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About Automated Depression Detection Using Deep Representation and Sequence Learning With EEG Signals
This study proposes a deep learning model using convolutional neural networks (CNN) and long short-term memory (LSTM) to detect depression from electroencephalography (EEG) signals. The CNN-LSTM model learns temporal patterns from EEG signals through the LSTM layers and represents the signals with the CNN layers. When tested on EEG data from the left and right hemispheres, the model achieved classification accuracies of 99.12% for the right hemisphere and 97.66% for the left hemisphere. This automated approach could help clinicians accurately detect depression using EEG signals.
Detailed Information
| Author: | ['Muhammad Nafizur Rahman'] |
|---|---|
| Publication Year: | 2023 |
| Pages: | 12 |
| Language: | English |
| Format: | |
| Price: | FREE |
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