Predicting vasovagal syncope during head-up tilt test : three machine learning approaches

Klemenc, Matjaž (avtor), Pellarini, Daniel (avtor), Papič, Aleš (avtor), Poličar, Pavlin Gregor (avtor), Štepec, Dejan (avtor), Bosnić, Zoran (avtor)

Introduction: Syncope prediction during head-up tilt testing (HUTT) remains challenging due to the complex interplay between autonomic and cardiovascular responses. This study investigates three computational approaches to forecast HUTT outcomes using continuous electrocardiogram (ECG) and blood pressure recordings from 105 patients with a history of syncope who underwent HUTT following a modified Italian protocol.Methods: Beat-to-beat heart rate and blood pressure signals were analyzed using: (1) gradient boosting models applied to frequency-domain features of heart rate variability (HRV); (2) an analytical modeling approach employing k-nearest neighbors (kNN) regression on transformed physiological signals; and (3) an incremental neural network model.Results and Discussion: Among these, the kNN regression approach provided the most consistent short-term forecasting of syncope probability, maintaining mean absolute errors below 0.13 for predictions up to 300 s before syncope onset. Gradient boosting models achieved promising classification performance with ROC AUC values up to 0.70, while the incremental network yielded moderate results. These findings demonstrate that data-driven analysis of early physiological changes can enable short-term forecasting of vasovagal syncope during HUTT, supporting the development of predictive tools for clinical risk assessment and personalized syncope management.

• Jezik: Angleški jezik
• Ključne besede: analytical modeling, head-up tilt test, heart rate variability, machine learning, vasovagal syncope
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FRI - Fakulteta za računalništvo in informatiko
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2026
• Št. strani: 10 str.
• Številčenje: Vol. 20
• PID: 20.500.12556/RUL-184043
• UDK: 004.85:616.12-073
• ISSN pri članku: 1662-5196
• DOI: 10.3389/fninf.2026.1740746
• COBISS.SI-ID: 280898307
• Datum objave v RUL: 24.06.2026

Gradivo je del revije

• Naslov: Frontiers in neuroinformatics
• Skrajšan naslov: Front. neuroinform.
• Založnik: Frontiers Media S.A.
• ISSN: 1662-5196
• COBISS.SI-ID: 523096089

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: analitično modeliranje, tilt test, sprememba srčne frekvence, strojno učenje, sinkopa