Classification of Physiological States Through Machine Learning Algorithms Applied to Ultra-Short-Term Heart Rate and Pulse Rate Variability Indices on a Single-Feature Basis

Iovino, Marta; Lazic, Ivan M; Loncar-Turukalo, Tatjana G; Javorka, Michal; Pernice, Riccardo; Faes, Luca

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eNauka > Results > Classification of Physiological States Through Machine Learning Algorithms Applied to Ultra-Short-Term Heart Rate and Pulse Rate Variability Indices on a Single-Feature Basis

Title:	Classification of Physiological States Through Machine Learning Algorithms Applied to Ultra-Short-Term Heart Rate and Pulse Rate Variability Indices on a Single-Feature Basis
Authors:	Iovino, Marta; Lazic, Ivan M; Loncar-Turukalo, Tatjana G; Javorka, Michal; Pernice, Riccardo; Faes, Luca
Issue Date:	2024
Publication:	MEDICON 2023 AND CMBEBIH 2023, VOL 1
ISSN:	1680-0737 Search Idenfier
Type:	Conference Paper
Collation:	vol. 93 str. 114-124
DOI:	10.1007/978-3-031-49062-0_13
WoS-ID:	001261436400013
Scopus-ID:	2-s2.0-85181771981
URI:	https://enauka.gov.rs/handle/123456789/935131
Project:	"Sensoristica intelligente, infrastrutture e modelli gestionali per la sicurezza di soggetti fragili" (4FRAILTY) project - Italian MIUR [PON RI 2014-20, ARS01 00345] SiciliAnMicronanOTecH Research And Innovation CEnter "SAMOTHRACE" (MUR) [PNRR-M4C2, ECS 00000022] European Social Fund (ESF) Complementary Operational Programme [(POC) 2014/2020] Sicily Region
Metadata source:	(Preuzeto iz Nasi u WoS)
M-category:	Mp. category will be shown later

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