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A hierarchical classification method for automatic sleep scoring using multiscale entropy features and proportion information of sleep architecture

Tian P., Hu J., Qi J., Ye X., Che D., Ding Y., Peng Y.

Biocybernetics and Biomedical Engineering

2017

Vol. 37, no. 2

263--271

Background: Sleep scoring is a critical step in medical researches and clinical applications. Traditional visual scoring method is based on the processing of physiological signals, such as electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG), which is a time consuming and subjective procedure. It is an urgent task to develop an effective method for automatic sleep scoring. Method: This paper presents a hierarchical classification method for automatic sleep scoring by combining multiscale entropy features with the proportion information of the sleep architecture. Based on a three-layer classification scheme, sleep is categorized into five stages (Awake, S1, S2, SWS and REM). Specifically, the first layer is a standard SVM which performs classification between Awake and Sleep, while the second and third layers are implemented by combining probabilistic output SVM with proportion-based clustering. Multiscale entropy (MSE) from electroencephalogram (EEG) is extracted to represent signal characteristics in multiple temporal scales. Results: The proposed method is evaluated with 20 sleep recordings, including 10 subjects with mild difficulty falling asleep and 10 healthy subjects. The overall accuracy of the proposed method is 91.4%. Compared with traditional methods, the classification accuracy of the proposed method is more balanced and the global performance is much better. The dataset includes both healthy subjects and subjects with sleep disorders, which means the presented method has good generalization capacity. Experimental results demonstrate the feasibility of the attempt to introduce proportion information into automatic sleep scoring.

The influence of repetitive transcranial magnetic stimulation on sleep in parkinson's disease

Antczak J., Rakowicz M., Banach M., Derejko M., Sienkiewicz J., Zalewska U., Więcławska M., Jernajczyk W.

Biocybernetics and Biomedical Engineering

2011

Vol. 31, no. 3

35-46

Sleep disturbance is common in Parkinson's disease (PD). In this study we investigated the effect of a novel therapeutic tool, repetitive transcranial magnetic stimulation (rTMS) on sleep quality in PD patients. The study group consisted of 11 PD patients who underwent ten daily rTMS sessions at 15 Hz. Their sleep patterns were monitored with polysomnography. After the stimulation, non-REM stage-1 sleep and the number of nocturnal arousals decreased, thus improving sleep quality. These changes were probably related to the improvement of motor symptoms observed in UPDRS and in the 9 Hole peg test.

Wielostopniowa procedura monitorowania zaburzeń oddychania podczas snu (ZOPS)

Hatliński G., Kornacki W., Kukwa A.

Acta Bio-Optica et Informatica Medica. Inżynieria Biomedyczna

2006

Vol. 12, nr 1

4-8

Diagnostyka zaburzeń oddychania podczas snu (ZOPS) jest młodą i trudną dziedziną kliniczną, ale już obecnie ocenia się, że przypadłość związaną z nieprawidłowym oddychaniem podczas snu dotyka ponad 5% populacji ludzkiej. Chrakterystyczną cechą tych zaburzeń jest ograniczenie lub brak przepływu powietrza przez górne drogi oddechowe w czasie snu. Skrajnym przypadkiem ZOPS jest zespół snu z bezdechami (ZSzB, Sleep Apnea Syndrome). Podstawowym narzędziem do diagnostyki ZOPS jest polisomnografia. Opracowane i wdrożone przez nasz zespół systemy polisomnograficzne, które tworzą wielostopniową (czterostopniową) procedurę monitorowania ZOPS, składają się w kolejności z: a)pulsoksymetrii, b)małej polisomnografii (mPSG), c)dużej polisomnografii (PSG), d)hiperpolisomnografii (HPSG). Oryginalnym rozwiązaniem jest równoczesne monitorowanie ZOPS i zaburzeń sercowo-naczyniowych w ramach HPSG. Omówione również zostały nowe metody analizy danych oraz specyfika biopomiarów w poszczególnych procedurach monitorowania ZOPS.

The diagnostic of respiratory disorders during a sleep (RDDS) is a quite new and difficult branch of clincial medicine. It is estimated now that ca. 5% of population suffers from incorrect respiratory process during a sleep. The charracteristic for these disorders is limitation or a complete obstacle to air flow through upper airways in a sleeping patient. The most severe case of the disorders is a Sleep Apnea Syndrome (SAS). The basic method for the diagnosis of RDDS is polysomnography. The developed by our team polysomnography systems base on multistage (four stage) monitoring procedure consisting of: a)pulsoximetry, b)small polysomnography (SPSG), c)large polysomnography (PSG), d)hiperpolysomnography (HPSG). The original idea behind this methodology is simultaneous monitoring of respiratory and cardiovascular disorders in HPSG. The paper also presents new methods of data analysis and biomeasurements specificity in particular monitoring procedures of RDDS.