Deep learning-based photoplethysmography classification for peripheral arterial disease detection: a proof-of-concept study

Objective. A proof-of-concept study to assess the potential of a deep learning (DL) based photoplethysmography PPG (‘DLPPG’) classification method to detect peripheral arterial disease (PAD) using toe PPG signals. Approach. PPG spectrogram images derived from our previously published multi-site PPG datasets (214 participants; 31.3% legs with PAD by ankle brachial pressure index (ABPI)) were input into a pretrained 8-layer (five convolutional layers + three fully connected layers) AlexNet as tailored to the 2-class problem with transfer learning to fine tune the convolutional neural network (CNN). k -fold random cross validation (CV) was performed (for k  = 5 and k  = 10), with each evaluated over k training/validation runs. Overall test sensitivity, specificity, accuracy, and Cohen’s Kappa statistic with 95% confidence interval ranges were calculated and compared, as well as sensitivities in detecting mild-moderate (0.5 ≤ ABPI < 0.9) and major (ABPI < 0.5) levels of PAD. Main results. CV with either k  = 5 or 10 folds gave similar diagnostic performances. The overall test sensitivity was 86.6%, specificity 90.2% and accuracy 88.9% (Kappa: 0.76 [0.70–0.82]) (at k = 5). The sensitivity to mild-moderate disease was 83.0% (75.5%–88.9%) and to major disease was 100.0% (90.5%–100.0%). Significance. Substantial agreements have been demonstrated between the DL-based PPG classification technique and the ABPI PAD diagnostic reference. This novel automatic approach, requiring minimal pre-processing of the pulse waveforms before PPG trace classification, could offer significant benefits for the diagnosis of PAD in a variety of clinical settings where low-cost, portable and easy-to-use diagnostics are desirable.