A design of bat-based optimized deep learning model for EEG signal analysis

Abstract

Depression is a mental illness that negatively affects a person’s thinking, action, and feeling. Thus the rate of depression is identified by analysing Electroencephalogram (EEG) signals. Because of noise, the problem of classifying depression rate has some issues, such as low accuracy and required high training time. In this research work, a novel Bat-based U-NET Signal Analysis (BUSA) architecture is developed to estimate the patient’s depression rate with an EEG dataset. This technique involves pre-processing, feature selection, feature extraction, and classification. After the data training, the pre-processing function was activated to neglect the noise in the brain signal. Hereafter, the noiseless Signal is used for the further process. Here, the bat algorithm mimics the behaviour of the bat’s frequency and loudness, increasing the accuracy of prediction and classification. This fitness function is upgraded in the U-NET classification phase. Moreover, the brain signal’s feature selection and depression rate were classified using the bat fitness that has helped to gain the desired output. Finally, the performance metrics of the proposed BUSA technique are compared with other existing methods regarding the accuracy, AUC, precision, recall, and power. The proposed BUSA framework attained a high accuracy rate of about 99.64%, a maximum precision level of approximately 99.98%, a high recall rate of approximately 99.95%, and a high AUC of approximately 99.2%. The developed framework has attained better results in classifying depression rates.