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Found 6 papers, 2 papers with code
SeizeIT2: Wearable Dataset Of Patients With Focal Epilepsy
The increasing technological advancements towards miniaturized physiological measuring devices have enabled continuous monitoring of epileptic patients outside of specialized environments.
Multimodal wearable EEG, EMG and accelerometry measurements improve the accuracy of tonic-clonic seizure detection in-hospital
The combination of wearable EEG and EMG achieved overall the most clinically useful performance in offline TCS detection with a sensitivity of 97. 7%, a FPR of 0. 4/24 h, a precision of 43. 0%, and a F1-score of 59. 7%.
Machine-Learning-Based Diagnostics of EEG Pathology
The results demonstrate that the proposed feature-based decoding framework can achieve accuracies on the same level as state-of-the-art deep neural networks.
Early Seizure Detection with an Energy-Efficient Convolutional Neural Network on an Implantable Microcontroller
Most approaches for early seizure detection in the literature are, however, not optimized for implementation on ultra-low power microcontrollers required for long-term implantation.
Intracranial Error Detection via Deep Learning
Deep learning techniques have revolutionized the field of machine learning and were recently successfully applied to various classification problems in noninvasive electroencephalography (EEG).
A single channel sleep-spindle detector based on multivariate classification of EEG epochs: MUSSDET.
The classification of normalized EEG epochs in a multidimensional space, as well as the use of a validation set, allowed to objectively define a single detection threshold for all databases and participants.
Ranked #6 on
Spindle Detection
on MASS SS2
