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Improving the Accuracy of R-Peak Detection in a Wearable Ar..:

, In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC),

Hajeb-Mohammadalipour, Shirin ; Hossain, Md-Billal ; Chon, Ki H. - p. 4291-4294 , 2022

Link: https://doi.org/10.1109/EMBC48229.2022.9871609

RT 
T1 <cTitle>2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)</cTitle>: T1 <Title>Improving the Accuracy of R-Peak Detection in a Wearable Armband Device for Daily Life Electrocardiogram Monitoring Using a Deep Convolutional Denoising Encoder-Decoder Network</Title>

UL https://suche.suub.uni-bremen.de/peid=ieee-9871609&Exemplar=1&LAN=DE
				
A1 Hajeb-Mohammadalipour, Shirin
A1 Hossain, Md-Billal
A1 Chon, Ki H.
YR 2022
SN 2694-0604
K1 Heart rate
K1 Training
K1 Wearable computers
K1 Noise reduction
K1 Electrocardiography
K1 Electromyography
K1 Recording
K1 Clinical Relevance─This study provides a method to remove significant EMG artifacts that may blur the identification of R-peaks in the armband ECG recordings. This approach can more accurately detect HR and may increase the percent of data that are usable
K1 even during daytime recordings
SP 4291
OP 4294
LK http://dx.doi.org/https://doi.org/10.1109/EMBC48229.2022.9871609
DO https://doi.org/10.1109/EMBC48229.2022.9871609
SF ELIB - SuUB Bremen

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