Real-World Consumer-Grade Sensor Signal Alignment Procedure Applied to High-Noise ECG to BCG Signal Synchronization *

Schütz, Narayan; Botros, Angela A.; Knobel, Samuel E. J.; Saner, Hugo; Buluschek, Philipp; Nef, Tobias (27 August 2020). Real-World Consumer-Grade Sensor Signal Alignment Procedure Applied to High-Noise ECG to BCG Signal Synchronization *. In: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society (pp. 5858-5962). IEEE 10.1109/EMBC44109.2020.9175449

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In recent years, consumer-grade sensors that measure health relevant physiological signals have become widely available and are increasingly used by consumers and researchers alike. While this allows for multiple novel, potentially highly beneficial, large-scale health monitoring applications, quality of these data streams is oftentimes suboptimal. This makes alignment of different high-frequency data streams from multiple, non-connected sensors, a difficult task. In this work we describe a noise-robust framework to align high-frequency signals from different sensors, that share some underlying characteristic, obtained in a free-living, non-clinical, home environment. We demonstrate the approach on the basis of a single-lead, medical-grade, mobile electrocardiography device and a consumer-grade sleep sensor that allows for ballistocardiography. Both commercially available sensors measure the physiological process of a heartbeat. We show, on the basis of real-world data with multiple people and sensors, that the two highly noisy and sometimes dissimilar signals could in most cases be aligned with considerable precision. As a result, we could reduce mean heartbeat peak-to-peak difference by 58.1% on average and increase signal correlation by 0.40 on average.

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - Gerontechnology and Rehabilitation

UniBE Contributor:

Schütz, Narayan; Botros, Angela Amira; Knobel, Samuel; Saner, Hugo Ernst and Nef, Tobias

Subjects:

500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health
600 Technology > 620 Engineering

ISBN:

978-1-7281-1990-8

Publisher:

IEEE

Language:

English

Submitter:

Angela Amira Botros

Date Deposited:

14 Sep 2020 09:49

Last Modified:

14 Sep 2020 09:49

Publisher DOI:

10.1109/EMBC44109.2020.9175449

BORIS DOI:

10.7892/boris.146358

URI:

https://boris.unibe.ch/id/eprint/146358

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