Five Day Attachment ECG Electrodes for Longitudinal Bio-Sensing Using Conformal Tattoo Substrates

Casson, Alexander J.; Saunders, Robert D.; Batchelor, John C.

doi:10.1109/jsen.2017.2650564

Cited by 28 publications

(15 citation statements)

References 40 publications

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“…In Figure 6 we compare the raw ECG waveform in Figure 6(a) against two filtering methods: the removal of the baseline wander using a discrete wavelet transform as described in [35] shown in Figure 6 Angular Velocity / a.u.…”

Section: Resultsmentioning

confidence: 99%

Motion artefact removal in electroencephalography and electrocardiography by using multichannel inertial measurement units and adaptive filtering

Beach

Balaban

et al. 2021

Healthcare Tech Letters

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This paper presents a new active electrode design for electroencephalogram (EEG) and electrocardiogram (ECG) sensors based on inertial measurement units to remove motion artefacts during signal acquisition. Rather than measuring motion data from a single source for the entire recording unit, inertial measurement units are attached to each individual EEG or ECG electrode to collect local movement data. This data is then used to remove the motion artefact by using normalised least mean square adaptive filtering. Results show that the proposed active electrode design can reduce motion contamination from EEG and ECG signals in chest movement and head swinging motion scenarios. However, it is found that the performance varies, necessitating the need for the algorithm to be paired with more sophisticated signal processing to identify scenarios where it is beneficial in terms of improving signal quality. The new instrumentation hardware allows data driven artefact removal to be performed, providing a new data driven approach compared to widely used blind-source separation methods, and helps enable in the wild EEG recordings to be performed.

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Section: Resultsmentioning

confidence: 99%

Motion artefact removal in electroencephalography and electrocardiography by using multichannel inertial measurement units and adaptive filtering

Beach

Balaban

et al. 2021

Healthcare Tech Letters

Self Cite

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show abstract

“…6 we compare the raw ECG waveform in Fig. 6a against two filtering methods: the removal of the baseline wonder using a discrete wavelet transform as described in [32] shown in Fig. 6b against the filtering using the adaptive filtering with the accelerometer data in Fig.…”

Section: Resultsmentioning

confidence: 99%

Motion artefact removal in electroencephalography and electrocardiography by using multichannel inertial measurement units and adaptive filtering

Beach¹,

Li²,

Balaban³

et al. 2021

Preprint

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This paper presents a new active electrode design for electroencephalogram (EEG) and electrocardiogram (ECG) sensors based on Inertial Measurement Units (IMUs) to remove motion artefacts during signal acquisition. Rather than measuring motion data from a single source for the entire recording unit, IMUs are attached to each individual EEG or ECG electrode to collect more local movement data. This movement data is then used to remove the motion artefact by using Normalised Least Mean Square (NLMS) adaptive filtering. Results show that the proposed active electrode design can reduce motion contamination from EEG and ECG signals in chest movement and head swinging motion scenarios. However the performance depends on the quality of the input signal with the algorithm providing better performance on signals with lower signal-to-noise ratios. The new instrumentation hardware allows data driven artefact removal to be performed, providing a new approach compared to widely used, non-parametric, blind-source separation methods, and helps enable \emph{in the wild} EEG recordings to be performed.

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“…When used for ECG on the chest, our tattoo devices have been kept in place for up to five days at a time [93]. Those in [90] where kept in place for ear EEG for two weeks, with usable BCI signals collected after this period.…”

Section: Flexible Electronics: Conformal Tattoo Devicesmentioning

confidence: 99%

Wearable EEG and beyond

2019

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The electroencephalogram (EEG) is a widely used non-invasive method for monitoring the brain. It is based upon placing conductive electrodes on the scalp which measure the small electrical potentials that arise outside of the head due to neuronal action within the brain. Historically this has been a large and bulky technology, restricted to the monitoring of subjects in a lab or clinic while they are stationary. Over the last decade much research effort has been put into the creation of "wearable EEG" which overcomes these limitations and allows the long term non-invasive recording of brain signals while people are out of the lab and moving about. This paper reviews the recent progress in this field, with particular emphasis on the electrodes used to make connections to the head and the physical EEG hardware. The emergence of conformal "tattoo" type EEG electrodes is highlighted as a key next step for giving very small and socially discrete units. In addition, new recommendations for the performance validation of novel electrode technologies are given, with standards in this area seen as the current main bottleneck to the wider take up of wearable EEG. The paper concludes by considering the next steps in the creation of next generation wearable EEG units, showing that a wide range of research avenues are present.

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Five Day Attachment ECG Electrodes for Longitudinal Bio-Sensing Using Conformal Tattoo Substrates

Abstract: The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

Cited by 28 publications

References 40 publications

Motion artefact removal in electroencephalography and electrocardiography by using multichannel inertial measurement units and adaptive filtering

Motion artefact removal in electroencephalography and electrocardiography by using multichannel inertial measurement units and adaptive filtering

Motion artefact removal in electroencephalography and electrocardiography by using multichannel inertial measurement units and adaptive filtering

Wearable EEG and beyond

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