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. 2024 Mar 15;12(1):25.
doi: 10.1007/s13755-024-00284-9. eCollection 2024 Dec.

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

G Sudha  1 , N Saravanan  2 , M Muthalakshmi  3 , M Birunda  1
Affiliations

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

G Sudha et al. Health Inf Sci Syst. .

Abstract

Alzheimer'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 work 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 (ACEWT) 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 feature 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 proposed DSRNN-AGTOA-ADD algorithm is activated in MATLAB. The metrics including accuracy, specificity, sensitivity, precision, computation time, ROC are examined for AD diagnosis. The performance of the proposed DSRNN-AGTOA-ADD approach attains 12.98%, 5.98% and 23.45% high specificity; 29.98%, 23.32% and 19.76% lower computation Time and 29.29%, 8.365%, 8.551% and 7.915% higher ROC compared with the existing methods.

Keywords: Adaptive and concise empirical wavelet transform; Artificial Gorilla Troops optimization algorithm; Dynamic context-sensitive filter; Dynamically stabilized recurrent neural network; EEG signal.

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Figures

Fig. 1
Fig. 1
Block diagram of the DSRNN-AGTO-ADD-EEG
Fig. 2
Fig. 2
Sample image of the EEG dataset
Fig. 3
Fig. 3
Flowchart of AGTO to optimize DSRNN parameter
Fig. 4
Fig. 4
Computation time
Fig. 5
Fig. 5
ROC analysis
Fig. 6
Fig. 6
Execution time analysis
Fig. 7
Fig. 7
Output signal images of proposed DSRNN-AGTO-ADD
See this image and copyright information in PMC

References

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