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Using the MENN, the highest mean accuracy is 98. 34%.
In this study, we propose a new dimensionality reduction framework for reducing the dimension of CNN inputs based on the tensor decomposition of the time-frequency representation of EEG signals.
SOTA for Seizure Detection on CHB-MIT
In this work, we propose new objective functions to train deep neural network based density ratio estimators and apply it to a change point detection problem.
Commonly used deep learning models for time series don't offer a way to leverage structural information, but this would be desirable in a model for structural time series.
Epileptic seizure detection and classification in clinical electroencephalogram data still is a challenge, and only low sensitivity with a high rate of false positives has been achieved with commercially available seizure detection tools, which usually are patient non-specific.
Automatic classification of epliptic seizure types in EEG datacould enable more precise diagnosis and efficient manage-ment of the disease.
In this work, we propose an automated epileptic seizure detection method by applying a two-step minimization technique: first, we reduce the data points using a statistical sampling technique and then, we minimize the number of features using our novel feature selection algorithm.
Dynamical Component Analysis (DyCA) is a recently-proposed method to detect projection vectors to reduce the dimensionality of multi-variate deterministic datasets.
On that note, in this paper, we undertake the first study to explore the application of machine learning algorithms for multi-class seizure type classification.