Clinical evaluation of a method for automatic detection and removal of artifacts in auditory evoked potential monitoring

Beer, de Mm Michiel; Velde, van de M Maarten; Cluitmans, Pjm Pierre

doi:10.1007/bf01616744

Cited by 30 publications

(14 citation statements)

References 5 publications

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“…Visual inspection of the data was then performed using topographical maps and synoptic plots, but no further channels or segments were removed. The mean percentage of trials retained for AEP averaging was at least 80% of total segments for each group (HC: 82.11 ± 14.65%; MDD: 85.38 ± 16.80%), consistent with prior studies (23, 24). Finally, data from the 185 channels were re-referenced using linked mastoids and baseline corrected by subtracting the average voltage during the 100 msec preceding the tone.…”

Section: Methodssupporting

confidence: 88%

Overnight changes in waking auditory evoked potential amplitude reflect altered sleep homeostasis in major depression

Goldstein

Plante

Hulse

et al. 2011

Acta Psychiatr Scand

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Objective Sleep homeostasis is altered in major depressive disorder (MDD). Pre-to post-sleep decline in waking auditory evoked potential (AEP) amplitude has been correlated with sleep slow wave activity (SWA), suggesting that overnight changes in waking AEP amplitude are homeostatically regulated in healthy individuals. This study investigated whether the overnight change in waking AEP amplitude and its relation to SWA is altered in MDD. Method Using 256-channel high-density electroencephalography, all-night sleep polysomnography and single-tone waking AEPs pre-and post-sleep were collected in 15 healthy controls (HC) and 15 non-medicated individuals with MDD. Results N1 and P2 amplitudes of the waking AEP declined after sleep in the HC group, but not in MDD. The reduction in N1 amplitude also correlated with fronto-central SWA in the HC group, but a comparable relationship was not found in MDD, despite equivalent SWA between groups. No pre-to post-sleep differences were found for N1 or P2 latencies in either group. These findings were not confounded by varying levels of alertness or differences in sleep variables between groups. Conclusion MDD involves altered sleep homeostasis as measured by the overnight change in waking AEP amplitude. Future research is required to determine the clinical implications of these findings.

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Section: Methodssupporting

confidence: 88%

Overnight changes in waking auditory evoked potential amplitude reflect altered sleep homeostasis in major depression

Goldstein

Plante

Hulse

et al. 2011

Acta Psychiatr Scand

View full text Add to dashboard Cite

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“…The traditional approach to remove the eye blinks is to use the linear filters for certain frequency bands that belong to artifact range [73]. This however leads to significant loss of neurological activity, as there is always spectral overlap between neurological and artifactual phenomenon [74]. Another common practice for correcting the ocular artifacts (OA) is by using regression analysis [75].…”

Section: Processing Of Brain Signalsmentioning

confidence: 99%

A Brief Review of Brain Signal Monitoring Technologies for BCI Applications: Challenges and Prospects

Morshed¹

2014

J Bioeng Biomed Sci

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Significant strides have been made since 1940s for monitoring brain activities and utilizing the information for diagnosis, therapy, and control of robotic instruments including prosthetics. Monitoring brain activities with brain computer interfacing (BCI) technologies are of recent interest to due to the immense potential for various medical applications, particularly for many neurological disorder patients, and the emergence of technologies suitable for long duration BCI applications. Recent initiatives are geared towards transforming these clinic centric technologies to patient centric technologies by monitoring brain activities in practical settings. This paper briefly reviews current status of these technologies and relevant challenges. The technologies can be broadly classified into non-invasive (EEG, MEG, MRI) and invasive (Microelectrode, ECoG, MEA). Challenges to resolve include neuronal damage, neurotrophicity, usability and comfort.

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“…A secondary benefit is that additional physiological information in the form of EOG or EMG signals are not necessary to remove the artefacts. However, linear filtering fails when the neurological phenomenon of interest and artefact lie in similar frequency bands (de Beer et al, 1995). A look at the frequency ranges of neurological phenomena used in neonatal (0-32 Hz) and epileptic (0-64 Hz) EEG shows that for ocular (0-16 Hz) , muscle (10-100 Hz), cardiac (1-3 Hz) and respiration (0-12 Hz) artefacts this is usually the case (Volpe, 2008;Rowan and Tolunsky, 2003;Cacioppo et al, 2007).…”

Section: Filtering and Regressionmentioning

confidence: 99%

Artefact Detection and Removal Algorithms for EEG Diagnostic Systems

O’Regan¹

2018

Preprint

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Type of publicationDoctoral thesis Rights AbstractThe electroencephalogram (EEG) is a medical technology that is used in the monitoring of the brain and in the diagnosis of many neurological illnesses. Although coarse in its precision, the EEG is a non-invasive tool that requires minimal set-up times, and is suitably unobtrusive and mobile to allow continuous monitoring of the patient, either in clinical or domestic environments. Consequently, the EEG is the current tool-of-choice with which to continuously monitor the brain where temporal resolution, ease-of-use and mobility are important.Traditionally, EEG data are examined by a trained clinician who identifies neurological events of interest. However, recent advances in signal processing and machine learning techniques have allowed the automated detection of neurological events for many medical applications. In doing so, the burden of work on the clinician has been significantly reduced, improving the response time to illness, and allowing the relevant medical treatment to be administered within minutes rather than hours.However, as typical EEG signals are of the order of microvolts (µV ), contamination by signals arising from sources other than the brain is frequent. These extra-cerebral sources, known as artefacts, can significantly distort the EEG signal, making its interpretation difficult, and can dramatically disimprove automatic neurological event detection classification performance.This thesis therefore, contributes to the further improvement of automated neurological event detection systems, by identifying some of the major obstacles in deploying these EEG systems in ambulatory and clinical environments so that the EEG technologies can emerge from the laboratory towards real-world settings, where they can have a real-impact on the lives of patients. In this context, the thesis tackles three major problems in EEG monitoring, namely: (i) the problem of head-movement artefacts in ambulatory EEG, (ii) the high numbers of false detections in state-of-the-art, automated, epileptiform activity detection systems and (iii) false detections in state-of-the-art, automated neonatal seizure detection systems. To accomplish this, the thesis employs a wide range of statistical, signal processing and machine learning techniques drawn from mathematics, engineering and computer science.The first body of work outlined in this thesis proposes a system to automatically detect head-movement artefacts in ambulatory EEG and utilises supervised machine learning i classifiers to do so. The resulting head-movement artefact detection system is the first of its kind and offers accurate detection of head-movement artefacts in ambulatory EEG.Subsequently, additional physiological signals, in the form of gyroscopes, are used to detect head-movements and in doing so, bring additional information to the head-movement artefact detection task. A framework for combining EEG and gyroscope signals is then developed, offering improved head-movement artefact detection.The artefact detecti...

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Clinical evaluation of a method for automatic detection and removal of artifacts in auditory evoked potential monitoring

Cited by 30 publications

References 5 publications

Overnight changes in waking auditory evoked potential amplitude reflect altered sleep homeostasis in major depression

Overnight changes in waking auditory evoked potential amplitude reflect altered sleep homeostasis in major depression

A Brief Review of Brain Signal Monitoring Technologies for BCI Applications: Challenges and Prospects

Artefact Detection and Removal Algorithms for EEG Diagnostic Systems

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