Abstract
Sleep staging is a fundamental but time consuming process in any sleep laboratory. To greatly speed up sleep staging without compromising accuracy, we developed a novel framework for performing real-time automatic sleep stage classification. The client–server architecture adopted here provides an end-to-end solution for anonymizing and efficiently transporting polysomnography data from the client to the server and for receiving sleep stages in an interoperable fashion. The framework intelligently partitions the sleep staging task between the client and server in a way that multiple low-end clients can work with one server, and can be deployed both locally as well as over the cloud. The framework was tested on four datasets comprising 1700 polysomnography records (12000 hr of recordings) collected from adolescents, young, and old adults, involving healthy persons as well as those with medical conditions. We used two independent validation datasets: one comprising patients from a sleep disorders clinic and the other incorporating patients with Parkinson’s disease. Using this system, an entire night’s sleep was staged with an accuracy on par with expert human scorers but much faster (5 s compared with 30–60 min). To illustrate the utility of such real-time sleep staging, we used it to facilitate the automatic delivery of acoustic stimuli at targeted phase of slow-sleep oscillations to enhance slow-wave sleep.
Highlights
Polysomnography (PSG) is the primary tool used for quantitatively assessing sleep and involves concurrent acquisition of multiple physiological signals comprising the electroencephalogram (EEG), electrooculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG)
The classification blocks were trained using 75 per cent of DS1 and DS2, whereas the remaining 25 per cent were used for testing
Part of the discrepancy in scoring can be attributed to scorer errors and bias, but most disagreements in scoring were from epochs that cannot be clearly assigned a sleep stage [35]
Summary
Polysomnography (PSG) is the primary tool used for quantitatively assessing sleep and involves concurrent acquisition of multiple physiological signals comprising the electroencephalogram (EEG), electrooculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG). Standardized rules for sleep staging using PSG were first laid out by Rechtschaffen and Kales [1] (R&K) in 1968. Numerous automatic sleep stage classification schemes have been proposed and demonstrated, but time consuming and resource intensive human expert review remains the main method by which sleep staging is performed in most clinical and sleep research labs worldwide. Automated systems typically incorporate particular carefully engineered features extracted from PSG data into a classification algorithm. The large variety of methods used, lack of convenient software for clinical users, concern about generalization of the methodology beyond the test samples used to demonstrate proof-of-concept, as well as earlier limitations in computational power, have hindered the broad employment of automated sleep staging systems
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
