In this study, we propose and analyze a noninvasive method for detecting the atherosclerotic changes of vasculature based on the analysis of photoplethysmographic (PPG) signals. METHODS: the proposed method is called finger-toe (FT)-plot analysis that utilizes both finger and toe PPG signals. For the features extracted from the FT-plots, we implemented different linear discriminant analysis based classifiers and analyzed seven promising ones in detail. We used the signals recorded from altogether 75 test subjects (categorized as 27 atherosclerotic patients and 48 control subjects based on ankle brachial pressure index, symptoms and disease history) in the training, and testing of the method. Besides leave one out cross validation, we tested the method by using training data independent signals recorded with two different PPG devices. The performance of the FT-plot is compared with other indicators related to the risk of cardiovascular diseases. RESULTS: we found an average area under ROC (receiver operating characteristic) curve of [Formula: see text] (mean ± standard deviation based on different datasets), sensitivity of [Formula: see text], specificity of [Formula: see text], accuracy of [Formula: see text], performance of [Formula: see text] and positive and negative predictive values of [Formula: see text] and [Formula: see text], respectively, for the different tested classifiers. CONCLUSIONS: the study shows that the FT-plot analysis could be a useful additional tool for detecting atherosclerotic changes. Our findings provide evidence for the utility of multi-channel pulse wave measurements and analysis for the detection of atherosclerosis. This may facilitate development of novel early diagnostic approaches and preventive strategies.
In this study, we propose and analyze a noninvasive method for detecting the atherosclerotic changes of vasculature based on the analysis of photoplethysmographic (PPG) signals. METHODS: the proposed method is called finger-toe (FT)-plot analysis that utilizes both finger and toe PPG signals. For the features extracted from the FT-plots, we implemented different linear discriminant analysis based classifiers and analyzed seven promising ones in detail. We used the signals recorded from altogether 75 test subjects (categorized as 27 atheroscleroticpatients and 48 control subjects based on ankle brachial pressure index, symptoms and disease history) in the training, and testing of the method. Besides leave one out cross validation, we tested the method by using training data independent signals recorded with two different PPG devices. The performance of the FT-plot is compared with other indicators related to the risk of cardiovascular diseases. RESULTS: we found an average area under ROC (receiver operating characteristic) curve of [Formula: see text] (mean ± standard deviation based on different datasets), sensitivity of [Formula: see text], specificity of [Formula: see text], accuracy of [Formula: see text], performance of [Formula: see text] and positive and negative predictive values of [Formula: see text] and [Formula: see text], respectively, for the different tested classifiers. CONCLUSIONS: the study shows that the FT-plot analysis could be a useful additional tool for detecting atherosclerotic changes. Our findings provide evidence for the utility of multi-channel pulse wave measurements and analysis for the detection of atherosclerosis. This may facilitate development of novel early diagnostic approaches and preventive strategies.