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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.
Calcified neurocysticercosis (NCC) is a major cause of acquired adult-onset epilepsy in low and middle-income countries, responsible for approximately 30% of seizure cases. Focal-onset seizures typically respond well to anti-seizure monotherapy, and these seizure disorders are largely preventable through appropriate management of NCC stages.
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 analyzed 136 studies to assess the humanistic and economic impact of focal epilepsy and primary generalized tonic-clonic seizures in adults, examining patient outcomes, quality of life, productivity loss, and caregiver burden. Findings demonstrate that epilepsy, particularly focal epilepsy, imposes substantial burden including poor work productivity, increased sick days, early retirement, and significant caregiver costs, with greater impact associated with high seizure frequency and medication resistance.
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.