scholarly journals Classification of epileptic seizure using feature selection based on fuzzy membership from EEG signal

2021 ◽  
Vol 29 ◽  
pp. 519-529
Author(s):  
Sang-Hong Lee
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
High Performance ◽  
Fuzzy Membership ◽  
Eeg Signal ◽  
Performance Result ◽  
The Mean ◽  
New Feature ◽  
Background Feature

BACKGROUND: Feature selection is a technology that improves the performance result by eliminating overlapping or unrelated features. OBJECTIVE: To improve the performance result, this study proposes a new feature selection that uses the distance between the centers. METHODS: This study uses the distance between the centers of gravity (DBCG) of the bounded sum of the weighted fuzzy memberships (BSWFMs) supported by a neural network with weighted fuzzy membership (NEWFM). RESULTS: Using distance-based feature selection, 22 minimum features with a high performance result are selected, with the shortest DBCG of BSWFMs removed individually from the initial 24 features. The NEWFM used 22 minimum features as inputs to obtain a sensitivity, accuracy, and specificity of 99.3%, 99.5%, and 99.7%, respectively. CONCLUSIONS: In this study, only the mean DBCG is used to select the features; in the future, however, it will be necessary to incorporate statistical methods such as the standard deviation, maximum, and normal distribution.

Wavelet Transform Based Feature Extraction for EEG Signal Classification

2021 ◽  
Vol 20 ◽  
pp. 199-206
Author(s):  
Seda Postalcioglu
Keyword(s):  
Wavelet Transform ◽  
High Performance ◽  
Feature Vector ◽  
Fast Response ◽  
Eeg Signal ◽  
Real Time Control ◽  
Time Control ◽  
Band Power ◽  
Performance Rate

This study focused on the classification of EEG signal. The study aims to make a classification with fast response and high-performance rate. Thus, it could be possible for real-time control applications as Brain-Computer Interface (BCI) systems. The feature vector is created by Wavelet transform and statistical calculations. It is trained and tested with a neural network. The db4 wavelet is used in the study. Pwelch, skewness, kurtosis, band power, median, standard deviation, min, max, energy, entropy are used to make the wavelet coefficients meaningful. The performance is achieved as 99.414% with the running time of 0.0209 seconds

scholarly journals Computational Classification and Diagnosis of Alcoholic Liver Diseases Using General Regression Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Naiping Li ◽  
Yongfang Jiang ◽  
Jin Ma ◽  
Bo He ◽  
Wei Tang ◽  
...  
Keyword(s):  
Neural Network ◽  
Liver Diseases ◽  
High Performance ◽  
Liver Function Tests ◽  
General Regression Neural Network ◽  
Diagnosis And Classification ◽  
Tenfold Cross Validation ◽  
Alcoholic Liver Diseases ◽  
General Regression

Alcoholic liver diseases cause high incidence of death worldwide. However, computational diagnosis and classification of alcoholic hepatitis have not yet been established. In this study, we used general regression neural network (GRNN) model with a high-performance classification ability to diagnose and classify alcohol hepatitis. We used tenfold cross-validation to demonstrate the error rate of networks. The results show an accuracy of 80.91% of the back diagnosis in 110 patients and the accuracy of 81.82% of predicting-diagnosis in 11 patients referring to the clinical diagnosis made by a group of experts. This study suggested that using the liver function tests as the input layer variables of GRNN model could accurately diagnose and classify alcoholic liver diseases.

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