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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.06378 |
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| _version_ | 1866915331808690176 |
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| author | Tsirmpas, Charalampos Konstantopoulos, Stasinos Andrikopoulos, Dimitris Kyriakouli, Konstantina Fatouros, Panagiotis |
| author_facet | Tsirmpas, Charalampos Konstantopoulos, Stasinos Andrikopoulos, Dimitris Kyriakouli, Konstantina Fatouros, Panagiotis |
| contents | Decomposing Electrodermal Activity (EDA) into phasic (short-term, stimulus-linked responses) and tonic (longer-term baseline) components is essential for extracting meaningful emotional and physiological biomarkers. This study presents a comparative analysis of knowledge-driven, statistical, and deep learning-based methods for EDA signal decomposition, with a focus on in-the-wild data collected from wearable devices. In particular, the authors introduce the Feel Transformer, a novel Transformer-based model adapted from the Autoformer architecture, designed to separate phasic and tonic components without explicit supervision. The model leverages pooling and trend-removal mechanisms to enforce physiologically meaningful decompositions. Comparative experiments against methods such as Ledalab, cvxEDA, and conventional detrending show that the Feel Transformer achieves a balance between feature fidelity (SCR frequency, amplitude, and tonic slope) and robustness to noisy, real-world data. The model demonstrates potential for real-time biosignal analysis and future applications in stress prediction, digital mental health interventions, and physiological forecasting. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_06378 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Transformer-Based Decomposition of Electrodermal Activity for Real-World Mental Health Applications Tsirmpas, Charalampos Konstantopoulos, Stasinos Andrikopoulos, Dimitris Kyriakouli, Konstantina Fatouros, Panagiotis Signal Processing Machine Learning Decomposing Electrodermal Activity (EDA) into phasic (short-term, stimulus-linked responses) and tonic (longer-term baseline) components is essential for extracting meaningful emotional and physiological biomarkers. This study presents a comparative analysis of knowledge-driven, statistical, and deep learning-based methods for EDA signal decomposition, with a focus on in-the-wild data collected from wearable devices. In particular, the authors introduce the Feel Transformer, a novel Transformer-based model adapted from the Autoformer architecture, designed to separate phasic and tonic components without explicit supervision. The model leverages pooling and trend-removal mechanisms to enforce physiologically meaningful decompositions. Comparative experiments against methods such as Ledalab, cvxEDA, and conventional detrending show that the Feel Transformer achieves a balance between feature fidelity (SCR frequency, amplitude, and tonic slope) and robustness to noisy, real-world data. The model demonstrates potential for real-time biosignal analysis and future applications in stress prediction, digital mental health interventions, and physiological forecasting. |
| title | Transformer-Based Decomposition of Electrodermal Activity for Real-World Mental Health Applications |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2506.06378 |