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A high-performance 8 nV/√Hz 8-channel wearable and wireless system for real-time monitoring of bioelectrical signals

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Title: A high-performance 8 nV/√Hz 8-channel wearable and wireless system for real-time monitoring of bioelectrical signals
Authors: Petkos, K
Koutsoftidis, S
Guiho, T
Degenaar, P
Jackson, A
Greenwald, S
Brown, P
Denison, T
Drakakis, E
Item Type: Journal Article
Abstract: Background: It is widely accepted by the scientific community that bioelectrical signals, which can be used for the identification of neurophysiological biomarkers indicative of a diseased or pathological state, could direct patient treatment towards more effective therapeutic strategies. However, the design and realisation of an instrument that can precisely record weak bioelectrical signals in the presence of strong interference stemming from a noisy clinical environment is one of the most difficult challenges associated with the strategy of monitoring bioelectrical signals for diagnostic purposes. Moreover, since patients often have to cope with the problem of limited mobility being connected to bulky and mains-powered instruments, there is a growing demand for small-sized, high-performance and ambulatory biopotential acquisition systems in the Intensive Care Unit (ICU) and in High-dependency wards. Finally, to the best of our knowledge, there are no commercial, small, battery-powered, wearable and wireless recording-only instruments that claim the capability of recording electrocorticographic (ECoG) signals. Methods: To address this problem, we designed and developed a low-noise (8 nV/√Hz), eight-channel, battery-powered, wearable and wireless instrument (55 × 80 mm2). The performance of the realised instrument was assessed by conducting both ex vivo and in vivo experiments. Results: To provide ex vivo proof-of-function, a wide variety of high-quality bioelectrical signal recordings are reported, including electroencephalographic (EEG), electromyographic (EMG), electrocardiographic (ECG), acceleration signals, and muscle fasciculations. Low-noise in vivo recordings of weak local field potentials (LFPs), which were wirelessly acquired in real time using segmented deep brain stimulation (DBS) electrodes implanted in the thalamus of a non-human primate, are also presented. Conclusions: The combination of desirable features and capabilities of this instrument, namely its small size (~one business card), its enhanced recording capabilities, its increased processing capabilities, its manufacturability (since it was designed using discrete off-the-shelf components), the wide bandwidth it offers (0.5 – 500 Hz) and the plurality of bioelectrical signals it can precisely record, render it a versatile and reliable tool to be utilized in a wide range of applications and environments.
Issue Date: 10-Dec-2019
Date of Acceptance: 26-Nov-2019
URI: http://hdl.handle.net/10044/1/75400
DOI: 10.1186/s12984-019-0629-2
ISSN: 1743-0003
Publisher: BioMed Central
Start Page: 1
End Page: 24
Journal / Book Title: Journal of NeuroEngineering and Rehabilitation
Volume: 16
Issue: 1
Copyright Statement: © 2019 The Author(s). This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Keywords: Science & Technology
Technology
Life Sciences & Biomedicine
Engineering, Biomedical
Neurosciences
Rehabilitation
Engineering
Neurosciences & Neurology
Neurological disorders
Bioelectrical signals
Analog front-end
High-performance
Wearable
Wireless
Bioinstrumentation
DEEP BRAIN-STIMULATION
ATRIAL-FIBRILLATION
AMPLIFIER
DISORDERS
DESIGN
DEVICE
Analog front-end
Bioelectrical signals
Bioinstrumentation
High-performance
Neurological disorders
Wearable
Wireless
Animals
Deep Brain Stimulation
Electrodiagnosis
Equipment Design
Humans
Signal Processing, Computer-Assisted
Wearable Electronic Devices
Wireless Technology
Animals
Humans
Electrodiagnosis
Deep Brain Stimulation
Equipment Design
Signal Processing, Computer-Assisted
Wireless Technology
Wearable Electronic Devices
0903 Biomedical Engineering
1109 Neurosciences
Rehabilitation
Publication Status: Published
Article Number: 156
Online Publication Date: 2019-12-10
Appears in Collections:Bioengineering
Faculty of Engineering