Detection of abdominal aortic aneurysm using photoplethysmographic signals measured from the index finger

Abstract

Currently, most of abdominal aortic aneurysms (AAA) are detected by accident on imaging investigations of other medical conditions. The objective of this study was to investigate the classification of subjects with AAA patients and control subjects into two groups using features calculated directly from photoplethysmographic (PPG) signals measured from the index finger. PPG signals were analyzed from 48 test participants from which 25 had AAA and 23 were controls without AAA. Six pulse waveform features were computed from the PPG signals and sequential backward feature selection (SBFS) with linear discriminant analysis (LDA) and leave-one-participant-out cross validation was used to find the most relevant features. The actual classification was also done with LDA using features chosen by the SBFS. The dataset was divided to 70% training and 30% testing groups before classification. The split was stratified so that percentages of AAA subjects and controls was the same in test and train sets. Classification was repeated 500 times, and the median of the classification results was calculated. Three out of six pulse wave features were chosen for the classification. The LDA model had an area under curve (AUC) of 75%, an accuracy of 71%, a specificity of 68%, a sensitivity of 75%, ${\text{F}}_1$ score of 71%, and a positive predictive value (PPV) of 70%. Features calculated directly from PPG signals can separate individuals with AAA from controls with moderate accuracy. PPG waveform analysis could provide an easy-to-access method for AAA screening. Nonetheless, the performance should still be improved for guaranteeing clinical utility.