Sebnem Yosunkaya

Examining the relevance with sleep stages of time domain features of EEG, EOG, and chin EMG signals

Sleep staging has an important role in determining sleep disorders such as sleepiness, human fatigue etc. Sleep staging is generally done according to Rechtschaffen and Kales standard (RKS) using EEG signal obtained from PSG signals taken from patient subjects who come with any sleep disorders. Sleep stages are generally divided into three stages including awake, REM and N-REM (stage 1, stage 2, and stage 3). In this study, time domain features of EEG, EOG of right and left eyes, and chin EMG signals belonging to sleep stages were investigated and correlation between these time domain features and sleep stages was calculated. The used time domain features are mean value, standard deviation, peak value, skewness, kurtosis, and shape factor belonging to EEG, EOG of right and left eyes, and chin EMG signals. In experimental studies, PSG recordings of 3 subjects were taken and average recording time of 6.22 h, total recording time was 18.67 h. When investigated correlation coefficients, it is seen that skewness feature in time domain features of EEG signal, standard deviation feature in time domain features of EOG signals belonging to right and left eyes, and mean value feature in time domain features of chin EMG signal were more correlated with sleep stages than other features. Consequently, a feature vector can be constituted combining features determined from time domain features of EEG, EOG belonging to right and left eyes, and chin EMG signals. This obtained feature vector can be easily used in distinguishing sleep stages.

Effect of the Hilbert-Huang transform method on sleep staging

Sleep scoring is performed by examining the recorded electroencephalogram (EEG) and some other signals recorded by a polysomnography (PSG) device. This process is considered more reliable as it is done manually by experts. However, due to the fact that experts may also be mistaken, it has led to an increase in the importance given to automatic sleep staging studies. Many methods have been tested on the signals in order to increase the success of these systems. In this study, an automatic sleep staging system was implemented using the Hilbert-Huang transformation method which is a new time-frequency analysis type. In the study, EEG signals from 5 subjects were used in the sleep laboratory. In the 5-class (Alpha, Beta, Theta, Delta and Spindle bands) applications, the highest classification success was 84.75% as a result of sequential feature selection method.

Elimination of EMG artifacts from EEG signal in sleep staging

Sleep staging is a tiring and time-consuming process for the experts. Thus, attention given to automatic sleep staging studies is increasing gradually. Many factors such as effects of EOG and EKG signals to EEG result in contaminated signals rather than clear recorded signals. EMG contamination to EEG is among that kind of factors. In this study, some filters and Discrete Wavelet Transform based EMG artifact elimination process were evaluated on the performance of sleep staging process. Features were extracted from cleaned EEG signals and subjected to a classifier to conduct sleep staging. By using test classification accuracy as a measure of performance, the method giving highest accuracy was tried to be found. Artificial Neural Networks was used in the applications and Discrete Wavelet Transform was found to be the method giving the highest accuracy.

Examining the effect of time and frequency domain features of EEG, EOG, and Chin EMG signals on sleep staging

Sleep staging has an effective role in diagnosing sleep disorders. Sleep staging is generally done by a sleep expert through examining Electroencephalogram (EEG), Electrooculogram (EOG), Electromyogram (EMG) signals of the patients and determining the stages of sleep in different time sections. This type of sleep staging is preferred among the sleep experts but because it is rather tiring and time consuming task, attention to the automatic sleep staging systems has been begun to increase. In this study, we obtained EEG, EMG and EOG signals of five healthy people in Meram Faculty of medicine to use in sleep staging and extracted 74 features from them. We analyzed the effects of these features on sleep staging. We utilized from the sequential feature selection algorithm and Artificial Neural Networks in this application. We determined which features are more effective in classification of sleep stages and by this way we tried to guide researchers who will use EEG, EMG and EOG features in sleep staging. The highest classification accuracy was obtained as 69.30% with use of four features.

Pneumothorax as an initial manifestation of obstructive sleep apnea syndrome

Case reportA sixty-five-year-old man with bullous lung disease was admitted to emergency service with chest pain and dyspnea that developed during sleep. Pneumothorax was diagnosed both clinically and radiologically. After the chest drainage, the patient presented with a prolonged air leak that required thoracotomy. Further history and occurrence of pneumothorax during sleep suggested that obstructive sleep apnea might play a role in the development of pneumothorax. Nocturnal polysomnography later confirmed the diagnosis of severe obstructive sleep apnea syndrome.DiscussionWe hypothesized that obstructive sleep apnea may be a risk factor for pneumothorax especially in patients with bullous lung disease, and pneumothorax may be listed in the complications of obstructive sleep apnea syndrome.