EEG dynamical correlates of focal and diffuse causes of coma

Kafashan, MohammadMehdi; Ryu, Shoko; Hargis, Mitchell J; Laurido-Soto, Osvaldo; Roberts, Debra; Thontakudi, Akshay; Eisenman, Lawrence N.; Ching, ShiNung

doi:10.1186/s12883-017-0977-0

Cited by 10 publications

(3 citation statements)

References 57 publications

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“…The most typical and probably mentioned (kernel-based) algorithm for classification is known as Support Vector Machine (SVM) (Boser et al, 1992; Vapnik, 1995, 1998). An example of its application for DOC analysis can be found in Engemann et al (2015), Chennu et al (2017), and Kafashan et al (2017). Different approaches are given in Wielek et al (2018) where standard scoring rules developed by American Academy of Sleep Medicine were applied.…”

Section: Resultsmentioning

confidence: 99%

Computational Methods for Resting-State EEG of Patients With Disorders of Consciousness

Corchs

Chioma²,

Dondi

et al. 2019

Front. Neurosci.

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Patients who survive brain injuries may develop Disorders of Consciousness (DOC) such as Coma, Vegetative State (VS) or Minimally Conscious State (MCS). Unfortunately, the rate of misdiagnosis between VS and MCS due to clinical judgment is high. Therefore, diagnostic decision support systems aiming to correct any differentiation between VS and MCS are essential for the characterization of an adequate treatment and an effective prognosis. In recent decades, there has been a growing interest in the new EEG computational techniques. We have reviewed how resting-state EEG is computationally analyzed to support differential diagnosis between VS and MCS in view of applicability of these methods in clinical practice. The studies available so far have used different techniques and analyses; it is therefore hard to draw general conclusions. Studies using a discriminant analysis with a combination of various factors and reporting a cut-off are among the most interesting ones for a future clinical application.

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Section: Resultsmentioning

confidence: 99%

Computational Methods for Resting-State EEG of Patients With Disorders of Consciousness

Corchs

Chioma²,

Dondi

et al. 2019

Front. Neurosci.

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“…Scarpino et al [54] showed that specific EEG patterns were independent predictors of improved consciousness at discharge in UWS patients. Some studies showed that EEG provides accurate prognostic information in the early phase of coma [55][56][57]. Evoked potential examination plays an increasingly important role in predicting the rehabilitation and prognosis of patients with craniocerebral injury and disturbance of consciousness [58].…”

Section: Discussionmentioning

confidence: 99%

Transcranial Direct Current Stimulation Improves Some Neurophysiological Parameters but not Clinical Outcomes after Severe Traumatic Brain Injury

Sun¹,

Liu²,

Dong³

et al. 2023

J. Integr. Neurosci.

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Background: Disorders of consciousness (DOC) are one of the clinical hallmarks of severe traumatic brain injury (TBI). DOC impair patient life quality and increase the burden on their families and society. Methods: A double-blind, randomized, controlled clinical trial was conducted to determine the efficacy of routine rehabilitation combined with transcranial direct current stimulation (tDCS) in DOC patients after TBI. A total of 78 DOC patients were randomly divided after TBI into two groups: participants in the treatment group received routine rehabilitation combined with an active tDCS protocol. In contrast, participants in the control group received routine rehabilitation combined with a sham tDCS protocol. An anode was placed over the left dorsolateral prefrontal cortex and a cathode was placed over the right supraorbital area. The stimulation intensity was 2 mA. Both tDCS protocols lasted for eight consecutive weeks (20 minutes per day, six days per week). Patients were followed up for a further eight weeks. Glasgow Outcome Scale (GOS), Glasgow Coma Scale (GCS), brainstem auditory evoked potentials, somatosensory evoked potentials and electroencephalogram were measured at weeks zero, two, four, six, eight and sixteen from the start of tDCS. Results: Neither the GOS nor GCS scores differed significantly between the two groups, while brainstem auditory evoked potentials, somatosensory evoked potentials and electroencephalogram scores did. Conclusions: This study found that tDCS improves some neurophysiological parameters but not clinical outcomes of DOC patients after TBI. Clinical Trial Registration: Chinese Clinical Trial Registry, ChiCTR1800014808 (The version is V.1.0). Registered on February 7, 2018. http://www.chictr.org.cn/showproj.aspx?proj=25003.

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“…13,14,25,26 The support vector machine (SVM), a statistical machine learning algorithm, has been used in a wide range of biomedical applications, including automated seizure detection. 15,17,27–29…”

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confidence: 99%

Improved Patient-Independent System for Detection of Electrical Onset of Seizures

Veerasingam

Machireddy

Srinivasan

et al. 2019

Journal of Clinical Neurophysiology

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Purpose:To design a non–patient-specific system to detect the electrical onset of seizures in patients with temporal lobe epilepsy.Methods:We used EEG data from 29 seizures of 18 temporal lobe epilepsy patients who underwent multiday video-scalp EEG monitoring as part of their presurgical evaluations. We segmented each data set into preictal and ictal phases, and identified spectral entropy, spectral energy, and signal energy as useful features for discriminating normal and seizure conditions. The performance of five different classifiers was analyzed using these features to design an automated detection system.Results:Among the five classifiers, decision tree, k-nearest neighbor, and support vector machine performed with sensitivity (specificity) of 79% (81%), 75% (85%), and 80% (86%), respectively. The other two, linear discriminant algorithm and Naive Bayes classifiers, performed with sensitivity (specificity) of 54% (94%), 47% (96%), respectively.Conclusions:The support vector machine–based seizure detection system showed better detection capability in terms of sensitivity and specificity measures as compared to linear discriminant algorithm, Naive Bayes, decision tree, and k-nearest neighbor classifiers.Conclusions:Our study shows that a generalized system to detect the electrical onset of seizures in temporal lobe epilepsy using scalp-recorded EEG is possible. If confirmed on a larger data set, our findings may have significant implications for the management of seizures, especially in patients with drug-resistant epilepsy.

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EEG dynamical correlates of focal and diffuse causes of coma

Cited by 10 publications

References 57 publications

Computational Methods for Resting-State EEG of Patients With Disorders of Consciousness

Computational Methods for Resting-State EEG of Patients With Disorders of Consciousness

Transcranial Direct Current Stimulation Improves Some Neurophysiological Parameters but not Clinical Outcomes after Severe Traumatic Brain Injury

Improved Patient-Independent System for Detection of Electrical Onset of Seizures

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