Tracking Tidal Volume from Holter and Wearable Armband Electrocardiogram Monitoring

Lázaro, Jesús (Universidad de Zaragoza) ; Reljin, Natasa ; Bailón, Raquel (Universidad de Zaragoza) ; Gil, Eduardo (Universidad de Zaragoza) ; Noh, Yeonsik ; Laguna, Pablo (Universidad de Zaragoza) ; Chon, Ki H.

Resumen: A 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.

Idioma: Inglés

DOI: 10.1109/JBHI.2024.3383232

Año: 2024

Publicado en: IEEE journal of biomedical and health informatics 28, 6 (2024), 3457-3465
ISSN: 2168-2194

Factor impacto JCR: 6.8 (2024)

Categ. JCR: COMPUTER SCIENCE, INFORMATION SYSTEMS rank: 21 / 258 = 0.081 (2024) - Q1 - T1
Categ. JCR: MATHEMATICAL & COMPUTATIONAL BIOLOGY rank: 3 / 67 = 0.045 (2024) - Q1 - T1
Categ. JCR: MEDICAL INFORMATICS rank: 6 / 48 = 0.125 (2024) - Q1 - T1
Categ. JCR: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS rank: 23 / 175 = 0.131 (2024) - Q1 - T1

Factor impacto SCIMAGO: 1.649 - Electrical and Electronic Engineering (Q1) - Biotechnology (Q1) - Health Information Management (Q1) - Health Informatics (Q1) - Computer Science Applications (Q1)

Financiación: info:eu-repo/grantAgreement/ES/DGA/T39-23R
Financiación: info:eu-repo/grantAgreement/EC/H2020/745755/EU/Wearable Cardiorespiratory Monitor/WECARMON
Financiación: info:eu-repo/grantAgreement/ES/MICINN/PDC2021-120775
Financiación: info:eu-repo/grantAgreement/ES/MICINN/PID2021-126734OB-C21
Financiación: info:eu-repo/grantAgreement/ES/MICINN/PID2022-138585OA-C32
Financiación: info:eu-repo/grantAgreement/EUR/MICINN/TED2021-131106B-I00
Financiación: info:eu-repo/grantAgreement/ES/UZ/JIUZ-2022-IAR-06

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Ingen.Sistemas y Automát. (Dpto. Informát.Ingenie.Sistms.)
Área (Departamento): Área Teoría Señal y Comunicac. (Dpto. Ingeniería Electrón.Com.)