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ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges
Open AccessArticle

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

1
Department of Electronics and Medical Signal Processing, Technische Universität Berlin, Einsteinufer 17, 10587 Berlin, Germany
2
Department of Anesthesiology, Intensive Care Medicine and Pain Therapy, Evang. Kliniken Essen-Mitte, Huyssens-Stiftung/Knappschaft, Henricistr. 92, 45136 Essen, Germany
3
Department of Anesthesiology and Intensive Care Medicine, Charité - Universitätsmedizin Berlin, Campus Virchow-Klinikum, 13353 Berlin, Germany and Charité Campus Mitte, 10117 Berlin, Germany
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(7), 2033; https://doi.org/10.3390/s20072033
Received: 26 February 2020 / Revised: 25 March 2020 / Accepted: 30 March 2020 / Published: 4 April 2020
(This article belongs to the Special Issue ECG Monitoring System)
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. View Full-Text
Keywords: ECG; PEP; LVET; respiration rate; wearable cardiorespiratory monitoring; patch; digital stethoscope; ECG-derived respiration; phonocardiogram-derived respiration; neural network ECG; PEP; LVET; respiration rate; wearable cardiorespiratory monitoring; patch; digital stethoscope; ECG-derived respiration; phonocardiogram-derived respiration; neural network
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Klum, M.; Urban, M.; Tigges, T.; Pielmus, A.-G.; Feldheiser, A.; Schmitt, T.; Orglmeister, R. 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. Sensors 2020, 20, 2033.

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