Intelligent System for Screening Epileptic Seizures in the Erbil Electroencephalogram Epilepsy Dataset Images Utilizing Cascaded Histogram of Oriented Gradients-Gray Level Co-occurrence Matrix Features

View Full Paper

Ask AI

Bookmark

Authors

Discussion

Novel approach detects epileptic seizures in EEG images, suggesting improved accuracy with HOG-GLCM features.

The cascaded HOG-GLCM method achieved approximately 98.57% accuracy in detecting seizures.
Classification using SVM and KNN performed exceptionally, surpassing simpler methods significantly.
Statistical feature extraction combines HOG and GLCM to enhance detection capabilities in EEG images.
The study indicates single-feature approaches result in 5-8% lower accuracy compared to combined features.

Mohammed et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark

View Full Paper

Ask AI

Bookmark

Authors

Novel approach detects epileptic seizures in EEG images, suggesting improved accuracy with HOG-GLCM features.

The cascaded HOG-GLCM method achieved approximately 98.57% accuracy in detecting seizures.
Classification using SVM and KNN performed exceptionally, surpassing simpler methods significantly.
Statistical feature extraction combines HOG and GLCM to enhance detection capabilities in EEG images.
The study indicates single-feature approaches result in 5-8% lower accuracy compared to combined features.

Mohammed et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark