What is the problem being addressed? Wearable devices are promising tools for simplifying complex process of sleep stage classification. However, the data collected from wearable devices are highly noisy, multidimensional, and nonlinear, resulting in limited accuracy for sleep scoring. Therefore, in this study, we aimed to improve the performance of wearable-based sleep scoring algorithms by effectively processing complex wearable data with computational tools. Keep reading
Sleep is a complex biological process regulated by networks of neurons and environmental factors. As one falls asleep, neurotransmitters from sleep–wake regulating neurons work in synergy to control the switching of different sleep states throughout the night. As sleep disorders or underlying neuropathology can manifest as irregular switching, analyzing these patterns is crucial in sleep medicine and neuroscience. Keep reading
Accurate sleep–wake (SW) cycle detection is essential for extracting temporal sleep metrics from actigraphy data. Numerous detection algorithms have been developed for this task, however, there is no guarantee the existing algorithms can generalize to actigraphy data collected by sensors of different manufacturers, worn by diverse populations, or placed on various body locations. For a detailed explanation of why this may occur, please refer to the reasons outlined in our paper. Keep reading
What is the problem being addressed? Commonly used sleep-wake algorithms for wrist actigraphy tend to overclassify sleep, relative to wake. This overclassification is most evident when wrist actigraphy includes data from outside of an ‘in-bed’ (or ‘rest’) interval, as is the case for longitudinal studies of participants in free-living scenarios. Keep reading
Dr. Adam Jones is a Neuroscience Researcher at the University of Southern California. In this interview, Dr. Jones shares about how he used the NSRR to train and evaluate a novel neural network that can score sleep using a single lead of ECG data. The code is available for anyone to use. Keep reading
Dr. Olivia Walch is the CEO of Arcascope, a circadian rhythms software start-up, as well as an investigator in Neurology at the University of Michigan. In her interview with the NSRR, Dr. Walch delves into details of the new SleepAccel dataset available on the NSRR, exciting opportunities for future research, and her thoughts on data (and code) sharing! Check it out below: Keep reading
Our neural network can score sleep using a single lead of ECG data at an equivalent performance to expert human-scored PSG. It was trained and evaluated on 4,000 recordings from subjects 5 to 90 years old. What was the approach or how does the tool work? The neural network was designed with the intention of testing if cardiosomnography (an ECG-only sleep study) could complement or replace polysomnography (PSG). Keep reading
The goal of the study was to see if children’s treatment response to early adenotonsillectomy (eAT) could be predicted using the sleep quality index (SQI), which is based on cardiopulmonary coupling (CPC). We were specifically interested in changes in metabolic health. Keep reading
Read more about the NHLBI workshop here! Broadcast link: https://videocast.nih.gov/ Keep reading
Four key obstructive sleep apnea (OSA) endotypic traits have been identified, namely: collapsibility, upper airway muscle compensation, arousal threshold and loop gain. However, most methods for extracting these traits require specialized training and equipment not available in a standard sleep clinic, which has hampered the ability to assess the full impact of these traits on OSA outcomes. Keep reading