Key result
A machine learning classifier using PPG features detects mitral regurgitation with ~91% accuracy.
Why the study?
Standard echocardiography requires expert interpretation while other diagnostic modalities are costly and invasive, prompting investigation into non-invasive MR assessment using PPG signals.
Does a machine learning-based framework using photoplethysmography (PPG) signals accurately detect mitral regurgitation-related patterns?
Observational
Does a machine learning-based framework using photoplethysmography (PPG) signals accurately detect mitral regurgitation-related patterns?
A machine learning approach using photoplethysmography signals demonstrates high accuracy (91.07%) in detecting patterns associated with mitral regurgitation, offering a potential non-invasive monitoring tool.
Objectives This study investigates the feasibility of using photoplethysmography (PPG) signals for supporting mitral regurgitation (MR) assessment and monitoring using machine learning techniques. Methods MR occurs when blood flows backward from the left ventricle to the left atrium due to mitral valve insufficiency. Although echocardiography is the standard diagnostic method, it requires expert interpretation. Other techniques, such as magnetic resonance imaging, cardiac catheterization, and angiography, are costly and invasive. This study investigates the feasibility of using PPG signals for exploring MR-related signal patterns and supporting non-invasive monitoring via machine learning techniques. Results From 1,590 ten-second PPG recordings, 37 features were extracted. Following feature selection, 11 features were retained. Using 10-fold cross-validation, the proposed ensemble classifier achieved an accuracy of 91.07 %, sensitivity of 0.9462, specificity of 0.8765, AUC of 0.9113, and MCC of 0.8249 using 11 selected PPG features. The results indicate that PPG-derived features may capture signal characteristics associated with MR. Conclusions These findings demonstrate the feasibility of PPG-based machine learning approaches for supporting accessible and non-invasive monitoring of MR-related cardiovascular patterns.
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Bozkurt et al. (2026) conducted an observational in Mitral regurgitation. Photoplethysmography (PPG) signals with machine learning was evaluated on Detection of mitral regurgitation-related signal patterns (AUC 0.9113). A machine learning ensemble classifier using 11 photoplethysmography features detected mitral regurgitation with 91.07% accuracy, 0.9462 sensitivity, and an AUC of 0.9113.
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