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.
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 (5 Convolutional Layers + 3 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.
Cross validation 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%).
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.

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