Aykut Erdamar

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.

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.

Automated Detection and Classification of Sleep Apnea Types Using Electrocardiogram (ECG) and Electroencephalogram (EEG) Features

1.1 Sleep and sleep disorders Sleep, which is defined as a passive period in organic physiology until the mid-20th century, is accepted to be an indispensable period of life cycle with today’s technological advances. While wakefulness is associated with the active excitation of Central Nervous System (CNS), sleep has been recognized as a passive period by the elimination of excitation. However, recent studies have shown that sleep is independent of wakefulness, generated by a sequence of changes in CNS, and a combination of five periods with clear boundaries. Sleep is not the disruption of daily life for a period of time or a waste of time. It is an active period which is important to renew our mental and physical health everyday and is covering one– third of our lives. Sleep activity is important for resting during the working period of basal metabolism of human body. The advances in technology enabling the measurement and quantification of brain activity make possible the micro and macro analysis of brain during both sleep and wakefulness states. With the studies investigating the CNS, it is observed the existence of some centrals causing the sleep by inhibiting the other regions of brain. As a result, sleep, which is an active and other state of consciousness, is a brain state of high coordination (Erdamar, 2007). Since breathing is established autonomously during sleep, it is affected by many anatomical and physiological parameters. Depending on this situation, various sleep disorders occur. There are more than eighty known sleeping diseases. Most of them cause person’s health to deteriorate and a decrease in life quality. As a result of the research carried out for many years, a list of sleep disorders, which are generally occurring, can be seen as in Table 1. Sleep disorders can be examined in two classes, parasomnia and dissomnia.

Continuous wavelet transform based method for detection of arousal

Sleeping is a vital biological requirement in the homeostatic mechanism. Sleep disorders can cause personal health problems and life quality deterioration. Transient waveforms (k-complexes, sleep spindles, arousal, etc.) happens instantaneously in sleep, have distinctive structural features, amplitudes and frequencies, and are difficult to distinguish from the background of electroencephalography (EEG) which are called the microstructure of the EEG. Microstructure analysis is important for brain research, sleep studies, sleep stage scorings and assessment of sleep disorders. Detection of arousal, one of these structures, is performed by visual scoring of all night sleep records by an expert sleep physician. In particular, visual analysis of the microstructure is difficult and time-consuming task so it increases error rates in scoring. The aim of this study is automatic detection of arousals from EEG signals. A computer-aided diagnosis system that can detect arousal can give more objective results to the expert sleep experts for diagnosing. In this study, continuous wavelet transform analysis of scored EEG signals was performed and features were obtained. As a result, a decision support system algorithm for arousal has been developed using the obtained features and support vector machines. The specificity of the algorithm is 100% and sensitivity is 95.45%.

Detection of K-complexes in sleep EEG with support vector machines

Sleep is a state that can be characterized by the electrical oscillations of nerve cells, where brain activity is more stable than waking. Transient waveforms observed in sleep electroencephalography are structures with specific amplitude and frequency characteristics that can occur in some stages of sleep. The determination of the k-complex, which is one of these structures, is performed by visual scoring of all night sleep recordings by expert physicians. For this reason, a decision support system that allows automatic detection of the k-complex can give physicians more objective results in diagnosis. In this study, sleep EEG records scored by a physician were analyzed in different methods from the literature. Three features have been determined that express the k-complex presence and k-complexes were detected using these features and support vector machines. As a result, the performance of the algorithm was evaluated and sensitivity and specificity were determined as 70.83 % and 85.29%, respectively.

Sleep apnea detection using with EEG, ECG and respiratory signals

Sleep apnea is one of the major sleep disorders of today. The diagnosis of sleep apnea is performed by specialist physicians. This situation extends the duration of the diagnosis. To shorten this period and at the same time to avoid the mistakes that may occur in diagnosis, an automated decision support system has been considered in the diagnosis and classification of sleep apnea. In this study, ECG signal was analyzed to obtain Heart Rate Variability (HRV) signal and the power spectral density (PSD) of this signal was examined. It has been observed that the low and high frequency energy ratios are different in the PSD of examined HRV. Parallel to this analysis, the energy of the respiratory signal is obtained and it is understood that there is a significant energy exchange in the apnea cases. However, the powers of the frequency bands in the EEG signal were found separately and the ratios of these bands to each other were calculated. In the analysis, it was observed that the ratios of alpha and beta bands to each other were different between apnea and non-apnea periods. By using these differences, an artificial neural network (ANN) algorithm is constructed to diagnose and classify the sleep apnea. This algorithm was tested on two patient data; ANN was trained and tested separately for each patient. As a result, it was determined that the average accuracy rate of ANN is high.

A new image segmentation method for quantitative analysis of in vitro scratch assay

Image processing techniques are frequently used for extracting quantitative information from different types of microscopic images. Particularly, in cell biology (cell migration, growth, wound healing etc.), image analysis is time consuming, and requires personal expertise. In addition, evaluation of the results may be subjective. Therefore, computer-based learning/ vision-based applications have been developed rapidly in recent years. In this study, micrographs captured during scratch assay for the determination of migration of monolayer adherent carcinoma cells were analyzed by using image processing techniques. Scratch and cell monolayer areas were determined by using multi-tresholding and morphological image processing in analysis. In conclusion, scratch area and its border lines were automatically determined, and scratch and cell monolayer areas were calculated.

Image analysis for single cell gel electrophoresis

Detection of dioxyribonucleic acid (DNA) damage is very crucial in various areas of life sciences and in the clinical diagnosis of some pathophysiologies. Single cell gel electrophoresis, also called Comet Assay, is a reliable and easily applicable method to measure/detect level of DNA damage which is an indicator of an genotoxic and cytotoxic effect on living organisms caused by chemical and phsyical activity. The method is generally based on the fact that the DNA in the nucleus isolated from living tissues is placed in a thin agarose gel and run on an electrophoretic medium. DNA images obtained with the Comet protocol can be evaluated visually as well as can be analyzed using various software today. With such software, objective results can be obtained in a short period of time and without adhering to the researchers experience. In this study, Comet analysis images obtained from HepG2 (ATCC HB-8065) hepatocellular carcinoma liver cancer cells were used. Calculation of measurement results of these user-selected images and presenting parametric data to the user are intended.

The Quantitative Analysis of Uvulopalatal Flap Surgery

Amaç: Bu çalışmada, uvulopalatal flap cerrahisinin kantitatif analizi için sinyal işleme tekniklerine dayanan yeni bir metodoloji önerilmektedir. Uvulopalatal flap ameliyatı ile ilgili klinik değerlendirme çalışmaları, doktorların muayenesine bağlı olmakla birlikte, hastanın subjektif geribildirimini de içermektedir. Kantitatif ve objektif değerlendirme çalışması literatürde halen eksiktir. Gereç ve Yöntem: Ameliyat öncesinde ve sonrasında 21 hastanın tüm gece uyku kayıtları analiz edilmiştir. Önerilen algoritma, iki bağımsız bölümden oluşmaktadır. Birinci bölümde, kalp hızı değişkenliği ve elektrokardiyogramın karmaşıklığı hesaplanmıştır. İkinci bölüm ise, elektroensefalogram alt bantlarının enerjilerinin hesaplanmasını içerir. Ardından, klinik ve deneysel parametreler arasındaki ilintinin belirlenmesi için istatistiksel yöntemler uygulanmıştır. Bulgular: Düşük frekans/yüksek frekans oranı ve beta dalgasının alt bant enerjisi, ameliyat sonrası delta uyku süresi düşük olan hastalar için anlamlıdır. Ayrıca, hem ameliyat sonrası delta uyku süresi hem de kan gazında ölçülen O2 satürasyonu (SaO2) parametresi yüksek olanlar için alfa ve beta dalgalarının ve teta dalgasının alt bant enerjileri anlamlıdır. Ameliyat sonrası solunum bozukluğu endeksi ve SaO2 parametresi düşük olan hastalarda karmaşıklık anlamlıdır ve solunum bozukluğunun horlama indeksi ile ilintisi bulunmaktadır. Sonuç: Ameliyat öncesi ve sonrasındaki klinik bulgulara göre anlamlı olmayan solunum bozukluğu indeksinin karmaşıklık özelliği ile doğrudan ilişkili olduğu bulunmuştur. Bu çalışmanın en önemli sonucu, ameliyat öncesi karmaşıklık özelliğinin solunum bozukluğu ve horlama indeksi ile ilintili olmasıdır. Bu, karmaşıklık özelliğinin cerrahiden önce bir öngörü parametresi olabileceği anlamına gelmektedir. Anahtar Kelimeler: Uyku bozuklukları, obstrüktif uyku apnesi, uvulopalatal flap cerrahisi, karmaşıklık, Hjorth parametreleri Objective: In this work, a new methodology based on signal processing techniques for the quantitative analysis of uvulopalatal flap surgery is proposed. Clinical assessment studies of uvulopalatal flap surgery are based on not only the physician’s examination, but also the patient’s subjective feedback. Quantitative and objective evaluation studies are still lacking in the literature. Materials and Methods: Full night sleep records were analyzed for 21 patients before and after the surgery. The proposed algorithm consists of two independent parts. In the first part, the heart rate variability and complexity of the electrocardiogram were calculated. The second part includes calculating the electroencephalogram sub-band energy. Afterwards, the statistical methods were applied in order to determine the correlation of clinical and experimental parameters. Results: The low frequency/high frequency ratio and the sub-band energy of beta wave were significant for the patients having low postoperative delta sleep duration. Moreover, the sub-band energies of both alpha and beta waves, and theta wave were significant for the patients who had high post-operative delta sleep duration and blood oxygen saturation (SaO2)-parameter. Complexity was significant for the patients with low postoperative respiratory disturbance index and SaO2 parameter, and respiratory disturbance is correlated with snoring index. Conclusion: Respiratory disturbance index, which is not significant according to the preand post-operative clinical findings, was found to be directly related to the complexity feature. The most important result of this work is that the pre-operative complexity feature is correlated with respiratory disturbance and snoring index. This means that complexity feature can be a predictor prior to surgery.

Detection of epilepsy disease from EEG signals with artificial neural networks

The diagnosis of the epilepsy diseases are made by physicians with analyzing the electroencephalography (EEG) records. The epilepsy diseases can be determined with observing the main properties of before and on-time seizure signals in time and frequency domain. Physicians are evaluating the results after some necessary scoring on EEG records. However, this evaluation is specialistic, time consuming processes and also may subjective results. At this point, to allow detection of epilepsy diseases, a decision support system can give more objective results to the physicians for diagnosing. The subject of the study is automatically diagnosing the epilepsy diseases on EEG signals. In the proposed study, analyses of EEG signals in time and frequency domain were done and features of diseases were obtained. As a result, using artificial neural network (ANN) and obtained features, a decision support system is realized to diagnose the epilepsy. The specificity and the sensitivity of the algorithm are 94% and 66% respectively.