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: Machine learning for triage of strokes with large vessel occlusion using photoplethysmography biomarkers.
PHYSIOLOGICAL MEASUREMENT, 47 (1). ISSN 0967-3334 (2026)

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Folyóiratcikk

Szerző azonosítók:

Név	ORCID	MTMT szerző azonosító
Goda Márton Áron	0000-0003-0120-5940	10053946
Badge Helen	0000-0002-5351-393X
Khan Jasmeen	0009-0007-0555-620X
Solewicz Yosef	0000-0003-3987-1201
Davoodi Moran	0000-0001-6322-6523
Teramayi Rumbidzai
Cordato Dennis	0000-0001-8447-6644
Lin Longting	0000-0001-7104-9846
Christie Lauren	0000-0003-4900-5614
Blair Christopher	0000-0001-8685-9622
Sharma Gagan
Parsons Mark	0000-0001-8874-2487
Behar Joachim A	0000-0001-5956-7034

Absztrakt (kivonat):

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.

Folyóirat címe:

PHYSIOLOGICAL MEASUREMENT

Megjelenés éve:

2026

Kötet:

Szám:

ISSN:

0967-3334

Intézmény:

Pázmány Péter Katolikus Egyetem

Kar:

Információs Technológiai és Bionikai Kar (2013.07.-)