Seizure prediction with long-term iEEG recordings: What can we learn from data nonstationarity?

@article{Yang2021SeizurePW,
  title={Seizure prediction with long-term iEEG recordings: What can we learn from data nonstationarity?},
  author={Hongliu Yang and Matthias Eberlein and Jens M{\"u}ller and Ronald Tetzlaff},
  journal={2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)},
  year={2021},
  pages={1-6}
}
Repeated epileptic seizures impair around 65 million people worldwide and a successful prediction of seizures could significantly h elp p atients suffering from refractory epilepsy. For two dogs with yearlong intracranial electroencephalography (iEEG) recordings, we studied the influence of time series nonstationarity on the performance of seizure prediction using in-house developed machine learning algorithms. We observed a long-term evolution on the scale of weeks or months in iEEG time… 

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SHOWING 1-10 OF 22 REFERENCES
Evaluation of machine learning methods for seizure prediction in epilepsy
TLDR
New seizure prediction results are presented including a performance comparison of different methods based on a new set of intracranial EEG data that has been recorded in a working group during presurgical evaluation.
Efficient Epileptic Seizure Prediction Based on Deep Learning
TLDR
A novel patient-specific seizure prediction technique based on deep learning and applied to long-term scalp electroencephalogram (EEG) recordings is proposed to accurately detect the preictal brain state and differentiate it from the prevailing interictal state as early as possible and make it suitable for real time usage.
Focal Onset Seizure Prediction Using Convolutional Networks
TLDR
It is demonstrated that a robust set of features can be learned from scalp EEG that characterize the preictal state of focal seizures, and the results significantly outperform a random predictor and other seizure prediction algorithms.
Ensembling crowdsourced seizure prediction algorithms using long‐term human intracranial EEG
TLDR
The results suggest that for the specific algorithms, evaluation framework, and data considered here, incremental improvements are achievable but there may be upper bounds on machine learning–based seizure prediction performance for some patients whose seizures are challenging to predict.
Epilepsyecosystem.org: crowd-sourcing reproducible seizure prediction with long-term human intracranial EEG
TLDR
A crowd-sourcing ecosystem for seizure prediction is presented involving an international competition, a follow-up held-out data evaluation, and an online platform, Epilepsyecosystem.org, for yielding further improvements in prediction performance.
Seizure prediction: the long and winding road.
TLDR
A critically discuss the literature on seizure prediction and address some of the problems and pitfalls involved in the designing and testing of seizure-prediction algorithms, and point towards possible future developments and propose methodological guidelines for future studies on seizure predictions.
Convolutional Neural Networks for Epileptic Seizure Prediction
TLDR
A novel methodology for the classification of intracranial electroencephalography (iEEG) for seizure prediction using a convolutional neural network (CNN) topology for both the determination of suitable signal characteristics and the binary classification of preictal and interictal segments is presented.
Coherent false seizure prediction in epilepsy, coincidence or providence?
Automated seizure prediction
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