Key result
Support Vector Machine model achieves ~80% accuracy classifying physiological stress from cardiovascular time series.
Why the study?
Explainable artificial intelligence is needed for reliable healthcare decisions, and integrating information-theoretic approaches into feature selection can improve assessment of feature importance by capturing redundant and synergistic contributions.
Does an information-theoretic feature selection framework improve the identification of informative cardiovascular variables and the accuracy of physiological stress classification in healthy individuals?
Does an information-theoretic feature selection framework improve the identification of informative cardiovascular variables and the accuracy of physiological stress classification in healthy individuals?
An information-theoretic feature selection framework effectively identifies dynamic cardiovascular variability metrics, achieving ~80% accuracy in classifying physiological stress states.
May aid research stress classification; leaves open clinical validation and prospective trials.
Explainable artificial intelligence improves the interpretability of machine learning models, which is essential for reliable decision-making in healthcare. Integrating information-theoretic (IT) approaches into feature selection (FS) facilitates a more rational assessment of feature importance (FI) by capturing redundant and synergistic contributions. This study proposes a novel combined FI-FS method that extends the High-order Interactions Feature Importance (Hi-Fi) framework by replacing variance-based with IT metrics for improved quantification of high-order feature interactions. The method adapts the Leave One Covariate Out metric to identify feature subsets that maximise or minimise Conditional Mutual Information (CMI), prioritising features that increase synergy and reduce redundancy. Feature interpretation is further supported by analysing how selected variables interact, revealing both individual and joint predictive relevance. The proposed framework is applied to cardiovascular time series from 127 young, healthy individuals recorded at rest and under stress conditions (postural and mental stress). The analysed features are extracted from beat-to-beat electrocardiographic RR intervals, pulse-pulse intervals (PP), and systolic and diastolic blood pressure (SBP, DBP) time series and are computed in the time, frequency, and information domains. FI results show that features reflecting variability, spectral content, and entropy, especially from RR, DBP, and PP, are more informative than static measures. RR features consistently show the highest unique information, while PP contributes mainly through synergy. The FS process reduces feature count by ∼20%, and a Support Vector Machine trained on the selected features achieves ∼80% accuracy in multi-class stress classification. Overall, this IT-based Hi-Fi framework captures individual informativeness and complex signal interactions, improving dependency detection, interpretability, and physiological insight.
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Iovino et al. (2025) studied Physiological stress (n=127). Information-theoretic feature selection and Support Vector Machine classification was evaluated on Overall multi-class stress classification accuracy. An information-theoretic feature selection framework combined with a Support Vector Machine achieved 80.3% overall accuracy in classifying physiological stress states from cardiovascular time series.
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