Dataset of rhythmicity spectrogram based images of seizure and non-seizure EEG signals

Artificial intelligence (AI) based automated epilepsy diagnosis has aimed to ease the burden of manual detection, prediction, and management of seizure and epilepsy-specific EEG signals for medical specialists. Existing research work in this domain has highlighted the significance of 2D EEG frames extracted through various processing pipelines over 1D signal analysis using various CNN architectures like AlexNet, LeNet. This is a pre-processed image (rhythmicity spectrogram) dataset generated from the CHB-MIT EEG scalp database. The dataset consists of 105 frames from chb01, 30 frames from chb02, 90 frames from chb05, and 75 frames from chb05 separately from both ictal and non-seizure edf files. The total image frames and ictal time (20 ictal signals) are 600 frames and 25 minutes respectively. The dataset has been divided into train, test and validate folders wherein seizure and non-seizure EEG images have been put in png format. It can be incorporated in the machine and deep learning pipelines for the detection of seizure and non-seizure EEG images.

For further technical details see the following publication: Handa, P., & Goel, N. (2021, August). Epileptic Seizure Detection Using Rhythmicity Spectrogram and Cross-Patient Test Set. In 2021 8th International Conference on Signal Processing and Integrated Networks (SPIN) (pp. 898-902). IEEE.