Hyatt Moore
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View article: 0900 The Clinical Impact of Periodic Limb Movements: Under-Recognition in Sleep Disordered Breathing and Associations with Key Outcomes in 493,085 Patients
0900 The Clinical Impact of Periodic Limb Movements: Under-Recognition in Sleep Disordered Breathing and Associations with Key Outcomes in 493,085 Patients Open
Introduction We evaluated PLMS in a large cohort, exploring their association with sleep disordered breathing (SDB) and apnea scoring, and associations with key clinical outcomes. Methods This retrospective analysis included 493,085 adults…
View article: A Multimodal Sleep Foundation Model Developed with 500K Hours of Sleep Recordings for Disease Predictions
A Multimodal Sleep Foundation Model Developed with 500K Hours of Sleep Recordings for Disease Predictions Open
Sleep is a fundamental biological process with profound implications for physical and mental health, yet our understanding of its complex patterns and their relationships to a broad spectrum of diseases remains limited. While polysomnograp…
View article: SleepFM: Multi-modal Representation Learning for Sleep Across Brain Activity, ECG and Respiratory Signals
SleepFM: Multi-modal Representation Learning for Sleep Across Brain Activity, ECG and Respiratory Signals Open
Sleep is a complex physiological process evaluated through various modalities recording electrical brain, cardiac, and respiratory activities. We curate a large polysomnography dataset from over 14,000 participants comprising over 100,000 …
View article: Prediction Strength for Clustering Activity Patterns Using Accelerometer Data
Prediction Strength for Clustering Activity Patterns Using Accelerometer Data Open
Background : Clustering, a class of unsupervised machine learning methods, has been applied to physical activity data recorded by accelerometers to discover unique patterns of physical activity and health outcomes. The prediction strength …
View article: Statistical Learning Methods to Identify Nonwear Periods From Accelerometer Data
Statistical Learning Methods to Identify Nonwear Periods From Accelerometer Data Open
Background : Accelerometers are used to objectively measure movement in free-living individuals. Distinguishing nonwear from sleep and sedentary behavior is important to derive accurate measures of physical activity, sedentary behavior, an…
View article: 0082 External Validation of an Enhanced Machine Learning Algorithm: Polysomnography-Based Narcolepsy-Like Feature Assessment and Clinician Notification in Routine Sleep Medicine Clinics
0082 External Validation of an Enhanced Machine Learning Algorithm: Polysomnography-Based Narcolepsy-Like Feature Assessment and Clinician Notification in Routine Sleep Medicine Clinics Open
Introduction Polysomnography (PSG) contains quantitative information that, using machine learning (ML) algorithms, may aid the identification of type 1 narcolepsy. This study aimed to evaluate the utility of a previously developed quantita…
View article: Potential corner case cautions regarding publicly available implementations of the National Cancer Institute’s nonwear/wear classification algorithm for accelerometer data
Potential corner case cautions regarding publicly available implementations of the National Cancer Institute’s nonwear/wear classification algorithm for accelerometer data Open
The National Cancer Institute's (NCI) wear time classification algorithm uses a rule based on the occurrence of physical activity data counts-a cumulative measure of movement, influenced by both magnitude and duration of acceleration-to di…
View article: 0323 Design of a Deep Learning Based Algorithm forAutomatic Detection of Leg Movements During Sleep
0323 Design of a Deep Learning Based Algorithm forAutomatic Detection of Leg Movements During Sleep Open
Leg Movements (LM) and Periodic Leg Movements (PLM) during sleep are a key feature of nocturnal polysomnographic (PSGs) sleep studies. The current practice is manual annotation by technicians, which is time-consuming and prone to human err…
View article: The use of neural networks in the analysis of sleep stages and the diagnosis of narcolepsy.
The use of neural networks in the analysis of sleep stages and the diagnosis of narcolepsy. Open
Analysis of sleep for the diagnosis of sleep disorders such as Type-1 Narcolepsy (T1N) currently requires visual inspection of polysomnography records by trained scoring technicians. Here, we used neural networks in approximately 3,000 nor…