## 4 Discussion
However, to date, no studies have assessed whether patients with syncope and sleep apnea exhibiting elevated nocturnal sympathetic activity improve after CPAP treatment, nor whether such improvement correlates with a reduction in syncopal events. To confirm this relationship, studies with 24 h Holter monitoring before and after CPAP therapy are needed. Evaluating the circadian HRV cycle in these patients could help establish a causal link among apnea, syncope, and circadian rhythm disturbances in HRV. From an ethical standpoint, the adoption of AI in medicine requires transparency, explainability, and human oversight to maintain the trust of both patients and healthcare professionals [51; 52; 53; 54]. [PERSON] et al. [51] propose governance frameworks that ensure accountability and fairness, while [PERSON] and [PERSON] [52] highlight the need to assess algorithmic biases and their distributive impact. Studies on public perception show that patients are receptive to the use of AI for supportive tasks but prefer that critical clinical decisions remain under the physician's control [53; 54]. Our clustering model, designed to generate hypotheses and support early stratification, is applied without automating individual decision-making; its future implementation will require ongoing validation, clear communication, and alignment with bioethical principles to ensure its responsible and ethical integration into clinical practice. This study has some limitations. The most significant is the absence of standardized reference values for daytime and nocturnal HRV analysis. Home respiratory polygraphy, used instead of polysomography (the gold standard), may have introduced interpretation errors. External factors such as stress, insomnia, or ambient temperature could also have affected the results. Moreover, a larger sample size might have revealed statistically significant differences in more HRV parameters. In clustering analysis involving mixed clinical data, traditional cohesion metrics such as the within-cluster sum of squares are not applicable when using algorithms like k-prototypes, which rely on hybrid dissimilarity functions combining categorical and numerical variables. For this reason, we used the silhouette index calculated from the Gower distance matrix--a recognized metric for internal validation in mixed-type datasets. The average silhouette score obtained (0.15) may appear modest, but it aligns with expectations for real-world clinical data, where phenotypic boundaries are often diffuse. To better contextualize this metric, a table was added in Appendix A (Table A1) showing silhouette scores by cluster, which indicate greater internal cohesion within more clinically defined subgroups. Additionally, future strategies should include complementary internal validation metrics, such as the Davies-Bouldin or Calinski-Harabasz indices, as well as stability analyses via bootstrapping to evaluate the reproducibility of the clustering results more robustly. Moreover, the clustering structure was supported by a UMAP projection, which showed a coherent distribution of patients in the reduced space. Furthermore, since this is an unsupervised analysis, no reference labels (ground truth) exist for direct external validation of the identified clusters. This is a widely recognized limitation in clustering studies involving clinical data, where well-defined phenotypic classifications are often lacking. Both elements reinforce the interpretation of the results and support their potential clinical relevance. Nonetheless, it is important to acknowledge that potentially confounding factors--such as patients' lifestyle (e.g., physical activity, substance use, dietary habits) and mental health status (e.g., anxiety, depression, stress)--were not assessed in this study. These factors are known to modulate autonomic nervous system function and may therefore influence both the incidence of syncope and HRV parameters, potentially affecting the interpretation of clustering results.