Epileptic seizure detection using scalogram-based hybrid CNN model on EEG signals

• Published in: Signal, image and video processing Vol. 18; no. 2; pp. 1577 - 1588
• Main Authors: Sadam, Sesha Sai Priya, Nalini, N. J.
• Format: Journal Article
• Language: English
• Published: London Springer London 01-03-2024; Springer Nature B.V
• Subjects: Artificial neural networks; Brain; Computer Imaging; Computer Science; Continuous wavelet transform; Convulsions & seizures; Deep learning; Electroencephalography; Epilepsy; Feature extraction; Image Processing and Computer Vision; Image quality; Machine learning; Medical imaging; Multimedia Information Systems; Neurological diseases; Noise levels; Original Paper; Pattern Recognition and Graphics; Seizures; Signal,Image and Speech Processing; Support vector machines; Vision; Wavelet transforms; CWT; SD-CNN; Scaleogram; SVM; Epilepsy; EEG
• ISSN: 1863-1703; 1863-1711
• DOI: 10.1007/s11760-023-02871-x

Epilepsy is one of the most usual neurological diseases characterized by abnormal brain activity, resulting in seizures or strange behavior, sensations, and, in some cases, loss of consciousness. It is a persistent, non-communicable brain condition that can affect anyone at any age, nearly 50 million people globally, with about 80% of sufferers living in low- and middle-income countries. Electroencephalography (EEG) signals are largely used in epilepsy research to examine brain activity during seizures. The extraction of features and selection from EEG signals plays a major role in epileptic seizure detection. In traditional machine learning techniques, the hard-core feature extraction needs domain expertise, and this can be eliminated by deep learning. The benefits of deep learning techniques are they try to learn high-level features from the input signals in an incremental method. To meet the requirements of complicated feature engineering, deep learning techniques have received greater attention than conventional methods. A hybrid seizure detection-convolutional neural network and vector machine (SD-CNN and SVM) model is proposed for epileptic seizure detection with EEG signals. Transformation of signal to image is performed using continuous wavelet transform technique to generate scaleogram images and also SD-CNN works as a learnable feature extractor from the generated images and SVM works as a binary classifier. The experimental results extracted 94% with high quality of scaleogram images using hybrid SD-CNN and SVM model and removed the noise levels and time–frequency data from EEG signals.