首页|University College London (UCL) Reports Findings in Seizures (Nonictal electroen cephalographic measures for the diagnosis of functional seizures)
University College London (UCL) Reports Findings in Seizures (Nonictal electroen cephalographic measures for the diagnosis of functional seizures)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Nervous System Disease s and Conditions - Seizures is the subject of a report. According to news report ing out of London, United Kingdom, by NewsRx editors, research stated, “Function al seizures (FS) look like epileptic seizures but are characterized by a lack of epileptic activity in the brain. Approximately one in five referrals to epileps y clinics are diagnosed with this condition.” Our news journalists obtained a quote from the research from University College London (UCL), “FS are diagnosed by recording a seizure using video-electroenceph alography (EEG), from which an expert inspects the semiology and the EEG. Howeve r, this method can be expensive and inaccessible and can present significant pat ient burden. No single biomarker has been found to diagnose FS. However, the cur rent limitations in FS diagnosis could be improved with machine learning to clas sify signal features extracted from EEG, thus providing a potentially very usefu l aid to clinicians. The current study has investigated the use of seizure-free EEG signals with machine learning to identify subjects with FS from those with e pilepsy. The dataset included interictal and preictal EEG recordings from 48 sub jects with FS (mean age = 34.76 ? 10.55 years, 14 males) and 29 subjects with ep ilepsy (mean age = 38.95 ? 13.93 years, 18 males) from which various statistical , temporal, and spectral features from the five EEG frequency bands were extract ed then analyzed with threshold accuracy, five machine learning classifiers, and two feature importance approaches. The highest classification accuracy reported from thresholding was 60.67%. However, the temporal features were the best performing, with the highest balanced accuracy reported by the machine learning models: 95.71% with all frequency bands combined and a su pport vector machine classifier. Machine learning was much more effective than u sing individual features and could be a powerful aid in FS diagnosis.”
LondonUnited KingdomEuropeBrain Di seases and ConditionsCentral Nervous System Diseases and ConditionsCyborgsDiagnostics and ScreeningElectroencephalographyEmerging TechnologiesEpilep syHealth and MedicineMachine LearningNervous System Diseases and Condition sNeurologic ManifestationsSeizures
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