AIMC Topic: Epilepsy

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A hybrid CNN-Bi-LSTM model with feature fusion for accurate epilepsy seizure detection.

BMC medical informatics and decision making Jan 6, 2025
BACKGROUND: The diagnosis and treatment of epilepsy continue to face numerous challenges, highlighting the urgent need for the development of rapid, accurate, and non-invasive methods for seizure detection. In recent years, advancements in the analys...
Humans Neural Networks, Computer Epilepsy Seizures Wavelet Analysis Deep Learning Support Vector Machine Electroencephalography
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A novel way to use cross-validation to measure connectivity by machine learning allows epilepsy surgery outcome prediction.

NeuroImage Dec 27, 2024
The rate of success of epilepsy surgery, ensuring seizure-freedom, is limited by the lack of epileptogenicity biomarkers. Previous evidence supports the critical role of functional connectivity during seizure generation to characterize the epileptoge...
Electrocorticography Machine Learning Epilepsy Brain Nerve Net Adolescent Humans Female Young Adult Treatment Outcome Adult Electroencephalography Male
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Preictal period optimization for deep learning-based epileptic seizure prediction.

Journal of neural engineering Dec 27, 2024
. Accurate seizure prediction could prove critical for improving patient safety and quality of life in drug-resistant epilepsy. While deep learning-based approaches have shown promising performance using scalp electroencephalogram (EEG) signals, the ...
Adult Male Deep Learning Humans Female Seizures Electroencephalography Epilepsy
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Epileptic seizure detection in EEG signals via an enhanced hybrid CNN with an integrated attention mechanism.

Mathematical biosciences and engineering : MBE Dec 25, 2024
Epileptic seizures, a prevalent neurological condition, necessitate precise and prompt identification for optimal care. Nevertheless, the intricate characteristics of electroencephalography (EEG) signals, noise, and the want for real-time analysis re...
Pattern Recognition, Automated Deep Learning Electroencephalography Epilepsy Machine Learning Seizures Humans Neural Networks, Computer Algorithms Signal Processing, Computer-Assisted Sensitivity and Specificity Attention
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Exploring the Versatility of Spiking Neural Networks: Applications Across Diverse Scenarios.

International journal of neural systems Dec 23, 2024
In the last few decades, Artificial Neural Networks have become more and more important, evolving into a powerful tool to implement learning algorithms. Spiking neural networks represent the third generation of Artificial Neural Networks; they have e...
Algorithms Action Potentials Epilepsy Neurons Humans Neural Networks, Computer
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Annotated interictal discharges in intracranial EEG sleep data and related machine learning detection scheme.

Scientific data Dec 18, 2024
Interictal epileptiform discharges (IEDs) such as spikes and sharp waves represent pathological electrophysiological activities occurring in epilepsy patients between seizures. IEDs occur preferentially during non-rapid eye movement (NREM) sleep and ...
Sleep Humans Algorithms Electrocorticography Machine Learning Electroencephalography Epilepsy
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Detecting fast-ripples on both micro- and macro-electrodes in epilepsy: A wavelet-based CNN detector.

Journal of neuroscience methods Dec 14, 2024
BACKGROUND: Fast-ripples (FR) are short (∼10 ms) high-frequency oscillations (HFO) between 200 and 600 Hz that are helpful in epilepsy to identify the epileptogenic zone. Our aim is to propose a new method to detect FR that had to be efficient for in...
Brain Waves Electrocorticography Signal Processing, Computer-Assisted Wavelet Analysis Neural Networks, Computer Humans Female Male Adult Electroencephalography Epilepsy
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How accurate are machine learning models in predicting anti-seizure medication responses: A systematic review.

Epilepsy & behavior : E&B Dec 13, 2024
IMPORTANCE: Current epilepsy management protocols often depend on anti-seizure medication (ASM) trials and assessment of clinical response. This may delay the initiation of the ASM regimen that might optimally balance efficacy and tolerability for in...
Machine Learning Electroencephalography Epilepsy Seizures Anticonvulsants Humans
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Accuracy of Machine Learning in Detecting Pediatric Epileptic Seizures: Systematic Review and Meta-Analysis.

Journal of medical Internet research Dec 11, 2024
BACKGROUND: Real-time monitoring of pediatric epileptic seizures poses a significant challenge in clinical practice. In recent years, machine learning (ML) has attracted substantial attention from researchers for diagnosing and treating neurological ...
Child Electroencephalography Epilepsy Machine Learning Child, Preschool Seizures Humans
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Digital Twin for EEG seizure prediction using time reassigned Multisynchrosqueezing transform-based CNN-BiLSTM-Attention mechanism model.

Biomedical physics & engineering express Dec 11, 2024
The prediction of epileptic seizures is a classical research problem, representing one of the most challenging tasks in the analysis of brain disorders. There is active research into digital twins (DT) for various healthcare applications, as they can...
Signal Processing, Computer-Assisted Electroencephalography Epilepsy Seizures Machine Learning Algorithms Female Adult Male Deep Learning Humans Neural Networks, Computer
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