![]() While these can make clinical sleep studies more cost efficient and consistent, the absence of an easily accessible and reliable screening mechanism still leaves a large diagnosis gap between doctors and patients. Multiple machine learning algorithms have been devised to automate sleep scoring and recent algorithms have reached near human level scoring using PSG data 11. Manual scoring also has considerable interscorer and intrascorer variability, making its reliability and reproducibility questionable 10. Laboratory PSG studies can cause significant disruption to the patient’s sleep and fail to capture a patient’s normal sleep patterns 9. These studies may have other disadvantages as well. Therefore PSG studies are expensive and often only used after significant progression of a patient’s symptoms 1. ![]() Once the data are collected, scoring requires an expert to spend up to 2 hours to analyze and manually annotate each night. Therefore measuring sleep behavior can diagnose sleep disorders and also lead to early detection of other health conditions.Ĭurrently, clinical sleep diagnosis requires polysomnography (PSG) study to measure overnight electroencephalogram (EEG), electrooculogram (EOG), electrocardiogram (ECG), airflow, and other signals. Moreover, sleep deficiency and anomalies in sleep architecture are linked to many chronic health problems, including sleep apnea, diabetes, stroke, brain injury, Parkinson’s disease, depression, and Alzheimer’s disease 2, 3, 4, 5, 6, 7, 8. As symptoms appear during sleep, they are not easily apparent to patients and most sleep disorders remain undiagnosed 1. Finally, we demonstrate that the stages predicted by our algorithm can reproduce previous clinical studies correlating sleep stages with comorbidities such as sleep apnea and hypertension as well as demographics such as age and gender.Ībout 50 to 70 million Americans suffer from sleep or wakefulness disorders. Moreover, we demonstrate that the algorithm generalizes well to an independent dataset of 993 subjects labeled by American Academy of Sleep Medicine (AASM) licensed clinical staff at Massachusetts General Hospital that was not used for training or validation. The algorithm has an overall performance of 0.77 accuracy and 0.66 kappa against the reference stages on a held-out portion of the SHHS dataset for classifying every 30 s of sleep into four classes: wake, light sleep, deep sleep, and rapid eye movement (REM). We trained and validated an algorithm on over 10,000 nights of data from the Sleep Heart Health Study (SHHS) and Multi-Ethnic Study of Atherosclerosis (MESA). In this study, we applied deep learning methods to create an algorithm for automated sleep stage scoring using the instantaneous heart rate (IHR) time series extracted from the electrocardiogram (ECG). Sleep staging using cardiac rhythm is an active area of research because it can be measured much more easily using a wide variety of both medical and consumer-grade devices. The following is an example to show the various ways you can apply filtering and de-noising to a signal.Clinical sleep evaluations currently require multimodal data collection and manual review by human experts, making them expensive and unsuitable for longer term studies. There's an accompanying demo, just run sgolaydemo. If you have access to the Signal Processing Toolbox, then check out the Savitzky-Golay filter, namely the function sgolay.
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