Computational analysis of heart rate variability in ASD and ADHD: a systematic review

Heart rate variability (HRV) represents a rich physiological signal that captures the computational dynamics of autonomic regulation. Emerging evidence suggests that atypical autonomic control contributes to the neurocognitive phenotype of neurodevelopmental disorders (NDD), including autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD). However, the characterization of HRV alterations in these populations remains incomplete. This review was motivated by the broader research context of the EMPOWER project, funded through the Horizon Europe program, which investigates technology-supported approaches for children with NDD, including smartwatch-based physiological sensing and machine-learning analysis of heart rate, HRV, skin temperature, and photoplethysmography signals during cognitive and relaxation tasks. The present manuscript does not report EMPOWER empirical data. Instead, it systematically synthesizes previously published studies on HRV in pediatric ASD and ADHD. The review examines how HRV has been acquired, processed, modeled, and interpreted in pediatric NDD research, with particular attention to nonlinear signal properties, computational approaches, and interactions between autonomic regulation and cognitive processes. Following PRISMA guidelines, the review surveyed publications over the past decade and included 24 empirical studies from five major databases. ASD studies most often indicated altered vagally mediated HRV and autonomic reactivity, whereas ADHD studies more often suggested task-dependent changes linked to attentional control. Cross-study comparability was constrained by substantial heterogeneity in preprocessing pipelines, feature extraction procedures, and analytical frameworks. Despite these methodological inconsistencies, current evidence supports HRV as a promising physiological signal marker of autonomic dysregulation in pediatric NDD. Nevertheless, the field requires standardized signal processing protocols and reproducible modeling approaches to elucidate the mechanistic and predictive relevance of HRV in ASD and ADHD.