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Epilepsy is a chronic neurological disease marked by recurrent, unpredictable seizures. These seizures are caused by abnormal electrical discharges in the brain, leading to behavior, sensation, or consciousness alterations. They can also cause transient impairment of awareness, interfering with daily activities.
Various factors can trigger epilepsy, including genetic factors, brain damage, metabolic causes, and unknown etiology. Diagnosis of epilepsy involves electroencephalography (EEG), which...

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Epilepsy Diagnosis from EEG Signals Using Continuous Wavelet Transform-Based Depthwise Convolutional Neural Network

Fırat Dişli¹, Mehmet Gedikpınar¹, Hüseyin Fırat²

¹Department of Electrical and Electronic Engineering, Faculty of Technology, Firat University, 23000 Elazig, Turkey.

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Summary

This summary is machine-generated.

This study introduces a novel deep learning model for automated epilepsy diagnosis using electroencephalogram (EEG) images. The developed system achieves high accuracy, offering a promising tool for neurologists.

Keywords:

continuous wavelet transform depthwise convolution epilepsy image concatenate

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Published on: January 29, 2018

Area of Science:

Neurology
Artificial Intelligence
Signal Processing

Background:

Epilepsy is a common neurological disorder causing seizures, necessitating reliable diagnostic tools.
Automated epilepsy diagnosis systems are crucial due to the unpredictable nature of seizures.
Electroencephalogram (EEG) signal analysis is key, with deep learning offering advanced capabilities.

Purpose of the Study:

To develop an automated epilepsy diagnosis system using a novel deep learning approach.
To leverage continuous wavelet transform and depthwise convolutional neural networks (DCNNs) for EEG analysis.
To enhance diagnostic accuracy and efficiency in epilepsy detection.

Main Methods:

EEG signals were converted into images using continuous wavelet transform.
Images were concatenated into a single input for a DCNN model.
A DCNN was trained and evaluated for epilepsy diagnosis.

Main Results:

The DCNN model achieved high performance metrics: 95.99% accuracy, 94.27% sensitivity, 97.29% specificity, and 96.34% precision.
Comparative analysis showed superior performance over existing methods.
The image concatenation technique proved effective for DCNN input.

Conclusions:

The proposed DCNN model with image concatenation offers a novel and effective method for epilepsy diagnosis.
This approach eliminates the need for additional classifiers or feature selection.
The system provides a valuable tool for supporting neurologists in epilepsy diagnosis and can be adapted to other datasets.