Please use this identifier to cite or link to this item:
For citation please use:
Main Title: Wearable Cardiorespiratory Monitoring Employing a Multimodal Digital Patch Stethoscope: Estimation of ECG, PEP, LVET and Respiration Using a 55 mm Single-Lead ECG and Phonocardiogram
Author(s): Klum, Michael
Urban, Mike
Tigges, Timo
Pielmus, Alexandru-Gabriel
Feldheiser, Aarne
Schmitt, Theresa
Orglmeister, Reinhold
Type: Article
Language Code: en
Abstract: Cardiovascular diseases are the main cause of death worldwide, with sleep disordered breathing being a further aggravating factor. Respiratory illnesses are the third leading cause of death amongst the noncommunicable diseases. The current COVID-19 pandemic, however, also highlights the impact of communicable respiratory syndromes. In the clinical routine, prolonged postanesthetic respiratory instability worsens the patient outcome. Even though early and continuous, long-term cardiorespiratory monitoring has been proposed or even proven to be beneficial in several situations, implementations thereof are sparse. We employed our recently presented, multimodal patch stethoscope to estimate Einthoven electrocardiogram (ECG) Lead I and II from a single 55 mm ECG lead. Using the stethoscope and ECG subsystems, the pre-ejection period (PEP) and left ventricular ejection time (LVET) were estimated. ECG-derived respiration techniques were used in conjunction with a novel, phonocardiogram-derived respiration approach to extract respiratory parameters. Medical-grade references were the SOMNOmedics SOMNO HDTM and Osypka ICON-CoreTM. In a study including 10 healthy subjects, we analyzed the performances in the supine, lateral, and prone position. Einthoven I and II estimations yielded correlations exceeding 0.97. LVET and PEP estimation errors were 10% and 21%, respectively. Respiratory rates were estimated with mean absolute errors below 1.2 bpm, and the respiratory signal yielded a correlation of 0.66. We conclude that the estimation of ECG, PEP, LVET, and respiratory parameters is feasible using a wearable, multimodal acquisition device and encourage further research in multimodal signal fusion for respiratory signal estimation.
Issue Date: 4-Apr-2020
Date Available: 11-Jun-2020
DDC Class: 610 Medizin und Gesundheit
004 Datenverarbeitung; Informatik
Subject(s): ECG
respiration rate
wearable cardiorespiratory monitoring
digital stethoscope
ECG-derived respiration
phonocardiogram-derived respiration
neural network
Sponsor/Funder: DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berlin
Journal Title: Sensors
Publisher: MDPI
Publisher Place: Basel
Volume: 20
Issue: 7
Article Number: 2033
Publisher DOI: 10.3390/s20072033
EISSN: 1424-8220
Appears in Collections:FG Elektronik und medizinische Signalverarbeitung » Publications

Files in This Item:
Format: Adobe PDF | Size: 7.53 MB
DownloadShow Preview

Item Export Bar

This item is licensed under a Creative Commons License Creative Commons