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Sleep stages classification using vital signals recordings

  • To evaluate the quality of a person's sleep it is essential to identify the sleep stages and their durations. Currently, the gold standard in terms of sleep analysis is overnight polysomnography (PSG), during which several techniques like EEG (eletroencephalogram), EOG (electrooculogram), EMG (electromyogram), ECG (electrocardiogram), SpO2 (blood oxygen saturation) and for example respiratory airflow and respiratory effort are recorded. These expensive and complex procedures, applied in sleep laboratories, are invasive and unfamiliar for the subjects and it is a reason why it might have an impact on the recorded data. These are the main reasons why low-cost home diagnostic systems are likely to be advantageous. Their aim is to reach a larger population by reducing the number of parameters recorded. Nowadays, many wearable devices promise to measure sleep quality using only the ECG and body-movement signals. This work presents an android application developed in order to proof the accuracy of an algorithm published in the sleep literature. The algorithm uses ECG and body movement recordings to estimate sleep stages. The pre-recorded signals fed into the algorithm have been taken from physionet1 online database. The obtained results have been compared with those of the standard method used in PSG. The mean agreement ratios between the sleep stages REM, Wake, NREM-1, NREM-2 and NREM-3 were 38.1%, 14%, 16%, 75% and 54.3%.

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Metadaten
Author:Agnes Klein, Oana Ramona Velicu, Natividad Martínez MadridORCiD, Ralf SeepoldORCiDGND
URL:http://ieeexplore.ieee.org/document/7356980/
ISBN:978-8-8875-4808-2
Parent Title (English):12th International Workshop on Intelligent Solutions in Embedded Systems WISES), 29-30 Oct. 2015, Ancona, Italy
Volume:2015
Document Type:Conference Proceeding
Language:English
Year of Publication:2015
Release Date:2019/03/04
Tag:Algorithm; Body-movement; Heartbeat; Non REM stage; REM stage; Sleep stage
First Page:47
Last Page:50
Note:
Volltextzugriff im Campusnetz der Hochschule Konstanz möglich.
Relevance:Keine peer reviewed Publikation (Wissenschaftlicher Artikel und Aufsatz, Proceeding, Artikel in Tagungsband)
Open Access?:Nein
Licence (English):License LogoLizenzbedingungen IEEE