Continuous Wavelet Transformation and VGG16 Deep Neural Network for Stress Classification in PPG Signals

TL;DR

This paper presents a novel stress classification method using PPG signals by combining Continuous Wavelet Transformation with VGG16, achieving high accuracy and robustness for stress monitoring applications.

Contribution

It introduces a new approach integrating CWT and VGG16 neural network for improved stress classification from PPG signals, with robust preprocessing.

Findings

Maximum training accuracy of 98%

Average training accuracy of 96% across scenarios

Effective stress monitoring performance

Abstract

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.