Photoplethysmography combined with a Support Vector Machine classifier detected coronary artery disease in ICU patients with a sensitivity of 85% and a specificity of 78%.
Observational
Can photoplethysmography (PPG) signal analysis using a Support Vector Machine classifier accurately detect coronary artery disease?
Photoplethysmography combined with machine learning shows potential as a non-invasive, inexpensive screening technique for coronary artery disease detection.
Effect estimate: Sensitivity 85%, Specificity 78%
This paper presents a technique for coronary artery disease (CAD) detection through photoplethysmography (PPG). This work is aimed at developing a non-invasive, inexpensive screening technique suitable for home monitoring. Time domain analysis of PPG signal and its second derivative has been carried out to extract distinguishing features. Support Vector Machine based classifier has been used to classify CAD patients. ICU patient data from MIMIC-II dataset has been used for performance evaluation. Sensitivity of 85% and specificity of 78% has been achieved for the analysed data.
Paradkar et al. (2017) conducted an observational in Coronary artery disease. Photoplethysmography (PPG) with Support Vector Machine classifier was evaluated on Coronary artery disease detection (Sensitivity 85%, Specificity 78%). Photoplethysmography combined with a Support Vector Machine classifier detected coronary artery disease in ICU patients with a sensitivity of 85% and a specificity of 78%.
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