GACNN SleepTuneNet: a genetic algorithm designing the convolutional neuralnetwork architecture for optimal classification ofsleep stages from a single EEG channel

Abstract

This study presents a method for designing-by a genetic algorithm, without manual intervention-the feature learning architecture for classification of sleep stages from a single EEG channel, when using a convolutional neural network called GACNN SleepTuneNet. Two EEG electrode positions were selected, namely FP2-F4 and FPz-Cz, from two available datasets. Twenty-five generations were involved in diagnosis without hand-crafted features, to learn the architecture for classification of sleep stages based on AASM standard. Based on the results, our model not only achieved the highest classification accuracy, but it also distinguished the sleep stages based on either of the two EEG electrode signals, in both datasets. The results show that our model performed the best with highest overall accuracy rates and kappa statistic (CAP sleep: 95.61% and 0.94; Sleep EDF: 92.51% and 0.90) among other state-of-the-art methods that require no manual intervention. Our model could automatically learn the features for classification of sleep stages, for different raw EEG electrode positions in different datasets, without user-assisted feature extraction.

Convolutional-Neural-Network Assisted Segmentation and SVM Classification of Brain Tumor in Clinical MRI Slices

“…The earlier works [11,16,17,23] confirmed that the combination of DF and HF (DF+HF) will improve the performance of deep-learning system. In this work, the essential HF from the brain MRI slices are mined using the well known methods such as GLCM [3,10,29], Hu [3,10,30] and LBP [31,32]. The GLCM is widely adopted due to its superior performance and the essential GLCM parameters of the MRI slices are extracted from the segmented BT by VGG-UNet.…”