000135179 001__ 135179
000135179 005__ 20240822135945.0
000135179 0247_ $$2doi$$a10.1109/JBHI.2024.3383232
000135179 0248_ $$2sideral$$a138528
000135179 037__ $$aART-2024-138528
000135179 041__ $$aeng
000135179 100__ $$0(orcid)0000-0001-8742-0072$$aLázaro, Jesús$$uUniversidad de Zaragoza
000135179 245__ $$aTracking Tidal Volume from Holter and Wearable Armband Electrocardiogram Monitoring
000135179 260__ $$c2024
000135179 5203_ $$aA novel method for tracking the tidal volume (TV) from electrocardiogram (ECG) is presented. The method is based on the amplitude of ECG-derived respiration (EDR) signals. Three different morphology-based EDR signals and three different amplitude estimation methods have been studied, leading to a total of 9 amplitude-EDR (AEDR) signals per ECG channel. The potential of these AEDR signals to track the changes in TV was analyzed. These methods do not need a calibration process. In addition, a personalized-calibration approach for TV estimation is proposed, based on a linear model that uses all AEDR signals from a device. All methods have been validated with two different ECG devices: a commercial Holter monitor, and a custom-made wearable armband. The lowest errors for the personalized-calibration methods, compared to a reference TV, were -3.48% [-17.41% / 12.93%] (median [first quartile / third quartile]) for the Holter monitor, and 0.28% [-10.90% / 17.15%] for the armband. On the other hand, medians of correlations to the reference TV were higher than 0.8 for uncalibrated methods, while they were higher than 0.9 for personal-calibrated methods. These results suggest that TV changes can be tracked from ECG using either a conventional (Holter) setup, or our custom-made wearable armband. These results also suggest that the methods are not as reliable in applications that induce small changes in TV, but they can be potentially useful for detecting large changes in TV, such as sleep apnea/hypopnea and/or exacerbations of a chronic respiratory disease.
000135179 536__ $$9info:eu-repo/grantAgreement/ES/UZ/JIUZ-2022-IAR-06$$9info:eu-repo/grantAgreement/EUR/MICINN/TED2021-131106B-I00$$9info:eu-repo/grantAgreement/ES/MICINN/PID2022-138585OA-C32$$9info:eu-repo/grantAgreement/ES/MICINN/PID2021-126734OB-C21$$9info:eu-repo/grantAgreement/ES/MICINN/PDC2021-120775$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 745755-WECARMON$$9info:eu-repo/grantAgreement/EC/H2020/745755/EU/Wearable Cardiorespiratory Monitor/WECARMON$$9info:eu-repo/grantAgreement/ES/DGA/T39-23R
000135179 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000135179 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000135179 700__ $$aReljin, Natasa
000135179 700__ $$0(orcid)0000-0003-1272-0550$$aBailón, Raquel$$uUniversidad de Zaragoza
000135179 700__ $$0(orcid)0000-0001-7285-0715$$aGil, Eduardo$$uUniversidad de Zaragoza
000135179 700__ $$aNoh, Yeonsik
000135179 700__ $$0(orcid)0000-0003-3434-9254$$aLaguna, Pablo$$uUniversidad de Zaragoza
000135179 700__ $$aChon, Ki H.
000135179 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát.
000135179 7102_ $$15008$$2800$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Teoría Señal y Comunicac.
000135179 773__ $$g28, 6 (2024), 3457-3465$$pIEEE j. biomed. health inform.$$tIEEE journal of biomedical and health informatics$$x2168-2194
000135179 8564_ $$s758077$$uhttps://zaguan.unizar.es/record/135179/files/texto_completo.pdf$$yVersión publicada
000135179 8564_ $$s3477144$$uhttps://zaguan.unizar.es/record/135179/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000135179 909CO $$ooai:zaguan.unizar.es:135179$$particulos$$pdriver
000135179 951__ $$a2024-08-22-13:58:16
000135179 980__ $$aARTICLE