Musa Hakan Asyali

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.

Nonlinear system identification via Laguerre network based fuzzy systems

In this study, identification of nonlinear systems via Laguerre network based fuzzy model is introduced. We first describe the proposed modeling approach in detail and suggest a fast learning scheme for its training. The proposed approach is applied in three dynamic system modeling problems including Box-Jenkins gas furnace data and forced Van der Pol oscillator. When we compare the performance of the proposed approach against the classical Sugeno and adaptive network based fuzzy inference system modeling, our approach is found to have superior modeling performance and generalization capability.

FPGA based arrhythmia classifier

Processing of ECG (Electro CardioGram) records by software- based systems was started in the beginning of the 1960s. Many studies on different techniques about this topic have been made in the last 20 years. ANN (Artificial Neural Network) is the tool that is mostly used in medical diagnosis systems because of the belief in its powerful prediction characteristics. However, the suggested ANN architectures in literature are very complex software-based architectures. Consequently, these models with high computational complexity can only be run on expensive processors. To enable the implementation of ANN models on mobile and cheap devices, the features of ECG signal, which are applied to ANN inputs, should be reduced. This approach enables the implementation of a simple ANN architecture. In this study, the features of ECG signal are reduced dramatically using PCA (Principle Component Analysis), while keeping the error of the ANN learning rate at an acceptable level such as 5%. As a result, a simple Matlab ANN model, which consists of eight inputs, a hidden layer with two neurons and one output neuron, is implemented on an FPGA (Field Programmable Gate Arrays) by using IEE 754 32 bits floating-point numerical representation.

Staging of the liver fibrosis from CT images using texture features

Even though liver biopsy is critical for evaluating chronic hepatitis and fibrosis, it is an invasive, costly, and difficult to standardize approach. The developments in medical image processing and artificial intelligence methods have advanced the potential of using computer-aided diagnosis techniques in the classification of liver tissues. The aim of this study was to develop a non-invasive, cost-effective, and fast approach to specify fibrosis stage using the texture properties of computed tomography images of liver. Gray level co-occurrence matrix, discrete wavelet transform, and discrete Fourier transform were the image analysis tools in the feature extraction phase. Following dimension reduction of the texture features support vector machines and k-nearest neighbor methods were used in the classification phase of this study. Our results showed that our approach is feasible in fibrosis staging especially in pairwise stage comparisons with success rate of approximately 90%.

Effect of chewing on dental patients with total denture: an experimental study

In this study, we have explored the prospect of assessing and following level of total denture adaptation by use of EMG signals recorded during gum chewing. Total of 14 edentulous patients, 6 women and 8 men, with an average age of 63±9 years, were recruited. Separate EMG recordings were obtained from left and right temporalis and masseter muscles of the patients for a period of 10 seconds, while they were chewing a sugar-free gum on their left and right sides. EMG recordings were repeated at three times: before, right after, and six months after the placement of the denture. We have tried to standardize environmental and individual factors during EMG recordings. The EMG data have been pre-processed and analyzed using Discrete Wavelet Transform (DWT) and obtained features were statistically evaluated using the paired sample t-test. Chewing activity on the right and left side is analyzed by making comparisons of muscle activity between before and right-after cases and before and six-months-after denture fixation cases. A comparison between right and left side mastication is also made at different time points. We have suggested and implemented a new test and comparison procedure in order to assess adaptation to denture fixation using EMG analysis. In this study, the results indicate that DWT based EMG analysis is instrumental in evaluating denture adaptation and as time progresses the adaptation to denture and hence chewing efficiency increases in patients with total denture replacement.

Classification of root canal microorganisms using electronic-nose and discriminant analysis

BackgroundRoot canal treatment is a debridement process which disrupts and removes entire microorganisms from the root canal system. Identification of microorganisms may help clinicians decide on treatment alternatives such as using different irrigants, intracanal medicaments and antibiotics. However, the difficulty in cultivation and the complexity in isolation of predominant anaerobic microorganisms make clinicians resort to empirical medical treatments. For this reason, identification of microorganisms is not a routinely used procedure in root canal treatment. In this study, we aimed at classifying 7 different standard microorganism strains which are frequently seen in root canal infections, using odor data collected using an electronic nose instrument.MethodOur microorganism odor data set consisted of 5 repeated samples from 7 different classes at 4 concentration levels. For each concentration, 35 samples were classified using 3 different discriminant analysis methods. In order to determine an optimal setting for using electronic-nose in such an application, we have tried 3 different approaches in evaluating sensor responses. Moreover, we have used 3 different sensor baseline values in normalizing sensor responses. Since the number of sensors is relatively large compared to sample size, we have also investigated the influence of two different dimension reduction methods on classification performance.ResultsWe have found that quadratic type dicriminant analysis outperforms other varieties of this method. We have also observed that classification performance decreases as the concentration decreases. Among different baseline values used for pre-processing the sensor responses, the model where the minimum values of sensor readings in the sample were accepted as the baseline yields better classification performance. Corresponding to this optimal choice of baseline value, we have noted that among different sensor response model and feature reduction method combinations, the difference model with standard deviation based dimension reduction or normalized fractional difference model with principal component analysis based dimension reduction results in the best overall performance across different concentrations.ConclusionOur results reveal that the electronic nose technology is a promising and convenient alternative for classifying microorganisms that cause root canal infections. With our comprehensive approach, we have also determined optimal settings to obtain higher classification performance using this technology and discriminant analysis.

Evaluation of anxiety related changes in skin conductance and blood volume pulse signals during coronary angiography

Although, stress, sleep deprivation, excessive fatigue and inattention are common health problem in advanced society, there are many methodological problems that make it difficult to measure these emotional states in clinical situations. The main goal of this study is to investigate whether variation of stress due to treatment techniques used in a hospital setting can be successfully assessed and/or quantized using physiological variables. To this end, blood volume pulse and skin conductance signals of 8 patients who underwent angiography operation at the Cardiology Center of Erciyes University (Kayseri, Turkey), are obtained. Recordings were done at three stages: one hour before, during, and one hour after the angiography test. Our preliminary results indicate that the changes in the physiological variables across different subjects are remarkably consistent. This promising result is motivating us to consider and include other possible physiological variables and/or signals in our recording scheme to quantitatively assess changes in the emotional state of subjects undergoing critical operations.

Use of kNN and quadratic discriminant analysis methods for sleep staging from single lead ECG recordings

Sleep consists of REM and four non-REM stages. Determining a persons sleep stage in a certain part of night sleep is performed by the technical experts using the polysomnographic recordings acquired in special sleep laboratories. The acquisition of these recordings for the sleep characterization require not only the connection of various sensors and electrodes to the subject but also spending the night in a bed which is different from the subjects own bed. In this study we investigated the feasibility of using only an electrocardiographic holter device instead of a polysomnography system used in a sleep laboratory for the sleep study and phase determination. For this purpose, single lead ECG data obtained during the night sleep (mean sleep duration 7 hours) from 18 subjects (6 men) with ages between 20 and 67 were used for sleep staging based on R-R interval values. The validation was performed by the sleep stage data previously determined by the sleep experts. Phase determination consists of R-R interval computation, feature extraction and classification studies. The features used in this study were the median value, the difference between the 75 and 25 percentile values, and mean absolute deviations of the R-R intervals computed in each 30-second epoch. The k nearest neighbor (kNN) and quadratic discriminant analysis methods based on one-versus-others approach were used as the classification tools. In the testing procedure cross-validation was employed. As a result, out of awake stage and other five sleep stages four stages were classified accurately at a rate of greater than 80%.

Examining EEG signals with parametric and non-parametric analyses methods in migraine patients during pregnancy

EEG tools are used for diagnosing and interpreting brain diseases at neurology clinics. In this study, EEG data collected from healthy, migraine, and pregnant women were analyzed using Modified Covariance (a parametric spectral analysis method) and Welch (a non-parametric method) methods, in an attempt to re-evaluate the value EEG in migraine diagnosis. We have also compared the performance of different spectral analysis methods in distinguishing different conditions. Further, we have examined changes in the EEG spectral characteristics that occur due to migraine in the pregnancy term.

Effect of diazepam on the sempatovagal balance and O2 saturation

Diazepam is an active ingredient of the drugs which is used in the treatment of the anxiety, insomnia, alcohol withdrawal, and muscle spasm. It has anxiolytic, sedative, muscle relaxant and memory weakening effects. Diazepam is also administrated before surgical operations that require only local anesthesia, to prevent possible anxiety. In this study, we aimed at assessing differences in anxiety levels of patients undergoing local anesthesia. Two groups of subjects with no prior history of psychological disorders were gathered to perform a comparison. One group was administered 5 mg of diazepam and the other was not. In this way, we tried to confirm and discuss whether administering subjects who will undergo local anesthesia in their treatment with diazepam is actually beneficial or not. ECG and SaO2 signals of 16 patients who underwent coronary angiography operation at the Cardiology Center of Erciyes University (Kayseri, Turkey) were recruited. Recordings were done at three stages: one hour before, during, and one hour after the angiography test. Our preliminary results indicate that sempatovagal balance values of the group of with diazepam are greater than the other group and there are not differences in values of SaO2 between two groups.