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This arXiv preprint tests which classifiers, windowing choices, and class-balancing methods work best for predicting valence and arousal from heart rate variability, with Logistic Regression performing best. It also shows that federated training on a small Raspberry Pi cluster can approach centralized accuracy without sharing raw physiological data. The evidence is limited: 74 participants across public datasets, a single 75% pilot result on one unseen person, and no clinical validation, so the mental-health claims are aspirational.
Privacy-Preserving ECG-HRV Emotion Classification for Mood Swing Detection using Federated Learning on Constrained Edge Devices
arXiv:2611.01853v1 Announce Type: cross Abstract: This paper presents a privacy-preserving mood monitoring framework that classifies emotional states from ECG-derived heart rate variability (HRV) features. The framework is evaluated on three public datasets comprising 74 participants and further validated using a wearable device in a pilot study. Leave-One-Participant-Out cross-validation (LOPO-CV) is used to assess generalization to unseen individuals. A series of experiments investigates the impact of temporal windowing, HRV feature selection, and class balancing on both four-class valence-arousal and binary valence classification. Results show that class balancing substantially improves minority-class recall, while Logistic Regression consistently outperforms five alternative classifiers and achieves competitive performance against HRV-based baselines. Pilot validation using a Polar H10 wearable achieves 75% binary valence accuracy on an unseen individual without calibration, demonstrating the feasibility of real-world deployment. To preserve privacy, the framework is also implemented using federated learning on a six-node Raspberry Pi cluster. Federated Logistic Regression achieves performance comparable to centralized training without sharing raw physiological data. These results demonstrate the potential of interpretable and privacy-preserving mood monitoring for continuous mental health assessment in clinical and community settings.
Abstract:This paper presents a privacy-preserving mood monitoring framework that classifies emotional states from ECG-derived heart rate variability (HRV) features. The framework is evaluated on three public datasets comprising 74 participants and further validated using a wearable device in a pilot study. Leave-One-Participant-Out cross-validation (LOPO-CV) is used to assess generalization to unseen individuals. A series of experiments investigates the impact of temporal windowing, HRV feature selection, and class balancing on both four-class valence-arousal and binary valence classification. Results show that class balancing substantially improves minority-class recall, while Logistic Regression consistently outperforms five alternative classifiers and achieves competitive performance against HRV-based baselines. Pilot validation using a Polar H10 wearable achieves 75% binary valence accuracy on an unseen individual without calibration, demonstrating the feasibility of real-world deployment. To preserve privacy, the framework is also implemented using federated learning on a six-node Raspberry Pi cluster. Federated Logistic Regression achieves performance comparable to centralized training without sharing raw physiological data. These results demonstrate the potential of interpretable and privacy-preserving mood monitoring for continuous mental health assessment in clinical and community settings.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2611.01853 [cs.LG]
(or arXiv:2611.01853v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2611.01853 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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