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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%.
To assess the quality of a person’s sleep, it is essential to examine the sleep behaviour by identifying the several sleep stages, their durations and sleep cycles. The established and gold standard procedure for sleep stage scoring is overnight polysomnography (PSG) with the Rechtschaffen and Kales (R-K) method. Unfortunately, the conduct of PSG is timeconsuming and unfamiliar for the subjects and might have an impact of the recorded data. To avoid the disadvantages with PSG, it is important to make further investigations in low-cost home diagnostic systems. For this intention it is necessary to find suitable bio vital parameters for classifying sleep stages without any physical impairments at the same time.
Due to the promising results in several publications we want to analyse existing methods for sleep stage classification based on the parameters body movement,
heartbeat and respiration. Our aim was to find different behaviour patterns in the several sleep stages. Therefore, the average values of 15 wholenight PSG recordings -obtained from the ‘DREAMS Subjects Database’- where analysed in the light of heartbeat, body movement and respiration with 10 different methods.