Dynamically stabilized recurrent neural network optimized with Artificial Gorilla Troops espoused Alzheimer ’s disorder detection using EEG signals

AbstractAlzheimer ’s disease is an incurable neurological disorder that damages cognitive abilities, but early identification reduces the symptoms significantly. The absence of competent healthcare professionals has made automatic identification of Alzheimer’s disease more crucial since it lessens the amount of w ork for staff members and improves diagnostic outcomes. The major aim of this work is “to develop a computer diagnostic scheme that makes it possible to identify AD using the Electroencephalogram (EEG) signal”. Therefore, Dynamically Stabilized Recurrent Neural Network Optimized with Artificial Gorilla Troops espoused Alzheimer’s Disorder Detection using EEG signals (DSRNN-AGTO-ADD) is proposed in this paper. Here, Dynamic Context-Sensitive Filter (DCSF) is considered to eliminate the noise, and interference from the EEG signal. Then Adaptive and Concise Empirical Wavelet Transform (ACEW T) is utilized to separate the filtered signals from the frequency bands, and to feature extraction from the EEG signals. Signal’s characteristics, like logarithmic bandwidth power, standard deviation, variance, kurtosis, mean energy, mean square, norm are combined to ACEWT method to create featur e vectors and enhance diagnostic performance. After that, the extracted features are fed to Dynamically Stabilized Recurrent Neural Network (DSRNN) for task classification. Weight parameter of DSRNN is enhanced using Artificial Gorilla Troops Optimization Algorithm (AGTOA). The ...
Source: Health Information Science and Systems - Category: Information Technology Source Type: research