Tsirmpas Charalampos, Konstantopoulos Stasinos, Andrikopoulos Dimitris, Kyriakouli Konstantina, Fatouros Panagiotis
Feel Therapeutics Inc., San Francisco, CA 94108, USA.
Siglyx, 151 22 Athens, Greece.
Sensors (Basel). 2025 Jul 15;25(14):4406. doi: 10.3390/s25144406.
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.
将皮肤电活动（EDA）分解为相位成分（短期、与刺激相关的反应）和紧张性成分（长期基线）对于提取有意义的情绪和生理生物标志物至关重要。本研究对基于知识驱动、统计和深度学习的EDA信号分解方法进行了比较分析，重点关注从可穿戴设备收集的实际环境数据。特别是，作者介绍了Feel Transformer，这是一种基于Transformer架构改编的新型模型，旨在在无明确监督的情况下分离相位和紧张性成分。该模型利用池化和趋势消除机制来实现具有生理意义的分解。与Ledalab、cvxEDA等方法以及传统去趋势方法的对比实验表明，Feel Transformer在特征保真度（皮肤电反应频率、幅度和紧张性斜率）与对嘈杂的真实世界数据的鲁棒性之间取得了平衡。该模型展示了实时生物信号分析以及在压力预测、数字心理健康干预和生理预测等未来应用中的潜力。
