EEG Artifact Detection Using Spatial Distribution of Rhythmicity

Skupch, A.M.; Dollfuss, P.; Fürbass, Franz; Hartmann, M.; Perko, Hannes; Pataraia, Ekaterina; Lindinger, G.; Kluge, Tilmann

doi:10.1016/j.apcbee.2013.08.005

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…Artifact-related false positives have been a common problem in traditional EEG recordings as well. The rhythmic patterns generated from a faulty electrode or ocular and muscle movements during EEG may mimic epileptiform activity, thereby misinterpreting the epileptic seizures [ 60 , 61 ]. Interestingly, this limitation was only observed by Weisdorf among the EEG-based systems.…”

Section: Discussionmentioning

confidence: 99%

Medical Technology: A Systematic Review on Medical Devices Utilized for Epilepsy Prediction and Management

Ong

Wong

Arulsamy

et al. 2022

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: Epilepsy is a devastating neurological disorder that affects nearly 70 million people worldwide. Epilepsy causes uncontrollable, unprovoked and unpredictable seizures that reduces the quality of life of those afflicted, with 1-9 epileptic patient deaths per 1000 patient occurring annually due to sudden unexpected death in epilepsy (SUDEP). Predicting the onset of seizures and managing them may help patients from harming themselves and may improve their well-being. For a long time, electroencephalography (EEG) devices have been the mainstay for seizure detection and monitoring. This systematic review aimed to elucidate and critically evaluate the latest advancements of medical devices, besides EEG, that have been proposed for the management and prediction of epileptic seizures. A literature search was performed on three databases; PubMed, Scopus and EMBASE. Following title/abstract screening by two independent reviewers, 27 articles were selected for critical analysis in this review. These articles revealed ambulatory, non-invasive and wearable medical devices such as the in-ear EEG devices, the accelerometer-based devices and the subcutaneous implanted EEG devices might be more acceptable than traditional EEG systems. In addition, extracerebral signal-based devices may be more efficient than EEG-based systems, especially when combined with an intervention trigger. Although further studies may still be required to improve and validate these proposed systems before commercialization, these findings may give hope to epileptic patients, particularly those with refractory epilepsy, to predict and manage their seizures. The use of medical devices for epilepsy may improve patients' independence and quality of life and possibly prevent sudden unexpected death in epilepsy (SUDEP).

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Section: Discussionmentioning

confidence: 99%

Medical Technology: A Systematic Review on Medical Devices Utilized for Epilepsy Prediction and Management

Ong

Wong

Arulsamy

et al. 2022

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A Convolutional Neural Network for Artifacts Detection in EEG Data

Boudaya

Chaabene

Bouaziz

et al. 2022

Lecture Notes in Networks and Systems

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A flexible method for the automated offline-detection of artifacts in multi-channel electroencephalogram recordings

Waser

Garn

Benke

et al. 2017

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Electroencephalogram (EEG) signal quality is often compromised by artifacts that corrupt quantitative EEG measurements used in clinical applications and EEG-related studies. Techniques such as filtering, regression analysis and blind source separation are often used to remove these artifacts. However, these preprocessing steps do not allow for complete artifact correction. We propose a method for the automated offline-detection of remaining artifacts after preprocessing in multi-channel EEG recordings. In contrast to existing methods it requires neither adaptive parameters varying between recordings nor a topography template. It is suited for short EEG segments and is flexible with regard to target applications. The algorithm was developed and tested on 60 clinical EEG samples of 20 seconds each that were recorded both in resting state and during cognitive activation to gain a realistic artifact set. Five EEG features were used to quantify temporal and spatial signal variations. Two distance measures for the single-channel and multi-channel variations of these features were defined. The global thresholds were determined by three-fold cross-validation and Youden's J statistic in conjunction with receiver operating characteristics (ROC curves). We observed high sensitivity of 95.5%±4.8 and specificity of 88.8%±2.1. The method has thus shown great potential and is promising as a possible tool for both EEG-based clinical applications and EEG-related research.

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EEG Artifact Detection Using Spatial Distribution of Rhythmicity

Cited by 3 publications

References 6 publications

Medical Technology: A Systematic Review on Medical Devices Utilized for Epilepsy Prediction and Management

Medical Technology: A Systematic Review on Medical Devices Utilized for Epilepsy Prediction and Management

A Convolutional Neural Network for Artifacts Detection in EEG Data

A flexible method for the automated offline-detection of artifacts in multi-channel electroencephalogram recordings

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