Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Ann, Amala K.A.; Vaidehi, V. Title A name given to the resource PERSONALIZED SLEEP SIGNATURE: A NOVEL APPROACH TO UNVEIL PAEDIATRIC SLEEP BEHAVIOUR WITH TRANSFORMER ATTENTION MECHANISM AND GRAPH ATTENTION NETWORKS Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Journal of Theoretical and Applied Information Technology;Volume;103;Issue;7;pp.3053-3069 Identifier An unambiguous reference to the resource within a given context <a href="https://www.scopus.com/pages/publications/105002774265?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105002774265?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Ann A.K.A., CHRIST (Deemed to be University), Bengaluru, India; Vaidehi V., CHRIST (Deemed to be University), Bengaluru, India Description An account of the resource Sleep disorders manifest differently across individuals, making accurate diagnosis and treatment highly complex. Even within the same diagnosis, there can be variation in sleep architecture among patients, which makes generalization across people difficult. Traditional sleep analysis methods rely on manual scoring and fixed diagnostic criteria, which fail to capture subject-specific variability in sleep patterns. To address this, we propose a data-driven Personalized Sleep Signature (PSS) approach that learns individualized sleep behaviour using AI models. This study introduces the PSS framework, combining Transformer-based Attention and Graph Attention Networks (GATs) to model nuanced sleep characteristics. We utilize the Nationwide Childrens Hospital Sleep Data, a paediatric Polysomnography (PSG) dataset containing EEG and physiological parameters such as ocular movements, EMG activity, blood pressure, and respiratory rate. From this, we extract sleep epoch features and demographics to form Sleep Signature Groups that reflect common behavioural patterns. Unlike conventional classification, our method captures personal variability and delivers individualized sleep hygiene guidance. The model achieved 94% accuracy in detecting sleep patterns, outperforming traditional methods. Beyond clinical applications, it can be integrated with wearable sensors (e.g., Fitbit, Oura, Apple Watch) to personalize wake/sleep routines and environments. It also enables early detection of sleep disorders and aligns daily schedules with individual chronotypes to enhance well-being. By focusing on sleep behaviour rather than rigid diagnostic categories, this approach supports non-pharmacological, personalized interventions backed by scientific evidence. Our work opens the door to precision sleep medicine, offering actionable insights for clinicians, researchers, and technology innovators. Little Lion Scientific. Subject The topic of the resource GAT; NCH Sleep Data; Personalized Sleep Signature; Polysomnography; Transformers Publisher An entity responsible for making the resource available Little Lion Scientific Relation A related resource ISSN: 19928645; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online