Necmettin Sezgin

The ANN-based computing of drowsy level

We have developed a new method for automatic estimation of vigilance level by using electroencephalogram (EEG), electromyogram (EMG) and eye movement (EOG) signals recorded during transition from wakefulness to sleep. In the previous studies, EEG signals and EEG signals with EMG signals were used for estimating vigilance levels. In the present study, it was aimed to estimate vigilance levels by using EEG, EMG and EOG signals. The changes in EEG, EMG and EOG were diagnosed while transiting from wakefulness to sleep by using wavelet transform and developed artificial neural network (ANN). EEG signals were separated to its subbands using wavelet transform, LEOG (Left EOG), REOG (Right EOG) and chin EMG was used in ANN process for increasing the accuracy of the estimation rate by evaluating their tonic levels and also used in data preparation stage to verify and eliminate the movement artifacts. Then, training and testing data sets consist of the EEG subbands (delta, theta, alpha and beta); LEOG, REOG and EMG signals were applied to the ANN for training and testing the system which gives three situations for the vigilance level of the subject: Awake, drowsy, and sleep. The accuracy of estimation is about 97-98% while the accuracy of the previous study which used only EEG was 95-96% and the study which used EEG with EMG was 98-99%. The reason of decreasing the percentage of present study according to the last study is because of the increase of the input data.

Estimating vigilance level by using EEG and EMG signals

We developed a new method for estimation of vigilance level by using both EEG and EMG signals recorded during transition from wakefulness to sleep. Previous studies used only EEG signals for estimating the vigilance levels. In this study, it was aimed to estimate vigilance level by using both EEG and EMG signals for increasing the accuracy of the estimation rate. In our work, EEG and EMG signals were obtained from 30 subjects. In data preparation stage, EEG signals were separated to its subbands using wavelet transform for efficient discrimination, and chin EMG was used to verify and eliminate the movement artifacts. The changes in EEG and EMG were diagnosed while transition from wakefulness to sleep by using developed artificial neural network (ANN). Training and testing data sets consist of the subbanded components of EEG and power density of EMG signals were applied to the ANN for training and testing the system which gives three situations for the vigilance level of the subject: awake, drowsy, and sleep. The accuracy of estimation was about 98–99% while the accuracy of the previous study, which uses only EEG, was 95–96%.

Classıfıcation of sleep apnea by using wavelet transform and artificial neural networks

This paper describes a new method to classify sleep apnea syndrome (SAS) by using wavelet transforms and an artificial neural network (ANN). The network was trained and tested for different momentum coefficients. The abdominal respiration signals are separated into spectral components by using multi-resolution wavelet transforms. These spectral components are applied to the inputs of the artificial neural network. Then the neural network was configured to give three outputs to classify the SAS situation of the patient. The apnea can be broadly classified into three types: obstructive sleep apnea (OSA), central sleep apnea (CSA) and mixed sleep apnea (MSA). During OSA, the airway is blocked while respiratory efforts continue. During CSA the airway is open, however, there are no respiratory efforts. In this paper we aim to classify sleep apnea in one of three basic types: obstructive, central and mixed.

A new approach for estimation of obstructive sleep apnea syndrome

Research highlights? OSA patients were differentiated from normals using EEG signal. ? QPC in ?, ?, α, s and γ subbands of EEG were obtained by bispectrum analysis. ? The Quadratic Phase Couplings were quantified and coupled to a NN classifier. ? This technic has provided OSA detection with 96.15% accuracy. Obstructive sleep apnea syndrome (OSAS) is a situation where repeatedly upper airway stops off while the respiratory effort continues during sleep at least for 10s. Apart from polysomnography, many researchers have concentrated on exploring alternative methods for OSAS detection. However, not much work has been done on using non-Gaussian and nonlinear behavior of the electroencephalogram (EEG) signals. Bispectral analysis is an advanced signal processing technique particularly used for exhibiting quadratic phase-coupling that may arise between signal components with different frequencies. From this perspective, in this study, a new technique for recognizing patients with OSAS was introduced using bispectral characteristics of EEG signal and an artificial neural network (ANN). The amount of Quadratic phase coupling (QPC) in each subband of EEG (namely; delta, theta, alpha, beta and gamma) was calculated over bispectral density of EEG. Then, these QPCs were fed to the input of the designed ANN. The neural network was configured with two outputs: one for OSAS and one for estimation of normal situation. With this technique a global accuracy of 96.15% was achieved. The proposed technique could be used in designing automatic OSAS identification systems which will improve medical service.

Classification of Sleep Apnea through Sub-band Energy of Abdominal Effort Signal Using Wavelets + Neural Networks

Detection and classification of sleep apnea syndrome (SAS) is a critical problem. In this study an efficient method for classification sleep apnea through sub-band energy of abdominal effort using a particularly designed hybrid classifier as Wavelets + Neural Network is proposed. The Abdominal respiration signals were separated into spectral sub-band energy components with multi-resolution Discrete Wavelet Transform (DWT). The energy content of these spectral components was applied to the input of the artificial neural network (ANN). The ANN was configured to give three outputs dedicated to SAS cases; obstructive sleep apnea (OSA), central sleep apnea (CSA) and mixed sleep apnea (MSA). Through the network, satisfactory results that rewarding 85.62% mean accuracy in classifying SAS were obtained.

Energy based feature extraction for classification of sleep apnea syndrome

In this paper it is aimed to classify sleep apnea syndrome (SAS) by using discrete wavelet transforms (DWT) and an artificial neural network (ANN). The abdominal and thoracic respiration signals are separated into spectral components by using multi-resolution DWT. Then the energy of these spectral components are applied to the inputs of the ANN. The neural network was configured to give three outputs to classify the SAS situation of the subject. The apnea can be mainly classified into three types: obstructive sleep apnea (OSA), central sleep apnea (CSA) and mixed sleep apnea (MSA). During OSA, the airway is blocked while respiratory efforts continue. During CSA the airway is open, however, there are no respiratory efforts. In this paper we aim to classify sleep apnea in one of three basic types: obstructive, central and mixed. A significant result was obtained.

The Correlation Analysis Between Airflow And Oxygen Saturation In Obstructive Sleep Apnea Events Using Correlation Function

Diagnosis of Sleep apnea syndrome (SAS) is currently performed by a full night polysomnography study at sleep laboratories. The majority of apnea patients are treated by constant positive airway pressure (CPAP) device. The apnea can be broadly classified into three types: obstructive sleep apnea (OSA), central sleep apnea (CSA) and mixed sleep apnea (MSA). During OSA, the airway is blocked while respiratory efforts continue. During CSA the airway is open, however there are no respiratory efforts. There is an efficient correlation between airflow and SaO2 in sleep apnea events. In this paper, it is aimed to find the correlation degree between airflow and oxygen saturation by using cross corelation function in obstructive sleep apnea events. In the future studies the correlation will be able to detect the sleep apnea and controlling CPAP device automatically as an intelligent system.

Diagnostic estimation of OSAS using binary mixture logistic regression

Binary (Binomial) Logistic Regression is a statistical model that can be used for classification. Concerning the targeted outcome, if the variance of observations is higher than the variance of expectations, because of overdispersion the success rate of the method in classification goes down. This overdispersion is thought as arising from the unobserved heterogen samples in the data set. In Composite models, the overdispersion is minimized by clustering the data into homogeneous subsets and performing a subset based process. In this study a composite binary logistic regression was used for estimating the sleep apnea. Through this model, snoring signals were classified and with a 98.16% success rate the apnea was diagnosed.

Time-frequency analysis of snoring sounds in patients with simple snoring and OSAS

In recent years variety of studies has been conducted towards the identification of correlation between Obstructive Sleep Apnea Syndrome (OSAS) and snoring. The features detected from time and frequency domain analysis of snores showed the differences between simple and OSAS patients. In this study the total episodes of 1500 snore records taken from 7 simple and 14 OSAS patients were evaluated through time-frequency analysis. From the time-frequency analysis the differences, particularly from the spectral bandwidth point of view, between the two groups were identified, and using this data the method was suggested as a cost effective and simple technique to be widely used in detection of OSAS from simple patients.

A novel approach to diagnosis of sleep apnea from snoring signals: Ternary pattern method

In this study, a new approach for estimation of Obstructive Sleep Apnea Syndrome (OSAS) was proposed. OSAS is a sleep disorder that affects the life comfortability in human life. Diagnosis of OSAS is usually done by expensive devices and specialist physicians. Since OSAS is serious, it should be diagnosed and treated early. In this study, a new feature extraction method is proposed for OSAS diagnosis from snoring signals. With one (1) dimensional ternary pattern method, effective attributes were extracted from raw snoring signals and identification process was performed by classification methods. According to the obtained results, 1D-TP method has shown significant success in diagnosing OSAS from snore signals. The results can be used in sleep laboratory for help to experts before put patient to the Polysomnography (PSG) test.