ArticleSensors (Basel, Switzerland)2021
A Machine Learning Approach Involving Functional Connectivity Features to Classify Rest-EEG Psychogenic Non-Epileptic Seizures from Healthy Controls.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Advancements and Challenges of Artificial Intelligence-Assisted Electroencephalography in Epilepsy Management.Journal of clinical medicine · 2025Review
- Enhancing detection of epileptic seizures using transfer learning and EEG brain activity signals.Computational and structural biotechnology journal · 2025Article
- Virtual Reality in Acute and Chronic Pain Medicine: An Updated Review.Current pain and headache reports · 2024Review
- Neuronal avalanches in temporal lobe epilepsy as a noninvasive diagnostic tool investigating large scale brain dynamics.Scientific reports · 2024Article
- Differentiating Epileptic and Psychogenic Non-Epileptic Seizures Using Machine Learning Analysis of EEG Plot Images.Sensors (Basel, Switzerland) · 2024Article
- Research on Brain Networks of Human Balance Based on Phase Estimation Synchronization.Brain sciences · 2024Article
- Characteristics of Resting-State Electroencephalogram Network in α-Band of Table Tennis Athletes.Brain sciences · 2024Article
- Efficient lung cancer detection using computational intelligence and ensemble learning.PloS one · 2024Article
- Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist.Current neurology and neuroscience reports · 2023Review
- Effective Early Detection of Epileptic Seizures through EEG Signals Using Classification Algorithms Based on t-Distributed Stochastic Neighbor Embedding and K-Means.Diagnostics (Basel, Switzerland) · 2023Article
- Automatic Seizure Detection and Prediction Based on Brain Connectivity Features and a CNNs Meet Transformers Classifier.Brain sciences · 2023Article
- Machine Learning Techniques Reveal Aberrated Multidimensional EEG Characteristics in Patients with Depression.Brain sciences · 2023Article
- Vision Transformers in Image Restoration: A Survey.Sensors (Basel, Switzerland) · 2023Article
- A Novel Hierarchical Extreme Machine-Learning-Based Approach for Linear Attenuation Coefficient Forecasting.Entropy (Basel, Switzerland) · 2023Article
- Limited clinical validity of univariate resting-state EEG markers for classifying seizure disorders.Brain communications · 2023Article
- A Method for the Study of Cerebellar Cognitive Function-Re-Cognition and Validation of Error-Related Potentials.Brain sciences · 2022Article
- Applying Multiple Functional Connectivity Features in GCN for EEG-Based Human Identification.Brain sciences · 2022Article
- Temporal and Spatial Analysis of Alzheimer's Disease Based on an Improved Convolutional Neural Network and a Resting-State FMRI Brain Functional Network.International journal of environmental research and public health · 2022Article
- Mental Health Intent Recognition for Arabic-Speaking Patients Using the Mini International Neuropsychiatric Interview (MINI) and BERT Model.Sensors (Basel, Switzerland) · 2022Article
- Diagnosing epileptic seizures using combined features from independent components and prediction probability from EEG data.Digital healthArticle
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Authors and funding
12 authors.
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Abstract
Until now, clinicians are not able to evaluate the Psychogenic Non-Epileptic Seizures (PNES) from the rest-electroencephalography (EEG) readout. No EEG marker can help differentiate PNES cases from healthy subjects. In this paper, we have investigated the power spectrum density (PSD), in resting-state EEGs, to evaluate the abnormalities in PNES affected brains. Additionally, we have used functional connectivity tools, such as phase lag index (PLI), and graph-derived metrics to better observe the integration of distributed information of regular and synchronized multi-scale communication within and across inter-regional brain areas. We proved the utility of our method after enrolling a cohort study of 20 age- and gender-matched PNES and 19 healthy control (HC) subjects. In this work, three classification models, namely support vector machine (SVM), linear discriminant analysis (LDA), and Multilayer perceptron (MLP), have been employed to model the relationship between the functional connectivity features (rest-HC versus rest-PNES). The best performance for the discrimination of participants was obtained using the MLP classifier, reporting a precision of 85.73%, a recall of 86.57%, an F1-score of 78.98%, and, finally, an accuracy of 91.02%. In conclusion, our results hypothesized two main aspects. The first is an intrinsic organization of functional brain networks that reflects a dysfunctional level of integration across brain regions, which can provide new insights into the pathophysiological mechanisms of PNES. The second is that functional connectivity features and MLP could be a promising method to classify rest-EEG data of PNES form healthy controls subjects.
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