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Updated: May 23, 2025

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program
Published on: April 19, 2019
Practicality meets precision: Wearable vest with integrated multi-channel PCG sensors for effective coronary artery
Matthew Fynn1, Kayapanda Mandana2, Javed Rashid2
1School of Electrical Engineering, Computing and Mathematical Sciences (EECMS), Faculty of Science and Engineering, Curtin University, Bentley, 6102, WA, Australia; Department of Electronics & Electrical Communication Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721302, West Bengal, India.
Insights
A new wearable vest system simplifies cardiovascular disease (CVD) screening using phonocardiogram (PCG) signals. This non-invasive method offers a convenient, rapid, and clinically significant approach for early detection and monitoring of coronary artery disease (CAD).
Area of Science:
- Biomedical Engineering
- Cardiology
- Signal Processing
Background:
- Cardiovascular disease (CVD), particularly coronary artery disease (CAD), is a leading global cause of mortality.
- Early detection of CAD is critical as myocardial infarction or stroke can be the first manifestation.
- Current screening methods for CAD lack cost-effectiveness, non-invasiveness, and ease of use for mass screening.
Purpose of the Study:
- To develop and validate a novel, convenient, and non-invasive data acquisition system (DAQS) for pre-screening coronary artery disease (CAD).
- To assess the feasibility of using multi-channel phonocardiogram (PCG) signals acquired via a wearable vest for CAD classification.
- To evaluate a machine learning approach for distinguishing between normal and CAD-affected heartbeats using PCG features.
Main Methods:
- A novel wearable vest incorporating multi-channel PCG sensors was developed for rapid (under two minutes) and simple signal acquisition.
- Seven PCG signals were acquired, including six from the chest and one from the back.
- Linear-frequency cepstral coefficients (LFCC) were extracted as features, and a support vector machine (SVM) was employed for classification, utilizing feature-level fusion of multiple channels.
Main Results:
- The proposed system achieved a subject-level accuracy of 80.44% and an F1-score of 81.00% using optimal feature-level fusion of multiple PCG channels.
- This performance represents a 7% improvement over the best-performing single channel.
- The system demonstrated clinically significant performance metrics, indicating suitability for practical application and post-procedural monitoring.
Conclusions:
- The novel DAQS offers a simple, convenient, and rapid method for mass screening of cardiovascular disease (CVD).
- The PCG-based classification using LFCC features and SVM with feature fusion shows significant potential for early CAD detection.
- The system is promising for both initial screening and post-intervention monitoring, including the detection of restenosis following procedures like PTCA or CABG.
Abstract:
The leading cause of mortality and morbidity worldwide is cardiovascular disease (CVD), with coronary artery disease (CAD) being the largest sub-category. Unfortunately, myocardial infarction or stroke can manifest as the first symptom of CAD, underscoring the crucial importance of early disease detection. Hence, there is a global need for a cost-effective, non-invasive, reliable, and easy-to-use system to pre-screen CAD. Previous studies have explored weak murmurs arising from CAD for classification using phonocardiogram (PCG) signals. However, these studies often involve tedious and inconvenient data collection methods, requiring precise subject preparation and environmental conditions. This study proposes using a novel data acquisition system (DAQS) designed for simplicity and convenience. The DAQS incorporates multi-channel PCG sensors into a wearable vest. The entire signal acquisition process can be completed in under two minutes, from fitting the vest to recording signals and removing it, requiring no specialist training. This exemplifies the potential for mass screening, which is impractical with current state-of-the-art protocols. Seven PCG signals are acquired, six from the chest and one from the subject's back, marking a novel approach. Our classification approach, which utilizes linear-frequency cepstral coefficients (LFCC) as features and employs a support vector machine (SVM) to distinguish between normal and CAD-affected heartbeats, outperformed alternative low-computational methods suitable for portable applications. Utilizing feature-level fusion, multiple channels are combined, and the optimal combination yields the highest subject-level accuracy and F1-score of 80.44% and 81.00%, respectively, representing a 7% improvement over the best-performing single channel. The proposed system's performance metrics have been demonstrated to be clinically significant, making the DAQS suitable for practical use. Moreover, the system shows promise in post-procedural monitoring for subjects undergoing percutaneous transluminal coronary angioplasty (PTCA) or coronary artery bypass grafting (CABG), effectively identifying cases of restenosis following intervention.
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