An Affordable Method for Evaluation of Ataxic Disorders Based on Electrooculography

López, Alberto; Ferrero, Francisco; Postolache, Octavian

doi:10.3390/s19173756

Cited by 18 publications

(7 citation statements)

References 45 publications

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“…Accelerometric data processing forms a complementary approach to traditional gait analysis that use stepping characteristics [49], [53] to discriminate ataxic patients from controls. Moreover, its accuracy is higher than that of different models based on the selection of spatial domain features [54], [55] with an accuracy between 70 and 80% in the early stage of the disease [53], [56].…”

Section: Discussionmentioning

confidence: 91%

Deep Learning for Accelerometric Data Assessment and Ataxic Gait Monitoring

Procházka

Dostál

Cejnar

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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Ataxic gait monitoring and assessment of neurological disorders belong to important multidisciplinary areas that are supported by digital signal processing methods and machine learning tools. This paper presents the possibility of using accelerometric data to optimise deep learning convolutional neural network systems to distinguish between ataxic and normal gait. The experimental dataset includes 860 signal segments of 16 ataxic patients and 19 individuals from the control set with the mean age of 38.6 and 39.6 years, respectively. The proposed methodology is based upon the analysis of frequency components of accelerometric signals simultaneously recorded at specific body positions with a sampling frequency of 60 Hz. The deep learning system uses all of the frequency components in a range of 0, 30 Hz. Our classification results are compared with those obtained by standard methods, which include the support vector machine, Bayesian methods, and the two-layer neural network with features estimated as the relative power in selected frequency bands. Our results show that the appropriate selection of sensor positions can increase the accuracy from 81.2% for the foot position to 91.7% for the spine position. Combining the input data and the deep learning methodology with five layers increased the accuracy to 95.8%. Our methodology suggests that artificial intelligence methods and deep learning are efficient methods in the assessment of motion disorders and they have a wide range of further applications.

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Section: Discussionmentioning

confidence: 91%

Deep Learning for Accelerometric Data Assessment and Ataxic Gait Monitoring

Procházka

Dostál

Cejnar

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…To record real-time EOG during the rolling of the eyes, three Ag/CNT/PDMS/80 P sensors The application of the sensor for EOG recording was demonstrated as well. The EOG measures the electrostatic potential that exists between the retina and cornea during eyes movements [51,52]. Figure 5e shows the placement of our developed sensors and commercial dry Ag/AgCl electrodes as a standard electrode on the forehead for EOG recordings, where two Ag/CNT/PDMS/80 P sensors are used as working and reference/ground electrodes, respectively, due to their high flexibility and conductivity [53][54][55].…”

Section: Resultsmentioning

confidence: 99%

Solvent-Free and Cost-Efficient Fabrication of a High-Performance Nanocomposite Sensor for Recording of Electrophysiological Signals

Zhuo,

Zhang,

Tessier

et al. 2024

Biosensors

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Carbon nanotube (CNT)-based nanocomposites have found applications in making sensors for various types of physiological sensing. However, the sensors’ fabrication process is usually complex, multistep, and requires longtime mixing and hazardous solvents that can be harmful to the environment. Here, we report a flexible dry silver (Ag)/CNT/polydimethylsiloxane (PDMS) nanocomposite-based sensor made by a solvent-free, low-temperature, time-effective, and simple approach for electrophysiological recording. By mechanical compression and thermal treatment of Ag/CNT, a connected conductive network of the fillers was formed, after which the PDMS was added as a polymer matrix. The CNTs make a continuous network for electrons transport, endowing the nanocomposite with high electrical conductivity, mechanical strength, and durability. This process is solvent-free and does not require a high temperature or complex mixing procedure. The sensor shows high flexibility and good conductivity. High-quality electroencephalography (EEG) and electrooculography (EOG) were performed using fabricated dry sensors. Our results show that the Ag/CNT/PDMS sensor has comparable skin–sensor interface impedance with commercial Ag/AgCl-coated dry electrodes, better performance for noninvasive electrophysiological signal recording, and a higher signal-to-noise ratio (SNR) even after 8 months of storage. The SNR of electrophysiological signal recording was measured to be 26.83 dB for our developed sensors versus 25.23 dB for commercial Ag/AgCl-coated dry electrodes. Our process of compress-heating the functional fillers provides a universal approach to fabricate various types of nanocomposites with different nanofillers and desired electrical and mechanical properties.

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“…PSD values were log 10 -transformed to account for skewed distributions. For our analyses, we computed the total PSD power for the following frequency bands: delta (2-4 Hz), theta (4-6 Hz), low alpha (6-9 Hz), high alpha (9-13 Hz), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (30-48 Hz) [30].…”

Section: Power Spectral Density (Psd)mentioning

confidence: 99%

Eye-movement artifact correction in infant EEG

Srishyla,

Webb,

Elsabbagh

et al. 2024

Preprint

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BackgroundIndependent Component Analysis (ICA) is a well-established approach to clean EEG and remove the impact of signals of non-neural origin, such as those from muscular activity and eye movements. However, evidence suggests that ICA removes artifacts less effectively in infants than in adults. This study systematically compares ICA and Artifact Blocking (AB), an alternative approach proposed to improve eye-movement artifact correction in infant EEG.MethodsWe analyzed EEG collected from 50 infants between 6 and 18 months of age as part of the International Infant EEG Data Integration Platform (EEG-IP), a longitudinal multi-study dataset. EEG was recorded while infants sat on their caregivers’ laps and watched videos. We used ICA and AB to correct for eye-movement artifacts in the EEG and calculated the proportion of effectively corrected segments, signal-to-noise ratio (SNR), power-spectral density (PSD), and multiscale entropy (MSE) in manually selected segments with and without eye-movement artifacts.ResultsOn the one hand, the proportion of effectively corrected segments indicated that ICA corrected eye-movement artifacts (sensitivity) better than AB. SNR and PSD indicated that both AB and ICA correct eye-movement artifacts with equal sensitivity. MSE gave mixed results. On the other hand, AB caused less distortion to the clean segments (specificity) for SNR, PSD, and MSE.ConclusionsOur results suggest that ICA is more sensitive (i.e., it better removes artifacts) but less specific (it distorts clean signals) than AB for correcting eye-movement artifacts in infant EEG.

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An Affordable Method for Evaluation of Ataxic Disorders Based on Electrooculography

Cited by 18 publications

References 45 publications

Deep Learning for Accelerometric Data Assessment and Ataxic Gait Monitoring

Deep Learning for Accelerometric Data Assessment and Ataxic Gait Monitoring

Solvent-Free and Cost-Efficient Fabrication of a High-Performance Nanocomposite Sensor for Recording of Electrophysiological Signals

Eye-movement artifact correction in infant EEG

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