Early Depression Prediction and Estimation with EEG Signals using Machine Learning Algorithm | IEEE Conference Publication | IEEE Xplore

Early Depression Prediction and Estimation with EEG Signals using Machine Learning Algorithm


Abstract:

Depression is a mental disorder that causes feelings of unhappiness and loss of interest. Depression is afflicting a significant portion of the world's kids and adults at...Show More

Abstract:

Depression is a mental disorder that causes feelings of unhappiness and loss of interest. Depression is afflicting a significant portion of the world's kids and adults at the present time. Falls in the early detection of depression or falls in the timely analysis of a depressed individual might create significant complications. Depression can cause serious issues. It is one of the most common causes of suicidal behaviour. But unfortunately, our culture still refuses to recognise depression as a physical condition, resulting in a significant percentage of sad people being unidentified and untreated. In this research, we examined machine learning classifiers that use sociological and psychological data to determine whether a person is sad or not.
Date of Conference: 10-11 March 2022
Date Added to IEEE Xplore: 12 May 2022
ISBN Information:
Conference Location: Chennai, India
References is not available for this document.

I. Introduction

Depression is a very frequent mental illness marked Depression is a very frequent mental illness. sadness and a loss of interest in activities that people generally like, as well as a difficulty to carry out daily tasks, for 14 days or longer. The obstructions exist between the patients and the mentioned effective remedies those are include a deficit of reserves and erroneous estimation Doctors use a psychiatric test to assess a patient by asking questions about symptoms, thoughts, feelings, and behavior patterns.

Select All
1.
P. Samal and R. Singla, "EEG Based Stress Level Detection During Gameplay", GCAT 2021, pp. 1-4.
2.
E. T. Attar, V. Balasubramanian, E. Subasi and M. Kaya, "Stress Analysis Based on Simultaneous HRV and EEG Monitoring", IEEE Journal of Translational Engineering in Health and Medicine, vol. 9, pp. 1-7, 2021.
3.
M. Shim, S.-H. Lee and H.-J. Hwang, "Functional connectivity-based EEG features to aid the diagnosis of post-traumatic stress disorder patients", 2021 9th International Winter Conference on Brain-Computer Interface (BCI), pp. 1-4.
4.
T H.-Y. Zhang, C. E. Stevenson, T.-P. Jung and L.-W. Ko, "Stress-Induced Effects in Resting EEG Spectra Predict the Performance of SSVEP-Based BCI", IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 8, pp. 1771-1780, August 2020.
5.
O. AlShorman, M. Masadeh, A. Alzyoud, M. B. Bin Hayat, F. Akhtar and Rishipal, "The Effects of Emotional Stress on Learning and Memory Cognitive Functions: An EEG Review Study in Education", 2020 Sixth International Conference on e-Learning, pp. 177-182.
6.
S. Kamthekar and B. Iyer, "Tratak Meditation As a CAM for Stress Management: An EEG Based Analysis", 2021 International Conference on Intelligent Technologies (CONIT), pp. 1-6.
7.
A. Islam, A. K. Sarkar and T. Ghosh, "EEG Signal Classification for Mental Stress During Arithmetic Task Using Wavelet Transformation and Statistical Features", ACMI 2021, pp. 1-6.
8.
P. K. Upadhyay and C. Nagpal, "SCG Backpropagation Based Prediction of Stressed EEG Spectrum", Advances in Science and Engineering Technology International Conferences (ASET), pp. 1-5, 2020.
9.
A. Akella et al., "Classifying Multi-Level Stress Responses From Brain Cortical EEG in Nurses and Non-Health Professionals Using Machine Learning Auto Encoder", IEEE Journal of Translational Engineering in Health and Medicine, vol. 9, pp. 1-9, 2021.
10.
H. Al-Kaf, A. Khandoker, K. Khalaf and H. F. Jelinek, "NeuroSky Mindwave Mobile Headset 2 as an Intervention for Stress and Anxiety Measured With Pulse Rate Variability", 2020 Computing in Cardiology, pp. 1-4.
11.
S. Gedam and S. Paul, "A Review on Mental Stress Detection Using Wearable Sensors and Machine Learning Techniques", IEEE Access, vol. 9, no. 9, pp. 84045-84066, 2021.

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References

References is not available for this document.