Sinan Yetkin

Sleep stage and obstructive apneaic epoch classification using single-lead ECG

BackgroundPolysomnography (PSG) is used to define physiological sleep and different physiological sleep stages, to assess sleep quality and diagnose many types of sleep disorders such as obstructive sleep apnea. However, PSG requires not only the connection of various sensors and electrodes to the subject but also spending the night in a bed that is different from the subjects own bed. This study is designed to investigate the feasibility of automatic classification of sleep stages and obstructive apneaic epochs using only the features derived from a single-lead electrocardiography (ECG) signal.MethodsFor this purpose, PSG recordings (ECG included) were obtained during the nights sleep (mean duration 7 hours) of 17 subjects (5 men) with ages between 26 and 67. Based on these recordings, sleep experts performed sleep scoring for each subject. This study consisted of the following steps: (1) Visual inspection of ECG data corresponding to each 30-second epoch, and selection of epochs with relatively clean signals, (2) beat-to-beat interval (RR interval) computation using an R-peak detection algorithm, (3) feature extraction from RR interval values, and (4) classification of sleep stages (or obstructive apneaic periods) using one-versus-rest approach. The features used in the study were the median value, the difference between the 75 and 25 percentile values, and mean absolute deviations of the RR intervals computed for each epoch. The k-nearest-neighbor (kNN), quadratic discriminant analysis (QDA), and support vector machines (SVM) methods were used as the classification tools. In the testing procedure 10-fold cross-validation was employed.ResultsQDA and SVM performed similarly well and significantly better than kNN for both sleep stage and apneaic epoch classification studies. The classification accuracy rates were between 80 and 90% for the stages other than non-rapid-eye-movement stage 2. The accuracies were 60 or 70% for that specific stage. In five obstructive sleep apnea (OSA) patients, the accurate apneaic epoch detection rates were over 89% for QDA and SVM.ConclusionThis study, in general, showed that RR-interval based classification, which requires only single-lead ECG, is feasible for sleep stage and apneaic epoch determination and can pave the road for a simple automatic classification system suitable for home-use.

Efficient sleep spindle detection algorithm with decision tree

In this study, an efficient sleep spindle detection algorithm based on decision tree is proposed. After analyzing the EEG waveform, the decision algorithm determines the exact location of sleep spindle by evaluating the outputs of three different methods namely: Short Time Fourier Transform (STFT), Multiple Signal Classification (MUSIC) algorithm and Teager Energy Operator (TEO).The EEG records collected from patients used in this study have been recorded at the Sleep Research Center in Department of Psychiatry of Gulhane Military Medicine Academy. The obtained results are in agreement with the visual analysis of EEG evaluated by expert physicians. The method is applied to 16 distinct patients, 420,570 minutes long EEG records and the performance of the algorithm was assessed for the sleep spindles detection with 96.17% sensitivity and 95.54% specificity. As a result, it is found that the proposed sleep spindle detection algorithm is an efficient method to detect sleep spindles on EEG records.

Mirtazapine augmentation in depressed patients with sexual dysfunction due to selective serotonin reuptake inhibitors

To evaluate the effect of mirtazapine augmentation in patients with sexual dysfunction induced by current selective serotonin reuptake inhibitor (SSRI) treatment.

Impact of endoscopic sinus surgery on sleep quality in patients with chronic nasal obstruction due to nasal polyposis.

Objective: The aim of this study was to investigate the effect of endoscopic sinus surgery on sleep quality in a patient group who has chronic nasal obstruction resulting from nasal polyposis. Methods: Twenty-seven patients with nasal polyposis, filling at least 50% of each nasal passage, were enrolled in the study. Assessment of nasal patency was determined by nasal endoscopy and acoustic rhinometry. All patients underwent endoscopic sinus surgery with polypectomy. Sleep quality was evaluated, using visual analog scale, Epworth sleepiness scale, and polysomnography before and 3 months after the surgery. Results: Nasal resistance decreased significantly after the surgery (P < 0.01). Snoring scores were significantly improved postoperatively (P < 0.01) and completely disappeared in 9 of 27 patients. A significant improvement occurred in mean daytime sleepiness scores in the postoperative period (4.14) as compared with the preoperative values (9.44; P < 0.01). There was no significant difference between preoperative (6.85) and postoperative (5.53) mean values of apnea-hypopnea index (P = 0.55). Conclusions: Endoscopic sinus surgery with polypectomy significantly improves sleep quality, including snoring and daytime sleepiness in patients with chronic nasal obstruction due to nasal polyposis. However, it has a limited benefit on apnea-hypopnea index scores.

Evaluation of periodic leg movements and associated transcranial magnetic stimulation parameters in restless legs syndrome.

Restless legs syndrome (RLS), a sensorimotor disorder characterized by unpleasant sensations commonly localized in the legs, is frequently associated with periodic limb movements (PLMs) during sleep. We investigated the role of transcranial magnetic stimulation (TMS) and cortical silent period (CSP) duration as diagnostic and monitoring tools in 20 patients with primary RLS before and after 1 month of treatment and also studied 15 normal age‐ and gender‐matched subjects. Polysomnographic assessment was undertaken and the PLM index determined in 17 of the 20 patients. We also studied the correlation between sleep efficiency index and CSP duration because of the increasing severity of the sleep disturbance and PLMs in patients with RLS. Our results demonstrate that the duration of the CSP was reduced in patients with RLS, and that dopaminergic treatment normalized this duration. There was no correlation between the PLM index and CSP duration. It may be speculated that PLMs and the CSP are due to different inhibitory mechanisms and they may be used separately as diagnostic and monitoring tools in patients with primary RLS. Muscle Nerve, 2005

Polysomnography in patients with post-traumatic stress disorder

Aims:  The purpose of the present study was to investigate sleep structure in post‐traumatic stress disorder (PTSD) patients with and without any psychiatric comorbidities. The relationship between sleep variables and measurements of clinical symptom severity were also investigated.

A wavelet and teager energy operator based method for automatic detection of K-Complex in sleep EEG

In this study, an efficient algorithm is proposed for the automatic detection of K-complex from EEG recordings. First, the morphology of the K-complex had been examined and the detection features were determined according to visual recognition criterions of human scorer. These features were based on amplitude and duration properties of K-complex waveform. The algorithm is based on wavelet and teager energy operator and includes two main stages. Both results of stages were combined to make robust decision. The EEG recordings obtained from the Sleep Research Laboratory in Department of Psychiatry at Gulhane Military Medical Academy. All night sleep EEG data, total 1045 epochs and 690 of these are NREM 2 stage, from 25 years old healthy female subject were used. Three scorers inspected recording separately to score K-complexes. The detection algorithm was then tested on the same recording. The results show that the agreements between the scorers were fairly different. The results are evaluated with the ROC analysis which proves up to 91% success in detecting the K-complex.

Prevalence of insomnia symptoms: results from an urban district in Ankara, Turkey

Objective. Characteristics of insomnia symptoms in Turkey are not well established. The goal of this study was to determine the prevalence of insomnia and related symptoms in an urban district of Turkey. Method. The study was carried out in Ankara, in an urban district with a population of 2665. Out of the 1332 people in the sample, 1034 in the 15–65 age range were included in the study. Interviews were conducted according to the “Sleep Disorders Assessment Questionnaire” developed by the researchers. The Insomnia Severity Index (ISI) was also given to the subjects with a sleep problem to measure the subjective quality and quantity of insomnia symptoms. Results and conclusion. A total of 29.4% of all participants reported a sleep problem, out of which 23.7% defined one or more of the insomnia symptoms which included difficulty initiating sleep (DIS), difficulty maintaining sleep (DMS), early morning awakening (EMA), non-restorative sleep (NRS) and sleep deprivation (SD). Insomnia risk was found to be significantly increased with age, female sex, smoking and chronic medical illness. A total of 75.9% of participants who reported insomnia symptoms did not seek medical help for their complaint. According to the ISI, among the subjects with insomnia symptoms, 79 (32.2%) had subthreshold insomnia, 43 (17.6%) had clinical insomnia, 12 (4.9%) had severe clinical insomnia, while 88 (35.9%) did not score in the range indicating insomnia. The findings are discussed in the light of previous research and in relation to sociocultural factors emphasizing the need for public education on sleep disorders as medical conditions.

Mutual Information Analysis of Sleep EEG in Detecting Psycho-Physiological Insomnia

The primary goal of this study is to state the clear changes in functional brain connectivity during all night sleep in psycho-physiological insomnia (PPI). The secondary goal is to investigate the usefulness of Mutual Information (MI) analysis in estimating cortical sleep EEG arousals for detection of PPI. For these purposes, healthy controls and patients were compared to each other with respect to both linear (Pearson correlation coefficient and coherence) and nonlinear quantifiers (MI) in addition to phase locking quantification for six sleep stages (stage.1–4, rem, wake) by means of interhemispheric dependency between two central sleep EEG derivations. In test, each connectivity estimation calculated for each couple of epoches (C3-A2 and C4-A1) was identified by the vector norm of estimation. Then, patients and controls were classified by using 10 different types of data mining classifiers for five error criteria such as accuracy, root mean squared error, sensitivity, specificity and precision. High performance in a classification through a measure will validate high contribution of that measure to detecting PPI. The MI was found to be the best method in detecting PPI. In particular, the patients had lower MI, higher PCC for all sleep stages. In other words, the lower sleep EEG synchronization suffering from PPI was observed. These results probably stand for the loss of neurons that then contribute to less complex dynamical processing within the neural networks in sleep disorders an the functional central brain connectivity is nonlinear during night sleep. In conclusion, the level of cortical hemispheric connectivity is strongly associated with sleep disorder. Thus, cortical communication quantified in all existence sleep stages might be a potential marker for sleep disorder induced by PPI.

Cortical correlations in wavelet domain for estimation of emotional dysfunctions

In the present study, the level of nonlinear inter-hemispheric synchronization has been estimated by using wavelet correlation (WC) method for detection of emotional dysfunctions. Due to non-stationary nature of EEG series in addition to the assumption that the high-frequency band is possibly associated with emotional activation, WC has been applied to five distinct frequency band activities (fba) (Delta: