Investigating population-specific epilepsy detection from noisy EEG signals using deep-learning models.

"Epilepsy is a chronic brain disorder that affects people of all ages. The cause of epilepsy is often unknown and its effect in different age groups is not yet investigated. The main objective of this study is to introduce a novel approach that successfully detects epilepsy even from noisy EEG signals. In addition, this study also investigates population specific epilepsy detection for providing novel insights. Correspondingly, we utilized the TUH EEG corpus database, publicly available challenging multi-channel EEG database containing detailed patient information. We applied a band-pass filter and manual noise rejection to remove noise and artifacts from EEG signals. We then utilized statistical features and correlation to select channels, and applied different transform analysis methods such as continuous wavelet transform, spectrogram, and Wigner-Ville distribution, with and without ensemble averaging, to construct an image dataset. Afterwards, we used various deep-learning models for general analysis. Our findings suggest that different models such as DenseNet201, DenseNet169, DenseNet121, VGG16, VGG19, Xception, InceptionV3, and MobileNetV2 performed better while using images generated from different approaches in general analysis. Furthermore, we split the dataset into two sections according to age for population analysis. All the models that performed well in the general analysis were used for population analysis, which provided novel insights in epilepsy detection from EEG. Our proposed framework for epilepsy detection achieved 100% accuracy, which outperforms other concurrent methods."