Objective. This study evaluates the effectiveness of four machine learning algorithms in classifying physiological stress using heart rate variability (HRV) and pulse rate variability (PRV) time series, comparing an automatic feature selection based on Akaike’s criterion to a physiologically-based feature selection approach. Approach. Linear discriminant analysis, support vector machines, K-nearest neighbors and random forest were applied on ten HRV and PRV indices from time, frequency and information domains, selected with the two feature selection approaches. Data were collected from 127 healthy individuals during different stress conditions (rest, postural and mental stress). Main results. Our results highlight that, while specific stress classification is feasible, distinguishing between postural and mental stress remains challenging. The used classifiers exhibited similar performance, with automatic Akaike Information Criterion-based feature selection proving overall better than the physiology-driven approach. Additionally, PRV-based features performed comparably to HRV-based ones, indicating their potential in outpatient monitoring using wearable devices. Significance. The obtained findings help to determine the most relevant HRV/PRV features for stress classification, potentially useful to highlight different physiological mechanisms involved during both challenges accompanied by a shift in the sympathovagal balance. The proposed approach may have implications for advancing stress assessment methodologies in clinical settings and real-world contexts for well-being evaluation.

The framework of partial information decomposition (PID) unveils complex nonlinear interactions in network systems by dissecting the mutual information (MI) between a target variable and several source variables. PID measures have been formulated mostly for discrete variables, with only recent extensions to continuous systems. In the case of mixed variables where the target is discrete and the sources are continuous, the application of existing PID schemes requires the manipulation of the data generated by the analyzed system, thus altering their information content. To overcome this issue, we introduce a PID scheme whereby the MI between a specific state of the discrete target and (subsets of) the continuous sources is expressed as a Kullback-Leibler divergence and is estimated through a nearest-neighbor strategy. The effectiveness of this PID is demonstrated in simulated systems of mixed variables and on benchmark data. Our approach is relevant to many scientific problems, including sensory coding in neuroscience and feature selection in machine learning.

Objective: Stress recognition is a widely investigated and debated area in biomedical research. Physiological monitoring has gained increasing attention as one of the methodologies used to assess an individual’s stress level. In this study, we investigated the effectiveness of a novel normalization technique applied to multi-domain physiological data for the objective classification of stress levels using a feature extraction approach. Methods: Electrocardiographic (ECG) and respiratory data from a publicly available database, collected from drivers experiencing various stress levels, underwent a novel inter-subject normalization procedure. This method involved adjusting the time scale of the original data to a common scale across subjects according to fixed resting heart and respiratory rates. Subsequently, a feature-based stress state classification procedure was conducted using the Support Vector Machine (SVM) algorithm. The efficacy of this inter-subject normalization procedure was assessed by comparing the classification results obtained using features from the original signals with those obtained from the inter-subject-normalized signals. Additionally, the inter-subject normalization procedure was compared with two common feature normalization approaches: standardization and scaling. Results: Features derived from the subject-normalized signals yielded improved performance, significantly enhancing accuracy from 68% to 73%, as well as precision and sensitivity. Conclusions: The novel inter-subject normalization procedure proves to be an effective technique for highlighting differences in features among various stress states and for mitigating basal physiological variability across subjects. Significance: Using inter-subject normalization on multi-domain physiological signals holds promise as a method to improve multilevel stress classification through feature extraction, ensuring that the features maintain their correspondence even after the normalization process.

Objective: Stress recognition is a widely investigated and debated area in biomedical research. Physiological monitoring has gained increasing attention as one of the methodologies used to assess an individual's stress level. In this study, we investigated the effectiveness of a novel normalization technique applied to multi-domain physiological data for the objective classification of stress levels using a feature extraction approach. Methods: Electrocardiographic (ECG) and respiratory data from a publicly available database, collected from drivers experiencing various stress levels, underwent a novel inter-subject normalization procedure. This method involved adjusting the time scale of the original data to a common scale across subjects according to fixed resting heart and respiratory rates. Subsequently, a feature-based stress state classification procedure was conducted using the Support Vector Machine (SVM) algorithm. The efficacy of this inter-subject normalization procedure was assessed by comparing the classification results obtained using features from the original signals with those obtained from the inter-subject normalized signals. Additionally, the inter-subject normalization procedure was compared with two common feature normalization approaches: standardization and scaling. Results: Features derived from the subject-normalized signals yielded improved performance, significantly enhancing accuracy from 68% to 73% as well as precision and sensitivity. Conclusion: The novel inter-subject normalization procedure proves to be an effective technique for highlighting differences in features among various stress states and for mitigating basal physiological variability across subjects. Significance: Using inter-subject normalization on multi-domain physiological signals holds promise as a method to improve multilevel stress classification through feature extraction, ensuring that the features maintain their correspondence even after the normalization process.