Evidence map›Paper›PMID 31823804›Full record

ArticleJournal of neuroengineering and rehabilitation2019

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

Konstantinos Petkos, Simos Koutsoftidis, Thomas Guiho, Patrick Degenaar, Andrew Jackson, Stephen E Greenwald, Peter Brown, Timothy Denison, Emmanuel M Drakakis

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of neuroengineering and rehabilitation, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Konstantinos PetkosDepartment of Bioengineering, Imperial College London, Prince Consort Road, London, SW7 2AZ, UK.
Simos KoutsoftidisDepartment of Bioengineering, Imperial College London, Prince Consort Road, London, SW7 2AZ, UK.
Thomas GuihoInstitute of Neuroscience, Newcastle University, Framlington Place, Newcastle upon Tyne, NE2 4HH, UK.
Patrick DegenaarSchool of Electrical & Electronic Engineering, Newcastle University, Merz Court, Newcastle upon Tyne, NE1 7RU, UK.
Andrew JacksonInstitute of Neuroscience, Newcastle University, Framlington Place, Newcastle upon Tyne, NE2 4HH, UK.
Stephen E GreenwaldBlizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, 4 Newark Street, London, E1 2AT, UK.
Peter BrownMRC Brain Network Dynamics Unit, University of Oxford, Mansfield Road, Oxford, OX1 3TH, UK.
Timothy DenisonMRC Brain Network Dynamics Unit, University of Oxford, Mansfield Road, Oxford, OX1 3TH, UK.
Emmanuel M DrakakisDepartment of Bioengineering, Imperial College London, Prince Consort Road, London, SW7 2AZ, UK. e.drakakis@imperial.ac.uk.ORCID 0000-0001-6649-0573

Funding

Medical Research Council G0901503Medical Research Council MC_UU_12024/1Medical Research Council MR/R001189/1
6 · The paper itself

Abstract

backgroundIt 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.

methodsTo address this problem, we designed and developed a low-noise (8 nV/√Hz), eight-channel, battery-powered, wearable and wireless instrument (55 × 80 mm

resultsTo 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.

conclusionsThe 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.

Indexed as

Wearable Electronic DevicesAnimalsDeep Brain StimulationElectrodiagnosisEquipment DesignHumansSignal Processing, Computer-AssistedWireless TechnologyAnalog front-endBioelectrical signalsBioinstrumentationHigh-performanceNeurological disordersWearableWireless

Identifiers

PMID31823804
PMCPMC6905040

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.