Learning-Based Screening of Endothelial Dysfunction From Photoplethysmographic Signals

• Main Authors: Chiara Calamanti, Sara Moccia, Lucia Migliorelli, Marina Paolanti, Emanuele Frontoni
• Format: Article
• Language: English
• Published: MDPI AG 2019-03-01
• Series: Electronics
• Subjects: endothelial dysfunction; photoplethysmography; machine learning; computer-assisted screening
• Online Access: http://www.mdpi.com/2079-9292/8/3/271

Endothelial-Dysfunction (ED) screening is of primary importance to early diagnosis cardiovascular diseases. Recently, approaches to ED screening are focusing more and more on photoplethysmography (PPG)-signal analysis, which is performed in a threshold-sensitive way and may not be suitable for tackling the high variability of PPG signals. The goal of this work was to present an innovative machine-learning (ML) approach to ED screening that could tackle such variability. Two research hypotheses guided this work: (H1) ML can support ED screening by classifying PPG features; and (H2) classification performance can be improved when including also anthropometric features. To investigate H1 and H2, a new dataset was built from 59 subject. The dataset is balanced in terms of subjects with and without ED. Support vector machine (SVM), random forest (RF) and k-nearest neighbors (KNN) classifiers were investigated for feature classification. With the leave-one-out evaluation protocol, the best classification results for H1 were obtained with SVM (accuracy = 71%, recall = 59%). When testing H2, the recall was further improved to 67%. Such results are a promising step for developing a novel and intelligent PPG device to assist clinicians in performing large scale and low cost ED screening.