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Inspired by recent advances in neural memory networks (NMNs), we introduce a novel approach for the classification of seizure type using electrophysiological data.
However, on the detection of anomalies, the complexity of the network and computing precision are adaptively increased for accurate predictions.
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.