AIMC Topic: Seizures

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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 Support Vector Machine Electroencephalography Deep Learning
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A Novel State Space Model with Dynamic Graphic Neural Network for EEG Event Detection.

International journal of neural systems Dec 31, 2024
Electroencephalography (EEG) is a widely used physiological signal to obtain information of brain activity, and its automatic detection holds significant research importance, which saves doctors' time, improves detection efficiency and accuracy. Howe...
Humans Neural Networks, Computer Signal Processing, Computer-Assisted Brain Electroencephalography Sleep Stages Seizures
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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 ...
Humans Female Deep Learning Electroencephalography Epilepsy Adult Male Seizures
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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...
Electroencephalography Epilepsy Signal Processing, Computer-Assisted Attention Seizures Algorithms Sensitivity and Specificity Humans Deep Learning Pattern Recognition, Automated Neural Networks, Computer Machine Learning
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DCSENets: Interpretable deep learning for patient-independent seizure classification using enhanced EEG-based spectrogram visualization.

Computers in biology and medicine Dec 20, 2024
Neurologists often face challenges in identifying epileptic activities within multichannel EEG recordings, requiring extensive hours of analysis. Computer-aided diagnosis systems have been proposed to reduce manual inspection of EEG signals by neurol...
Diagnosis, Computer-Assisted Algorithms Humans Female Deep Learning Electroencephalography Male Seizures Signal Processing, Computer-Assisted
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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...
Anticonvulsants Electroencephalography Epilepsy Seizures Humans Machine Learning
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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 ...
Seizures Electroencephalography Epilepsy Humans Child, Preschool Child Machine Learning
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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 Epilepsy Seizures Female Neural Networks, Computer Deep Learning Machine Learning Humans Adult Male Algorithms Electroencephalography
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Analysis of the impact of deep learning know-how and data in modelling neonatal EEG.

Scientific reports Nov 14, 2024
The performance gains achieved by deep learning models nowadays are mainly attributed to the usage of ever larger datasets. In this study, we present and contrast the performance gains that can be achieved via accessing larger high-quality datasets v...
Deep Learning Humans Electroencephalography Infant, Newborn Seizures
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Anchoring temporal convolutional networks for epileptic seizure prediction.

Journal of neural engineering Nov 8, 2024
. Accurate and timely prediction of epileptic seizures is crucial for empowering patients to mitigate their impact or prevent them altogether. Current studies predominantly focus on short-term seizure predictions, which causes the prediction time to ...
Humans Time Factors Algorithms Epilepsy Neural Networks, Computer Electroencephalography Seizures
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