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Fyodor Dostoevsky, a 19th-century Russian novelist with epilepsy, incorporated his personal experience with seizure disorder into his novels, particularly in The Brothers Karamazov where he explores family dynamics and uses psychogenic non-epileptic seizures as a narrative element in the character Smerdyakov's criminal alibi.
This study proposes a deep learning approach for predicting epileptic seizures using EEG signal analysis, employing convolutional neural networks for feature extraction and Bi-LSTM for classification. The method achieved 99.61% accuracy and aims to improve patient quality of life through early seizure detection and timely intervention.
This systematic review of 37 studies examines how machine learning models can predict individual patient responses to anti-seizure medications by analyzing clinical data, brain imaging, EEG, and genetic information. While some models show promising accuracy, results vary widely, and the field faces significant limitations related to data quality and quantity that require further research and standardization.
This study examines temporal patterns and prognostic factors in seizures associated with glioblastoma, a primary brain malignancy. The research aims to characterize how seizure presentation timing relates to patient outcomes and disease progression.
This pharmacovigilance study analyzed FDA adverse event reports to investigate whether ALK inhibitors (crizotinib, ceritinib, alectinib, brigatinib, lorlatinib) are associated with seizures using Bayesian statistical methods. Results showed varying signal detection scores across different ALK inhibitors, with ceritinib, brigatinib, alectinib, and lorlatinib demonstrating stronger associations with seizure disorders compared to crizotinib.