Investigation on the Morphological Variations of Wearable ECG Signal During Cognitive Stress and Physical Activity Using Local Fluctuation-Based Fuzzy Entropy

Stress is a major contributor to mental health disorders due to the constant activation of the sympathetic nervous system, which alters the fluctuation dynamics of the ECG signal. Analyzing these fluctuations enables the capture of short-term variations in ECG amplitude, which reflect the underlying cardiac variations during stress. This study analyses the local fluctuations of ECG signals under stress and compares them with those observed during physical activity, thereby identifying differences in the complexity and adaptability of cardiac regulation. ECG signals were collected using a wireless wearable system from 24 participants and preprocessed to remove noise and powerline interference. The preprocessed signals were normalized and segmented across multiple scales to extract local fluctuations using linear, quadratic and cubic polynomial detrending methods. The results showed that cubic polynomial detrending most effectively conformed to the signal and removed slow-varying trends. The extracted local fluctuations exhibited rapid variability at fine-grained scales, reflecting ECG beat-to-beat variability, whereas at coarse-grained scales, the baseline fluctuations dominated the ECG signal. Therefore, fluctuations at finer scales were further analyzed using fuzzy entropy to quantify the signal complexity. Stress exhibited reduced fuzzy entropy values compared to physical activity, indicating constrained short-term fluctuations and reduced complexity. Additionally, statistically significant differences ([Formula: see text]) were observed between stress and physical activity. These findings demonstrate that local fluctuation-based fuzzy entropy effectively captures the subtle cardiac dynamics associated with stress and can be used as a potential biomarker in wearable healthcare monitoring systems.