Machine learning for triage of strokes with large vessel occlusion using photoplethysmography biomarkers.

Journal: Physiological measurement
Published Date: Jan 19, 2026

Abstract

Objective.Large vessel occlusion (LVO) stroke presents a major challenge in clinical practice due to the potential for poor outcomes with delayed treatment. Treatment for LVO involves highly specialized care, in particular endovascular thrombectomy, and is available only at certain hospitals. Therefore, prehospital identification of LVO by emergency ambulance services, can be critical for triaging LVO stroke patients directly to a hospital with access to endovascular therapy. Clinical scores exist to help distinguish LVO from less severe strokes, but they are based on a series of examinations that can be time-consuming and may be impractical for patients with dementia or those who cannot follow commands due to their stroke. There is a need for a fast and reliable method to aid in the early identification of LVO. In this study, our objective was to assess the feasibility of using 30 s photoplethysmography (PPG) recording to assist in recognizing LVO stroke.Approach.A total of 88 patients, including 25 with LVO, 27 with stroke mimic (SM), and 36 non-LVO stroke patients (NL), were recorded at the Liverpool Hospital emergency department in Sydney, Australia. Demographics (age, sex), as well as morphological features and beating rate variability measures, were extracted from the PPG. A binary classification approach was employed to differentiate between LVO stroke and NL + SM (NL.SM). A 2:1 train-test split was stratified and repeated randomly across 100 iterations.Main results.The best model achieved a median test set area under the receiver operating characteristic curve of 0.77 (0.71-0.82).Significance.Our study demonstrates the potential of utilizing a 30 s PPG recording for identifying LVO stroke.

Authors

  • Márton Áron Goda
    Pazmany Peter Katolikus Egyetem Informacios Technologiai es Bionikai Kar, Práter u. 50/A, Budapest, 1083, HUNGARY.
  • Márton Á Goda
    Pázmány Péter Catholic University Faculty of Information Technology and Bionics, Práter u. 50/A, Budapest 1083, Hungary.
  • Helen Badge
    Ingham Institute for Applied Medical Research, Sydney Brain Center UNSW, Liverpool Hospital, Sydney, 2052, AUSTRALIA.
  • Jasmeen Khan
    Ingham Institute for Applied Medical Research, Sydney Brain Center UNSW, Liverpool Hospital, Sydney, 2052, AUSTRALIA.
  • Yosef Solewicz
    Faculty of Biomedical Engineering, Technion Institute of Technology, Julius Silver Building, Haifa, 3200003, ISRAEL.
  • Moran Davoodi
    Faculty of Biomedical Engineering, Technion Institute of Technology, Julius Silver Building, Haifa, 3200003, ISRAEL.
  • Rumbidzai Teramayi
    Ingham Institute for Applied Medical Research, Sydney Brain Center UNSW, Liverpool Hospital, Sydney, 2052, AUSTRALIA.
  • Dennis Cordato
    Department of Neurology and Neurophysiology, Liverpool Hospital, Sydney, NSW, Australia; South Western Sydney Clinical School, University of New South Wales, Sydney, NSW, Australia; Ingham Institute for Applied Medical Research, Sydney, NSW, Australia.
  • Longting Lin
    Department of Neurology and Neurophysiology, Liverpool Hospital, Sydney, NSW, Australia; South Western Sydney Clinical School, University of New South Wales, Sydney, NSW, Australia.
  • Lauren Christie
    Ingham Institute for Applied Medical Research, Sydney Brain Center UNSW, Liverpool Hospital, Sydney, 2052, AUSTRALIA.
  • Christopher Blair
    South Western Sydney Clinical School, University of New South Wales, Sydney, NSW, Australia; Ingham Institute for Applied Medical Research, Sydney, NSW, Australia.
  • Gagan Sharma
    Ingham Institute for Applied Medical Research, Sydney Brain Center UNSW, Liverpool Hospital, Sydney, 2052, AUSTRALIA.
  • Mark Parsons
    Queensland University of Technology, 2 George St, Brisbane, QLD 4000, Australia. [email protected].
  • Joachim A Behar
    Faculty of Biomedical Engineering, Technion, Israel Institute of Technology, Haifa, Israel.

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