An Evolving-Dynamic Network Activity Approach toEpileptic Seizure Prediction using Machine Learning
Liu, C. J.; Sorokin, J.; Ganguli, S.; Huguenard, J.
Show abstract
Absence epilepsy is a neurological condition characterized by abnormally synchronous electrical activity within two mutually connected brain regions, the thalamus and cortex, that results in seizures and affects more than 6.5 million people. Epilepsy is commonly studied through the use of the electroencephalogram (EEG), a device that monitors brain waves over time. In this study, we introduced machine learning models to predict epileptic seizures in two ways, one to train logistic regression models to provide an accurate decision boundary to predict based off frequency features, and second to train convolutional neural networks to predict based off spectral power images from EEG. This pipeline employed a two model approach, using logistic regression and convolutional neural networks to predict seizures. The evaluation, performed on data from 9 mice, achieved prediction accuracies of 98%. The proposed methodology introduces a novel aspect of looking at predicting absence seizures, which are known to be short events, in addition to the comparison between a time-dependent and time-agnostic seizure prediction classifier. The overall goal of these experiments were to build a model that can accurately predict whether or not a seizure will occur.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Wavelet Phase Coherence of Ictal Scalp EEG-Extracted Muscle Activity (SMA) as a Biomarker for Sudden Unexpected Death in Epilepsy (SUDEP) 97%
- Automatic diagnostics of electroencephalography pathology based on multi-domain feature fusion 96%
- Epileptic seizure suppression: a computational approach for identification and control using real data 95%
Similar papers in this journal
- NLP-based tools for localization of the Epileptogenic Zone in patients with drug-resistant focal epilepsy 97%
- Dynamic Multiday Seizure Cycles in a Tetanus Toxin Rat Model of Epilepsy: Evolving Rhythms and Implications for Prediction 96%
- Dynamic network properties of the interictal brain determine whether seizures appear focal or generalised 95%
Similar papers in this journal
Similar papers in this journal
- Data-driven method to infer the seizure propagation patterns in an epileptic brain from intracranial electroencephalography 96%
- Evidence for spreading seizure as a cause of theta-alpha activity electrographic pattern in stereo-EEG seizure recordings 95%
- Active probing to highlight approaching transitions to ictal states in coupled neural mass models 94%
Similar papers in this journal
- Evaluating three different adaptive decomposition methods for EEG signal seizure detection and classification 96%
- A Transfer Entropy-based methodology to analyze information flow under eyes-open and eyes-closed conditions with a clinical perspective 94%
- SingleChannelNet: A Model for Automatic Sleep Stage Classification with Raw Single-Channel EEG 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.