Key result
Wearable multichannel PCG vest achieves ~82% accuracy in differentiating CAD, VHD, and normal heartbeats.
Why the study?
There is a global need for an effective non-invasive screening tool capable of detecting various cardiovascular diseases, such as CAD and VHD, on a mass scale.
Does a wearable multichannel PCG vest with a machine learning model accurately detect and differentiate between CAD, VHD, and normal heartbeats?
Observational
Does a wearable multichannel PCG vest with a machine learning model accurately detect and differentiate between CAD, VHD, and normal heartbeats?
A wearable multichannel PCG vest combined with machine learning can non-invasively detect and differentiate coronary artery disease and valvular heart disorders with over 81% accuracy.
May facilitate non-invasive screening; hypothesis-generating and requires prospective validation before clinical use.
Coronary artery disease (CAD) and valvular heart disorder (VHD) are major categories of cardiovascular disease (CVD), the leading cause of mortality and morbidity worldwide. CAD occurs due to plaque accumulation on the inner walls of the coronary arteries, restricting blood flow to the myocardium and potentially leading to heart attack or stroke. VHD refers to dysfunction in one or more heart valves, impairing blood flow between the heart's chambers or to other systemic organs. Due to the prevalence of CVD, there is a global need for an effective screening tool capable of detecting various CVDs on a mass scale. Both CAD and VHD alter the acoustic signature of phonocardiogram (PCG) signals, offering a non-invasive detection method. This study implements a multiclass classification model to differentiate between CAD, VHD, and normal heartbeats, collected from subjects using an innovative wearable multichannel PCG vest. Linear frequency cepstral coefficients (LFCCs), coupled with a support vector machine (SVM) using a radial basis function (RBF) kernel, achieved the highest multiclass subject-level accuracy of 81.53%, with a sensitivity of 85.20% for VHD and 81.47% for CAD. Additionally, a binary classification task between normal and abnormal (CAD and VHD together) heartbeats reported an accuracy of 82.01%. This is the first study to apply multiclass classification across different CVD categories using PCG signals collected with the same hardware.
No takes yet. Share an insight, caveat, or question.
Fynn et al. (2025) conducted an observational in Coronary artery disease and valvular heart disorder. Multichannel PCG vest with SVM classifier vs. Normal heartbeats was evaluated on Multiclass subject-level accuracy. A wearable multichannel PCG vest using an SVM classifier achieved 81.53% multiclass accuracy in differentiating coronary artery disease, valvular heart disorders, and normal heartbeats.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: