## 4 Discussion  
Although wearable devices provide valuable information on sleep stages, they typically do not provide the underlying physiological signal data, which are essential for a more detailed analysis. Our research, therefore, explored graphical representations that serve as a proxy for these underlying signals. Combining visual sleep stage patterns with medical histories and past health records, the models achieved a substantial improvement in the classification accuracy of OSA. This result indicates that data from wearables, while limited to sleep stages, gain significant value when combined with additional patient-centric information. The improved performance of the model when using this set of enriched features underscores the merit of this method, suggesting that it is a promising approach to the classification of sleep disorders. Due to the multifaceted nature of OSA and its comorbidities, we also sought to examine its association with insomnia and depression (marginally better than random chance). For insomnia, models struggled to delineate insomniacs effectively, even among patients with higher-severity OSA. This ambiguity reflects the mixed findings within the literature. Some studies [11, 24] report that nocturnal awakenings can be pertinent to chronic insomnia only and not related to OSA, some [7, 25] suggest that they are not necessarily precursors to each other, and yet more [26, 27] view insomnia as a comorbidity arising from OSA. In examining depression, as measured by the Zung questionnaire, our models similarly underperformed, mirroring the results of studies [28, 29, 30] that employ wearable technology. Although the initial data appeared promising, the link between depression and sleep disorders proved to be too weak to support the development of reliable machine learning models. Comparable machine learning efforts [31, 32] have mirrored our results, which can be attributed to the significant variation in the severity of depression between individuals. In particular, the variance in the severity of depression symptoms and associated behaviors between subjects with mild to moderate depression presented a challenge. This variability led to difficulty in identifying clear patterns, raising concerns that the models may have learned spurious correlations rather than meaningful relationships. Such findings imply that the influence of depression on sleep architecture extends beyond the scope of data captured in a single night. The limitations in distinguishing insomnia and depression highlight the inherent complexities of these conditions, as well as the subtlety and heterogeneity of the associated physiological and behavioral markers. Insomnia often reflects subjective complaints, such as difficulty in initiating or maintaining sleep, which do not always translate into measurable deviations in the sleep architecture [33]. Similarly, depression is characterized by various symptoms, such as mood disturbances and cognitive changes that may influence sleep, but are not directly detectable through wearable devices or PSG. This lack of specificity in the available data reduces the model's ability to generalize effectively across different patient profiles. Recent work also observes this phenomenon, and our work is in agreement with respect to the inconclusiveness of the interplay between sleep and these conditions [34, 35]. To authentically capture their complex interactions, a longitudinal study design is necessary to observe long-term trends and patterns. To improve diagnostic precision, future studies can integrate additional features such as heart rate variability, respiratory rates, and accelerometer data, which can complement hypnodensity and hypnogram data by providing information on physiological states related to sleep quality. Leveraging transfer learning with large models pre-trained on similar biomedical or physiological datasets can provide a strong foundation and enable better generalization when applied to task-specific datasets.