Stress Net: Multimodal Stress Detection using ECG and EEG Signals
This research work introduces Integrity of Time Domain Features & Machine Learning for Stress Classification using ECG & EEG Signals. Stress is a prevalent mental health issue in our daily lives, affecting many individuals. The impact of stress can lead to various problems, including hear...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English English |
| Published: |
INTI International University
2024
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| Subjects: | |
| Online Access: | http://eprints.intimal.edu.my/2066/1/jods2024_59.pdf http://eprints.intimal.edu.my/2066/2/607 http://eprints.intimal.edu.my/2066/ http://ipublishing.intimal.edu.my/jods.html |
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| Summary: | This research work introduces Integrity of Time Domain Features & Machine Learning for Stress
Classification using ECG & EEG Signals. Stress is a prevalent mental health issue in our daily
lives, affecting many individuals. The impact of stress can lead to various problems, including
heart attacks and depression. This research work aims to identify anxiety through a physical
examination using both EEG and ECG signals. By analyzing and monitoring these signals, we can
improve stress detection exactness, ultimately identifying and addressing mental health problems.
This research work is used to prevent early detection of diseases such as depression and suicidal
attempts. This task can benefit society as a whole. Moreover, using ECG signals to assess
cardiovascular and related risk factors in the early stages has been explored through machine
learning techniques. |
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