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

John Allen, Haipeng Liu, Sadaf Iqbal, Dingchang Zheng, Gerard Stansby

    Research output: Contribution to journalArticlepeer-review

    6 Citations (Scopus)
    21 Downloads (Pure)

    Abstract

    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 (fork = 5 andk = 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 eitherk = 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]) (atk= 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.

    Original languageEnglish
    Article number054002
    Number of pages23
    JournalPhysiological Measurement
    Volume42
    Issue number5
    Early online date20 Apr 2021
    DOIs
    Publication statusPublished - 17 Jun 2021

    Bibliographical note

    Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

    Keywords

    • AI
    • artery
    • deep learning
    • peripheral arterial disease
    • photoplethysmography
    • pulse
    • wavelet

    ASJC Scopus subject areas

    • Biophysics
    • Physiology
    • Biomedical Engineering
    • Physiology (medical)

    Fingerprint

    Dive into the research topics of 'Deep learning based photoplethysmography classification for peripheral arterial disease detection: a proof-of-concept study'. Together they form a unique fingerprint.

    Cite this