorporates optimized feature extraction using Mel-frequency cepstral coefficients (MFCCs) guided by GradCAM, and a band energy ratio (BER) metric to assess signal quality, showing that lower BER values are associated with higher misclassification rates due to noise. Experimental results demonstrated classification accuracies of 99.6% and 90.74% on the GitHub PCG and PhysioNet/CinC Challenge 2016 databases, respectively, where the models were trained and tested independently. The proposed model achieved superior accuracy using significantly fewer parameters (312,357) and lower computational cost (4.5 M FLOPs) compared with previously published research. Compared with the model proposed by Karhade et al., the proposed model use 74.9% fewer parameters and 99.3% fewer FLOPs. Furthermore, the proposed model was implemented on a Raspberry Pi, achieving real-time HVDs detection with a detection time of only 1.87 ms for a 1.4 s signal.
Keywords: convolution neural network (CNN); deep learning; heart valve diseases (HVDs); mel-frequency cepstral coefficient (MFCC); phonocardiogram (PCG) signal convolution neural network (CNN); deep learning; heart valve diseases (HVDs); mel-frequency cepstral coefficient (MFCC); phonocardiogram (PCG) signal

Share and Cite

MDPI and ACS Style

Lai, S.-C.; Chang, Y.-C.; Hung, Y.-H.; Wang, S.-T.; Liang, Y.-F.; Hsu, L.-C.; Sheu, M.-H.; Chang, C.-Y. A Robust and Efficient Workflow for Heart Valve Disease Detection from PCG Signals: Integrating WCNN, MFCC Optimization, and Signal Quality Evaluation. Sensors 2025, 25, 6562. https://doi.org/10.3390/s25216562

AMA Style

Lai S-C, Chang Y-C, Hung Y-H, Wang S-T, Liang Y-F, Hsu L-C, Sheu M-H, Chang C-Y. A Robust and Efficient Workflow for Heart Valve Disease Detection from PCG Signals: Integrating WCNN, MFCC Optimization, and Signal Quality Evaluation. Sensors. 2025; 25(21):6562. https://doi.org/10.3390/s25216562

Chicago/Turabian Style

Lai, Shin-Chi, Yen-Ching Chang, Ying-Hsiu Hung, Szu-Ting Wang, Yao-Feng Liang, Li-Chuan Hsu, Ming-Hwa Sheu, and Chuan-Yu Chang. 2025. "A Robust and Efficient Workflow for Heart Valve Disease Detection from PCG Signals: Integrating WCNN, MFCC Optimization, and Signal Quality Evaluation" Sensors 25, no. 21: 6562. https://doi.org/10.3390/s25216562

APA Style

Lai, S.-C., Chang, Y.-C., Hung, Y.-H., Wang, S.-T., Liang, Y.-F., Hsu, L.-C., Sheu, M.-H., & Chang, C.-Y. (2025). A Robust and Efficient Workflow for Heart Valve Disease Detection from PCG Signals: Integrating WCNN, MFCC Optimization, and Signal Quality Evaluation. Sensors, 25(21), 6562. https://doi.org/10.3390/s25216562

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop