Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Non-Gaussianity Detection of EEG Signals Based on a Multivariate Scale
  Mixture Model for Diagnosis of Epileptic Seizures   Objective: The detection of epileptic seizures from scalp
electroencephalogram (EEG) signals can facilitate early diagnosis and
treatment. Previous studies suggested that the Gaussianity of EEG distributions
changes depending on the presence or absence of seizures; however, no general
EEG signal models can explain such changes in distributions within a unified
scheme. Methods: This paper describes the formulation of a stochastic EEG model
based on a multivariate scale mixture distribution that can represent changes
in non-Gaussianity caused by stochastic fluctuations in EEG. In addition, we
propose an EEG analysis method by combining the model with a filter bank and
introduce a feature representing the non-Gaussianity latent in each EEG
frequency band. Results: We applied the proposed method to multichannel EEG
data from twenty patients with focal epilepsy. The results showed a significant
increase in the proposed feature during epileptic seizures, particularly in the
high-frequency band. The feature calculated in the high-frequency band allowed
highly accurate classification of seizure and non-seizure segments [area under
the receiver operating characteristic curve (AUC) = 0.881] using only a simple
threshold. Conclusion: This paper proposed a multivariate scale mixture
distribution-based stochastic EEG model capable of representing non-Gaussianity
associated with epileptic seizures. Experiments using simulated and real EEG
data demonstrated the validity of the model and its applicability to epileptic
seizure detection. Significance: The stochastic fluctuations of EEG quantified
by the proposed model can help detect epileptic seizures with high accuracy.

21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
1
The categories of this paper are: cs
7
The categories of this paper are: hep-th
12
The categories of this paper are: nucl-th
14
The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc