A machine learning-based framework for analyzing mitral regurgitation-related signal patterns from photoplethysmography.

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.