Continuous Wavelet Transformation and VGG16 Deep Neural Network for Stress Classification in PPG Signals
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929550398586880 |
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| author | Hasanpoor, Yasin Tarvirdizadeh, Bahram Alipour, Khalil Ghamari, Mohammad |
| author_facet | Hasanpoor, Yasin Tarvirdizadeh, Bahram Alipour, Khalil Ghamari, Mohammad |
| contents | Our research introduces a groundbreaking approach to stress classification through Photoplethysmogram (PPG) signals. By combining Continuous Wavelet Transformation (CWT) with the proven VGG16 classifier, our method enhances stress assessment accuracy and reliability. Previous studies highlighted the importance of physiological signal analysis, yet precise stress classification remains a challenge. Our approach addresses this by incorporating robust data preprocessing with a Kalman filter and a sophisticated neural network architecture. Experimental results showcase exceptional performance, achieving a maximum training accuracy of 98% and maintaining an impressive average training accuracy of 96% across diverse stress scenarios. These results demonstrate the practicality and promise of our method in advancing stress monitoring systems and stress alarm sensors, contributing significantly to stress classification. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_14747 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Continuous Wavelet Transformation and VGG16 Deep Neural Network for Stress Classification in PPG Signals Hasanpoor, Yasin Tarvirdizadeh, Bahram Alipour, Khalil Ghamari, Mohammad Image and Video Processing Machine Learning ut.ac Our research introduces a groundbreaking approach to stress classification through Photoplethysmogram (PPG) signals. By combining Continuous Wavelet Transformation (CWT) with the proven VGG16 classifier, our method enhances stress assessment accuracy and reliability. Previous studies highlighted the importance of physiological signal analysis, yet precise stress classification remains a challenge. Our approach addresses this by incorporating robust data preprocessing with a Kalman filter and a sophisticated neural network architecture. Experimental results showcase exceptional performance, achieving a maximum training accuracy of 98% and maintaining an impressive average training accuracy of 96% across diverse stress scenarios. These results demonstrate the practicality and promise of our method in advancing stress monitoring systems and stress alarm sensors, contributing significantly to stress classification. |
| title | Continuous Wavelet Transformation and VGG16 Deep Neural Network for Stress Classification in PPG Signals |
| topic | Image and Video Processing Machine Learning ut.ac |
| url | https://arxiv.org/abs/2410.14747 |