Deep Learning Based Depression Analysis using EEG and ECG Signals
| International Journal of Electrical and Electronics Engineering |
| © 2023 by SSRG - IJEEE Journal |
| Volume 10 Issue 7 |
| Year of Publication : 2023 |
| Authors : Sanchita M. Pange, Vijaya R. Pawar |
10.14445/23488379/IJEEE-V10I7P105
How to Cite?
Sanchita M. Pange, Vijaya R. Pawar, "Deep Learning Based Depression Analysis using EEG and ECG Signals," SSRG International Journal of Electrical and Electronics Engineering, vol. 10, no. 7, pp. 53-62, 2023. Crossref, https://doi.org/10.14445/23488379/IJEEE-V10I7P105
Abstract:
In covid -19 situation, most people suffer from stress. Continuous stress can lead to severe psychological and even physical disorders. To detect depression manually is time-consuming, tedious, and requires expertise. The present system detects and analyses depression based on EEG and ECG signals. The system layout strategies and calculations include extraction and choice strategies for classification, deteriorating techniques, and combination methodologies. The EEG and ECG features are extracted and sent for classification. The ST segment, P wave, and QRS wave are extracted from ECG signals as features. The most prominent features analyzed from EEG signals are Hjorth activity (HA), standard deviation, entropy, and band power alpha. The Long Short-Term Memory (LSTM) autoencoder and RNN deep learning model approach were used for depression analysis.
Keywords:
Deep learning, Depression analysis, Feature extraction, LSTM autoencoder, Recurrent Neural Networks.
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