Comparison of the EEG Signal Classifiers LDA, NBC and GNBC Based on Time-Frequency Features

Szuflitowska, Beata; Orłowski, Przemysław

doi:10.14313/par_224/39

Cited by 7 publications

(2 citation statements)

References 25 publications

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“…In this classi er, the input data is assigned to the class that has the most similarity with the statistical distribution of that class data. [26, [31][32] Here, the normal distribution of data has been considered, and GNB is used to classify the data.…”

Section: Nbmentioning

confidence: 99%

A Novel Channel Selection Approach for Human Neonate’s Pain EEG Data Analysis

Talebi

Frounchi

Tazehkand

2022

Preprint

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Quantitative measurement of pain using the Electroencephalogram (EEG) signals has received much attention, recently. Pain EEG data processing is associated with complexity and high computational cost. This study aims to propose a new method for selecting efficient EEG channels to determine the area of the scalp that contains the most information about brain activity during acute pain in neonates. Also, selecting relevant channels in pain assessment reduces computational costs. In this study, a new channel selection approach is proposed, which is a combination of filter and wrapper methods. A new pseudo-Sequential Forward Feature Selection (pseudo-SFFS) method is presented to reduce the computational complexity of wrapper methods. We preprocessed data by applying a bandpass filter. We used wavelet transform to extract features. After extracting the features, we applied two feature selection steps. In the first step, we applied the T-test to the extracted features. In the second step, we selected the effective channels based on the output of the applied pseudo-SFFS algorithm into Support Vector Machine (SVM), Decision Tree (DT), and Gaussian Naive Bayesian (GNB) classifiers. Using the proposed method two channels of the sensorimotor cortex including Cz and C4 channels have been selected from 18 EEG channels for pain stimulation through the left heel of neonates. Also, the results show that most of the acute pain information of neonates is related to the delta and theta frequency bands.

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Section: Nbmentioning

confidence: 99%

A Novel Channel Selection Approach for Human Neonate’s Pain EEG Data Analysis

Talebi

Frounchi

Tazehkand

2022

Preprint

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“…Kovcs et al [14] and Samiee et al [15] used similar features with an ANN classifier and the Bonn dataset and achieved an accuracy of 98.1%. Szuflitowska and Orlowski [16] implemented a similar approach and obtained an accuracy of 85%.…”

Section: Fourier-based Epilepsy Seizure Detectionmentioning

confidence: 99%

Statistical-Hypothesis-Aided Tests for Epilepsy Classification

et al. 2019

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In this paper, an efficient, accurate, and nonparametric epilepsy detection and classification approach based on electroencephalogram (EEG) signals is proposed. The proposed approach mainly depends on a feature extraction process that is conducted using a set of statistical tests. Among the many existing tests, those fit with processed data and for the purpose of the proposed approach were used. From each test, various output scalars were extracted and used as features in the proposed detection and classification task. Experiments that were conducted on the basis of a Bonn University dataset showed that the proposed approach had very accurate results ( 98.4 % ) in the detection task and outperformed state-of-the-art methods in a similar task on the same dataset. The proposed approach also had accurate results ( 94.0 % ) in the classification task, but it did not outperform state-of-the-art methods in a similar task on the same dataset. However, the proposed approach had less time complexity in comparison with those methods that achieved better results.

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Analysis of Parameters Distribution of EEG Signals for Five Epileptic Seizure Phases Modeled by Duffing Van Der Pol Oscillator

Szuflitowska

Orłowski

2022

Computational Science – ICCS 2022

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Comparison of the EEG Signal Classifiers LDA, NBC and GNBC Based on Time-Frequency Features

Cited by 7 publications

References 25 publications

A Novel Channel Selection Approach for Human Neonate’s Pain EEG Data Analysis

A Novel Channel Selection Approach for Human Neonate’s Pain EEG Data Analysis

Statistical-Hypothesis-Aided Tests for Epilepsy Classification

Analysis of Parameters Distribution of EEG Signals for Five Epileptic Seizure Phases Modeled by Duffing Van Der Pol Oscillator

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