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

Goda Márton Áron

Badge Helen

Khan Jasmeen

Solewicz Yosef

Davoodi Moran

Teramayi Rumbidzai

Cordato Dennis

Lin Longting

Christie Lauren

Blair Christopher

Sharma Gagan

Parsons Mark

Behar Joachim A

2026

01.06.18.08. Biomérnöki tudományok

01.06.01.17. Orvosi mikrobiológia

01.06.06.14. Biofizika

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.

angol

angol

Folyóiratcikk

PeerReviewed

text

https://publikacio.ppke.hu/id/eprint/3604/1/Goda_2026_Physiol._Meas._47_015007.pdf

Goda Márton Áron; Badge Helen; Khan Jasmeen; Solewicz Yosef; Davoodi Moran; Teramayi Rumbidzai; Cordato Dennis; Lin Longting; Christie Lauren; Blair Christopher; Sharma Gagan; Parsons Mark; Behar Joachim A: Machine learning for triage of strokes with large vessel occlusion using photoplethysmography biomarkers. PHYSIOLOGICAL MEASUREMENT, 47 (1). ISSN 0967-3334 (2026)

MTMT:36910560 10.1088/1361-6579/ae2562

https://publikacio.ppke.hu/id/eprint/3604/

https://doi.org/10.1088/1361-6579/ae2562

MTMT:36910560 10.1088/1361-6579/ae2562