Emotion-elicited heart rate variability analysis achieved 76.8% accuracy in distinguishing individuals with depression from healthy controls during sadness elicitation.
Does structured emotion induction combined with HRV feature analysis improve depression screening accuracy in individuals with suspected depression compared to healthy controls?
HRV-based affective computing during sadness induction achieves 76.8% accuracy in screening for depression, empirically validating heightened sadness susceptibility in individuals with depression.
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Abstract Objective Depression manifests significant emotional dysregulation, characterized by heightened sadness susceptibility and attenuated happiness responsiveness in individuals with depression (IWD). This study employs structured emotion induction protocols to analyze physiological response disparities between IWD and healthy controls (HC) across multiple affective states, establishing empirical foundations for optimizing affective computing-based depression screening. Methods Dual-phase statistical identification was conducted using Mann–Whitney U tests: initially verifying emotion elicitation validity by comparing HRV features between emotional states and resting conditions, subsequently detecting IWD/HC response differences within each emotion. Machine learning frameworks were then constructed leveraging HRV features and intergroup differential response patterns. Results Comparative analysis revealed generally consistent directional patterns and response magnitudes across groups for most features, while critical divergences emerged characterized by heightened sadness reactivity in IWD alongside attenuated happiness responsiveness. Implemented models achieved 76.8% accuracy (AUC = 0.772, 95% CI 0.699–0.841) under sadness-specific conditions, outperforming anger/happiness-induced models (≈ 70% accuracy) and substantially surpassing resting-state baselines. Conclusion Systematic investigation of HRV-mediated elicitation patterns through discrete emotion induction confirms clinically significant differential responsiveness between groups, empirically validating heightened sadness susceptibility in IWDs. Significance These findings offer valuable guidance for refining affective computing-based depression screening algorithms, while contributing to the mechanistic understanding of disorder-specific physiological responses to emotional stimuli.
Zhu et al. (Wed,) reported a other. Emotion-elicited heart rate variability analysis achieved 76.8% accuracy in distinguishing individuals with depression from healthy controls during sadness elicitation.
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