AIMC Topic: Sleep
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Tackling inter-subject variability in smartwatch data using factorization models.
Scientific reports
Jul 23, 2025
Smartwatches enable longitudinal and continuous data acquisition. This has the potential to remotely monitor (changes) of the health of users. However, differences among subjects (inter-subject variability) limit a model to generalize to unseen subje...
Detection and Analysis of Circadian Biomarkers for Metabolic Syndrome Using Wearable Data: Cross-Sectional Study.
JMIR medical informatics
Jul 16, 2025
BACKGROUND: Wearable devices are increasingly used for monitoring health and detecting digital biomarkers related to chronic diseases such as metabolic syndrome (MetS). Although circadian rhythm disturbances are known to contribute to MetS, few studi...
Personalizing brain stimulation: continual learning for sleep spindle detection.
Journal of neural engineering
Jul 15, 2025
Personalized stimulation, in which algorithms used to detect neural events adapt to a user's unique neural characteristics, may be crucial to enable optimized and consistent stimulation quality for both fundamental research and clinical applications....
Assessment of Recommendations Provided to Athletes Regarding Sleep Education by GPT-4o and Google Gemini: Comparative Evaluation Study.
JMIR formative research
Jul 8, 2025
BACKGROUND: Inadequate sleep is prevalent among athletes, affecting adaptation to training and performance. While education on factors influencing sleep can improve sleep behaviors, large language models (LLMs) may offer a scalable approach to provid...
Beyond accuracy: a framework for evaluating algorithmic bias and performance, applied to automated sleep scoring.
Scientific reports
Jul 1, 2025
Recent advancements in artificial intelligence (AI) have significantly improved sleep-scoring algorithms, bringing their performance close to the theoretical limit of approximately 80%, which aligns with inter-scorer agreement levels. While this sugg...
A novel feature extractor based on constrained cross network for detecting sleep state.
Scientific reports
Jul 1, 2025
With increasing awareness of healthy living and social pressure, more and more people have begun to pay attention to their sleep state. Most existing methods that utilize wrist-worn devices data for detection rely on heuristic algorithms or tradition...
Personalized machine learning models for noninvasive hypoglycemia detection in people with type 1 diabetes using a smartwatch: Insights into feature importance during waking and sleeping times.
PloS one
Jun 25, 2025
Hypoglycemia is a major challenge for people with diabetes. Therefore, glycemic monitoring is an important aspect of diabetes management. However, current methods such as finger pricking and continuous glucose monitoring systems (CGMS) are invasive, ...
Sleep Identification Enabled by Supervised Training Algorithms (SIESTA): An Open-Source Platform for Automatic Sleep Staging of Rodent Electrocorticographic and Electromyographic Data.
Journal of biological rhythms
Jun 6, 2025
Accurately capturing the temporal distribution of polysomnographic sleep stages is critical for the study of sleep function, regulation, and disorders in higher vertebrates. In laboratory rodents, scoring of electrocorticography (ECoG) and electromyo...
A skin-interfaced wireless wearable device and data analytics approach for sleep-stage and disorder detection.
Proceedings of the National Academy of Sciences of the United States of America
Jun 6, 2025
Accurate identification of sleep stages and disorders is crucial for maintaining health, preventing chronic conditions, and improving diagnosis and treatment. Direct respiratory measurements, as key biomarkers, are missing in traditional wrist- or fi...
The two ends of the spectrum: comparing chronic schizophrenia and premorbid latent schizotypy by actigraphy.
BMC psychiatry
May 24, 2025
Motor activity alterations are key symptoms of psychiatric disorders like schizophrenia. Actigraphy, a non-invasive monitoring method, shows promise in early identification. This study characterizes Positive Schizotypy Factor (PSF) and Chronic Schizo...
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