About: 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

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About: 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 Goto Sponge NotDistinct Permalink

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Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has 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
Creator	Feldheiser, Aarne Klum, Michael Orglmeister, Reinhold Pielmus, Alexandru-Gabriel Schmitt, Theresa Tigges, Timo Urban, Mike
Source	Medline; PMC
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 HD(TM) and Osypka ICON-Core(TM). 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.
has issue date	2020-04-04(xsd:dateTime)
bibo:doi	10.3390/s20072033
bibo:pmid	32260436
has license	cc-by
sha1sum (hex)	ba8a7815eb6ac4e669f420e53e267438f180b478
schema:url	https://doi.org/10.3390/s20072033
resource representing a document's 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
has PubMed Central identifier	PMC7180963
has PubMed identifier	32260436
schema:publication	Sensors (Basel)
resource representing a document's body	covid:ba8a7815eb6ac4e669f420e53e267438f180b478#body_text
is schema:about of	named entity 'respiratory' named entity 'lead' named entity 'conjunction' named entity 'monitoring' named entity 'ECG' named entity 'Monitoring' named entity 'CORE' named entity 'PARAMETERS' named entity 'ILLNESSES' named entity 'CLINICAL' named entity 'FUSION' named entity 'BEING' named entity 'COVID-19 PANDEMIC' named entity 'PROVEN' named entity 'USED' named entity 'CARDIORESPIRATORY MONITORING' named entity 'CONTINUOUS' covid:arg/ba8a7815eb6ac4e669f420e53e267438f180b478 named entity 'stethoscope' named entity 'communicable' named entity 'pandemic' named entity 'current' named entity 'electrocardiogram' named entity 'multimodal' named entity 'instability' named entity 'Respiratory' named entity 'prone position' named entity 'noncommunicable diseases' named entity 'stethoscope' named entity 'left ventricular' named entity 'electrocardiogram' named entity '21%' named entity 'Einthoven' named entity 'supine' named entity 'ECG' named entity 'Phonocardiogram' named entity 'respiration' named entity 'lung sounds' named entity 'reference values' named entity 'ECG' named entity 'morphology' named entity 'Respiratory rates' named entity 'delay line' named entity 'ECG' named entity '0.51' named entity 'ECG' named entity 'phonocardiogram' named entity 'medical device' named entity 'neurons' named entity 'neurons' named entity 'tidal volume' named entity 'STI' named entity 'ECG leads' named entity 'probands' named entity 'local maximum' named entity '1.2' named entity 'vectors' named entity 'ECG' named entity 'heart rate' named entity 'OSA' named entity 'training target' named entity 'respiratory sounds' named entity 'primary health care' named entity 'ECG' named entity 'MLP' named entity 'feature selection' named entity 'physiological parameters' named entity 'body temperature' named entity 'heart sounds' named entity 'stethoscope' named entity 'PCBs' named entity 'morphology' named entity 'ECG' named entity 'Bayesian information criterion' named entity 'cardiovascular'

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