Adaptive Normalization and Feature Extraction for Electrodermal Activity Analysis

Miguel Viana-Matesanz and Carmen Sánchez-Ávila ()
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Miguel Viana-Matesanz: PhD Programme in Biomedical Engineering, ETSI Telecomunicación, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Carmen Sánchez-Ávila: Research Group on Biometrics, Biosignals, Security and Smart Mobility, UPM’s R&D+i Center for Energy Efficiency, Virtual Reality, Optical Engineering and Biometrics (CeDInt-UPM), Campus Montegancedo of International Excelence, Universidad Politécnica de Madrid, 28223 Pozuelo de Alarcón, Spain

Mathematics, 2024, vol. 12, issue 2, 1-19

Abstract: Electrodermal Activity (EDA) has shown great potential for emotion recognition and the early detection of physiological anomalies associated with stress. However, its non-stationary nature limits the capability of current analytical and detection techniques, which are highly dependent on signal stability and controlled environmental conditions. This paper proposes a framework for EDA normalization based on the exponential moving average (EMA) with outlier removal applicable to non-stationary heteroscedastic signals and a novel set of features for analysis. The normalized time series preserves the morphological and statistical properties after transformation. Meanwhile, the proposed features expand on typical time-domain EDA features and profit from the resulting normalized signal properties. Parameter selection and validation were performed using two different EDA databases on stress assessment, accomplishing trend preservation using windows between 5 and 20 s. The proposed normalization and feature extraction framework for EDA analysis showed promising results for the identification of noisy, relaxed and arousal-like patterns in data with conventional clustering approaches like K-means over the aforementioned normalized features.

Keywords: electrodermal activity; normalization; stochastic; biosignals; stress; feature extraction; rolling window; unsupervised classification (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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