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  Published Paper Details:

  Paper Title

Enhancing Depression Diagnosis with Wearable EEG and Ant Lion Optimization Algorithm

  Authors

  Yasmin S. Khan,  Monika Thube

  Keywords

EEG technology Wearable EEG Sensor ALO Algorithm

  Abstract


Depression is a common mental illness affecting many people worldwide. The number of diagnosed cases has been increasing recently. Current methods for diagnosing depression are often slow and not very accurate. We need better tools to find depression. Scientists are using brain wave signals called EEG to help diagnose depression. But in the past, they mostly looked at the whole brain, which was complicated and took a long time. They realized that looking at a specific part of the brain, the prefrontal lobe, is better for finding depression. To use this prefrontal brain data well, we need lots of good quality data. The regular machines used for EEG are big and hard to use. Some smaller ones can't capture the right data. So, we made a wearable device to get good brain data. In this study, we made a system to help diagnose depression using this wearable device. It also has a smart way to figure out if someone has depression. Our work shows promise in helping diagnose depression quickly. We explain how we made the device and the smart way to find depression.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2312657

  Paper ID - 248274

  Page Number(s) - f861-f865

  Pubished in - Volume 11 | Issue 12 | December 2023

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Yasmin S. Khan,  Monika Thube,   "Enhancing Depression Diagnosis with Wearable EEG and Ant Lion Optimization Algorithm", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.11, Issue 12, pp.f861-f865, December 2023, Available at :http://www.ijcrt.org/papers/IJCRT2312657.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


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ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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