Dose-dependent cardiac autonomic dysregulation under classroom crowding: heart rate variability analysis and machine learning classification.

Abstract

Classroom overcrowding affects hundreds of millions of students globally, yet its physiological impact on cardiac autonomic function remains uncharacterized. This controlled within-subjects experiment enrolled 30 healthy university students who attended standardized 30-min lectures under three calibrated density conditions: low (30 students/150 m²), medium (60/150 m²), and high (100/150 m²), with all environmental parameters held constant. Twenty-five heart rate variability (HRV) parameters spanning time-domain, frequency-domain, and nonlinear domains were extracted from continuous electrocardiogram (ECG) recordings. Subjective workload was assessed via the NASA Task Load Index (NASA-TLX), and RoBoSS-SVM (a Support Vector Machine using the Robust, Bounded, Sparse, and Smooth loss function) with Recursive Feature Elimination (RFE) classified density conditions from HRV features. Repeated-measures ANOVA revealed significant density effects on all 25 HRV parameters (all p < 0.001), with 64% demonstrating exceptionally large effect sizes (η²p ≥ 0.50). Parasympathetic markers showed the largest responses: pNN50 declined 52% (η²p = 0.702), HF power declined 51% (η²p = 0.654), and RMSSD declined 32% (η²p = 0.420) from low to high density-while sympathetically influenced indices showed substantially smaller effects, confirming parasympathetic withdrawal as the predominant autonomic mechanism. These suppression magnitudes approach levels associated with adverse cardiovascular outcomes. NASA-TLX scores increased 74% across conditions (p < 0.001) and correlated with HRV vagal indices (r = -0.69 to -0.76), providing corroborating, though not definitive, evidence that crowding engages both subjective and autonomic stress pathways (see Discussion for caveats regarding this association). Temporal analysis confirmed progressive autonomic suppression without habituation across 10-min windows. RoBoSS-SVM achieved 92.2% accuracy for three-class discrimination and 97.2% accuracy (AUC = 0.993) for binary classification, with parasympathetic time-domain features comprising 64% of the optimal feature set. Classroom spatial density produces dose-dependent, mechanism-specific cardiac autonomic dysregulation dominated by vagal withdrawal-consistent with polyvagal theory's prediction for social-environmental stressors. The excellent classification performance supports feasibility of real-time wearable monitoring for evidence-based classroom capacity management.