Non‐invasive, home‐based electroencephalography hypoglycaemia warning system for personal monitoring using skin surface electrodes: a single‐case feasibility study

Clewett, Christopher J.; Langley, Philip; Bateson, Anthony D.; Asghar, Aziz U. R.; Wilkinson, A. J.

doi:10.1049/htl.2015.0037

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…The data was recorded using a smartphone-based EEG system (Clewett et al 2016 ; Bateson 2018 ; Bateson and Asghar 2021 ), with a sampling rate of 250 Hz, and was affixed to the backrest of a chair upon which the participant was sitting. EEG signals were acquired from 19 Ag/AgCl scalp electrodes (Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, O2) configured in the international 10–20 arrangement using an EEG headcap (Sleepcap, electrodes encased in a soft sponge material, cables located internally, Spes Medica).…”

Section: Methodsmentioning

confidence: 99%

A comparative experimental study of visual brain event-related potentials to a working memory task: virtual reality head-mounted display versus a desktop computer screen

et al. 2021

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Virtual reality head mounted display (VR HMD) systems are increasingly utilised in combination with electroencephalography (EEG) in the experimental study of cognitive tasks. The aim of our investigation was to determine the similarities/differences between VR HMD and the computer screen (CS) in response to an n-back working memory task by comparing visual electrophysiological event-related potential (ERP) waveforms (N1/P1/P3 components). The same protocol was undertaken for VR HMD and CS with participants wearing the same EEG headcap. ERP waveforms obtained with the VR HMD environment followed a similar time course to those acquired in CS. The P3 mean and peak amplitudes obtained in VR HMD were not significantly different to those obtained in CS. In contrast, the N1 component was significantly higher in mean and peak amplitudes for the VR HMD environment compared to CS at the frontal electrodes. Significantly higher P1 mean and peak amplitudes were found at the occipital region compared to the temporal for VR HMD. Our results show that successful acquisition of ERP components to a working memory task is achievable by combining VR HMD with EEG. In addition, the higher amplitude N1/P1 components seen in VR HMD indicates the potential utility of this VR modality in the investigation of early ERPs. In conclusion, the combination of VR HMD with EEG/ERP would be a useful approach to advance the study of cognitive function in experimental brain research.

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Section: Methodsmentioning

confidence: 99%

A comparative experimental study of visual brain event-related potentials to a working memory task: virtual reality head-mounted display versus a desktop computer screen

et al. 2021

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“…Schechter et al (Schechter et al, 2012) used a sensor system of HR, perspiration (unprocessed skin conductance at the fingertips), skin temperature and tremor (by accelerometer) measurements and obtained a sensitivity of 100% and a specificity of 85.7% in detecting nocturnal hypoglycemia in a pilot study in ten adolescents with type 1 diabetes. Clewett et al demonstrated feasibility of noninvasive EEG-based hypoglycemia warning in a single case study (Clewett et al, 2016).…”

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confidence: 99%

A multiparameter model for non-invasive detection of hypoglycemia

et al. 2019

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Severe hypoglycemia is the most serious acute complication for people with type 1 diabetes (T1D). Approximately 25% of people with T1D have impaired ability to recognize impending hypoglycemia, and nocturnal episodes are feared. We have investigated the use of non-invasive sensors for detection of hypoglycemia based on a mathematical model which combines several sensor measurements to identify physiological responses to hypoglycemia. Data from randomized singleblinded euglycemic and hypoglycemic glucose clamps in 20 participants with type 1 diabetes and impaired awareness of hypoglycemia was used in the analyses. Using a sensor combination of sudomotor activity at three skin sites, ECG-derived heart rate and heart rate corrected QT interval, near-infrared and bioimpedance spectroscopy; physiological responses associated with hypoglycemia could be identified with an F1 score accuracy up to 88%. We present a novel model for identification of non-invasively measurable physiological responses related to hypoglycemia, showing potential for detection of moderate hypoglycemia using a wearable sensor system.

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Towards adequate prediction of prediabetes using spatiotemporal ECG and EEG feature analysis and weight-based multi-model approach

Igbe

Kandwal

et al. 2020

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Non‐invasive, home‐based electroencephalography hypoglycaemia warning system for personal monitoring using skin surface electrodes: a single‐case feasibility study

Abstract: The feasibility of a non-invasive EEG-based hypoglycaemia warning system for personal monitoring in the home has been demonstrated in a single case study. The results suggest that further studies are warranted to evaluate the system prospectively in a larger group of participants.

Cited by 4 publications

References 12 publications

A comparative experimental study of visual brain event-related potentials to a working memory task: virtual reality head-mounted display versus a desktop computer screen

A comparative experimental study of visual brain event-related potentials to a working memory task: virtual reality head-mounted display versus a desktop computer screen

A multiparameter model for non-invasive detection of hypoglycemia

Towards adequate prediction of prediabetes using spatiotemporal ECG and EEG feature analysis and weight-based multi-model approach

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