Why the study?
Visual analysis of EEG data is time-consuming and subjective, and existing classification models typically focus on single diseases rather than a unified framework for multiple neurological disorders.
Population
Four real-time EEG datasets
Comparison
Four machine learning-based classifiers applied to textural features of spectrogram images
Design
Machine learning development and validation study
Key result
A machine learning framework utilizing tCENTRIST feature extraction and a Support Vector Machine classifier achieved an overall accuracy of 88.78% in classifying four neurological abnormalities and healthy controls from EEG data.
Authors
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May aid automated EEG interpretation in neurology; leaves open external validation before clinical adoption.
A unified machine learning framework using spectrogram images and textural features can effectively classify multiple neurological abnormalities from EEG data with high accuracy.
Tawhid et al. (2022) studied Neurological abnormalities (Autism Spectrum Disorder, Epilepsy, Parkinson's disease, Schizophrenia) (n=86). tCENTRIST feature extraction with Support Vector Machine (SVM) classifier vs. Other machine learning classifiers (cCENTRIST, kNN, RF, LDA) was evaluated on Classification accuracy for five-class categorization (ASD vs EP vs PD vs SZ vs HC). A machine learning framework utilizing tCENTRIST feature extraction and a Support Vector Machine classifier achieved an overall accuracy of 88.78% in classifying four neurological abnormalities and healthy controls from EEG data.