In this study, transimpedance amplifier based front-end circuits which can be employed to measure small capacitances were designed, analyzed and simulated using analog electronic circuit simulator. The front-end circuit converts the current flowing through the measured capacitance into a modulated voltage value which contains information regarding the desired capacitance. The frequency-domain, time-domain, stability and noise analyzes were carried out numerically and in simulation environment using a circuit simulator. The analytical, numerical and simulation results can be used to design optimized, precise and stable transimpedance amplifiers with low-noise value. The measured capacitance value was 10 pF which is low enough to simulate various real-world applications. Three commercially available, off-the-shelf operational amplifiers with different peripheral passive components were employed for computer based analysis. The designed transimpedance amplifiers are suitable to connect with capacitance extraction circuits which use analog or digital demodulation techniques.

This paper presents a lossy capacitance measuring circuit which is based on analog lock-in detection technique. Lossy capacitance can be modelled as a pure capacitor connected in parallel with a resistor. The measurement circuit mechanism consists of an excitation signal to drive the lossy capacitance, a transimpedance amplifier to produce a voltage, and a lock-in detection circuit to extract lossy values of capacitance. The lock-in detector multiplies its input with a square wave using switches and filters out high frequencies to give a DC output that is actually in proportional to the measured values. A field programmable gate array is employed to generate direct digital synthesis based sinusoidal excitation signal to generate reference signals required for demodulation and to measure the output of lock-in detection. The phase shift between the excitation signal and reference signals is controlled accurately in digital domain. Thus, due to the phase mismatch, errors are properly reduced. Also, analog phase shifter and analog switch-driving circuits are no longer required. Three different lossy capacitors realized using discrete components are simulated and tested. The maximum relative error is 1.62 % for the resistance measurement and 6.38 % for the capacitance measurement.

This study aims to optimize certain performance parameters of an analog front-end circuit for capacitance measurement. The front-end circuit produces an output voltage in proportional to the measured capacitance. This output voltage should be demodulated by means of digital or analog demodulators to extract the capacitance information. In order to obtain an accurate and precise capacitance measurement, the front-end circuit’s performance parameters must be optimized carefully. In this paper, a transimpedance amplifier consisting of an operational amplifier with a feedback resistor and a feedback capacitor is employed as the front-end topology. The transimpedance amplifier’s gain, settling time and outputreferred total noise are the performance criteria which form a multi-parameter optimization problem. These performance criteria primarily depend on the values of the feedback resistor, feedback capacitor and operation frequency. Grey Wolf optimizer Algorithm is used to find these values optimally. To verify the algorithm’s results, SPICE based simulations are carried out for two capacitance measurements.

Sleep electroencephalogram (EEG) signal is an important clinical tool for automatic sleep staging process. Sleep EEG signal is effected by artifacts and other biological signal sources, such as electrooculogram (EOG) and electromyogram (EMG), and since it is effected, its clinical utility reduces. Therefore, eliminating EOG artifacts from sleep EEG signal is a major challenge for automatic sleep staging. We have studied the effects of EOG signals on sleep EEG and tried to remove them from the EEG signals by using regression method. The EEG and EOG recordings of seven subjects were obtained from the Sleep Research Laboratory of Meram Medicine Faculty of Necmettin Erbakan University. A dataset consisting of 58 h and 6941 epochs was used in the research. Then, in order to see the consequences of this process, we classified pure sleep EEG and artifact-eliminated EEG signals with artificial neural networks (ANN). The results showed that elimination of EOG artifacts raised the classification accuracy on each subject at a range of 1%– 1.5%. However, this increase was obtained for a single parameter. This can be regarded as an important improvement if the whole system is considered. However, different artifact elimination strategies combined with different classification methods for another sleep EEG artifact may give higher accuracy differences between original and purified signals.

In recent years, the binocular stereo vision has become more popular in many different areas because of the latest developments in three-dimensional (3-D) image processing technology that ensures rich information in comparison with other sensor types. This study presents a novel method based on the binocular stereo vision system to reduce the measurement error encountered frequently in the orthognathic surgery. The main aim is to enhance the level of the accuracy of this sensitive operation. The developed system is not only useful for the preoperative assessment or the postoperative process but also can be utilized during the real-time operation. Additionally, this system provides a broader working field, more practical and healthier environment and less expensive setup. Therefore, the developed binocular stereo vision system may be acceptable for most surgeons. Experimental results show that the average error rate for all of X, Y and Z coordinates in the Cartesian system is 0.25 mm which is clinically acceptable (< 1.00 mm). The binocular stereo vision system would be a helpful throughout the orthognathic surgery to improve precision of the measurement and satisfy the healthy surgical operating environment

Stereo vision systems are utilized since it provides contactless measurements of objects in 3-D (threedimensional). Orthognathic surgery is a very sensitive operation that requires very high accuracy in measurements. The reduction of measurement error is an essential problem in orthognathic surgery. Moreover, quality inspection of the process during the course of operation aids the surgeon to avoid or minimize the mitigating circumstances. In this paper, artificial intelligence methods (neural network and neuro-fuzzy system) are used in order to increase the accuracy of positioning of jaws during the real-time practice. The comparison of artificial measurements with the real measurements shows that a statistically acceptable accuracy is achieved in 3-D positioning of teeth. (C) 2016 Elsevier B.V. All rights reserved.

The successful orthognathic surgery is directly influenced by more accurate measurement of distance or size of the surgery area. There exist various methods of measurements during the orthognathic surgery. One of the newest methods is to make measurements by using stereo vision system with stereo cameras. The result of stereo vision is affected by many factors, such as captured image colour, glossiness, ambient light, geometry, etc. One of the most important influence factors is ambient light, especially on measuring distances with the value of microns (10-6 m). The stereo vision system's cameras are also influenced by ambient light and the objective of this paper is to investigate more accurate result of stereo vision according to the ambient light of the orthognathic surgery operation room.

In this study, we have developed a nonlinear recognition system in the artificial immune systems (AIS) field named 'AaA-response (artificial neural network (ANN)-aided AIS-response)', which is different from previous AIS methods in that it uses a different modeling strategy in the formation of the memory response. Because it also uses ANNs in the determination of the correct output, it can be seen as a hybrid system that involves AIS and ANN approaches. Unlike the other AIS methods, AaA-response uses multiple system units (or antibodies) to form an output for a presented input. This property gives the proposed system the ability of producing the desired output values, other than just being a classification algorithm. That is, AaA-response can also be used as a regression method, like ANNs, by producing any output value for the given inputs. The parameter analyses of the system were conducted on an artificially generated dataset, the Chainlink dataset, and the important points in the parameter selection were emphasized. To investigate the performance of the system for real-world problems, the Iris dataset and Statlog Heart Disease dataset, taken from the University of California Irvine machine learning repository, were used. The system, which obtained 99.33% classification accuracy on the Iris dataset, has shown an important performance superiority with regard to the classification accuracy to other methods in the literature by reaching 90.37% classification accuracy for the Statlog Heart Disease dataset.

Sleep staging has an important role in diagnosing sleep disorders. It is usually done by a sleep expert through examining sleep Electroencephalogram (EEG), Electrooculogram (EOG), Electromyogram (EMG) signals of the patients and determining the stages of sleep in different time sections named as epochs. Manual sleep staging is preferred among the sleep experts but because it is rather tiring and time consuming task, automatic sleep stage scoring systems get popularity. In this study, we obtained EEG, EMG and EOG signals of four healthy people at sleep laboratory of Meram Medicine Faculty of Necmettin Erbakan University to use them in sleep staging and extracted 20 different features by using some power spectral density estimation methods which are: Fast Fourier Transform (FFT), Welch and Autoregressive (AR). We evaluated the effects of these methods on sleep staging through using ANN classifier. Comparison between these methods was done on each individual whose data were utilized separately from others. According to the results, the maximum test classification accuracy was reported as 79.72% by using of FFT method for subject1. Also, mean of test classification accuracies for all of subjects were obtained as 74.14%, 71,58 and 70.34% with use of FFT, Welch and AR, respectively.

Automatic Sleep Staging is an active field of research in sleep staging community. Many methods have been applied to get rid of the cumbersome of manual staging process. Even though very effective results were taken in some of these methods with respect to the classification accuracy, they are restricted either with their limited classification data or with lower number of classified stages like wake, sleepy and deep sleep. The accuracies obtained with methods for the classification of whole sleep stages are very low to apply in real sleep staging. One reason for this is the class imbalance in training data. Approximately half of one-night sleep consists of Non-REM2 stage while Wake, Non-REM1 and Non-REM3 stages are comparatively short duration. So, the used systems can converge to the characteristics of Non-REM2 stage. Taking equal amounts of data from each stage in training can be a solution for this but in this time a question arises: which samples should be picked from the each stage. Clustering schemes can play their roles for this question. In this study, we realized this clustering process with two methods: Fuzzy C-means Clustering (FCM) and Artificial Immune Clustering (AIC). We used 55 features that extracted from the sleep EEG, EOG and EMG signals of 8 subjects. We took a total of 300 data from each stage using FCM and AIC and classified these data with Artificial Neural Networks. The performances of the used clustering methods were compared on different number of features for which PCA was applied. The results showed that AIC was over-performed to FCM by obtaining a classification accuracy of 80.62% while this accuracy was 72.16% with FCM method used.

Sleep spindle is the one of important components determining N-REM (Non-Rapid Eye Movement) stage 2 in the sleep stages. The symptoms of N-REM stage 2 are sleep spindle and K-complex and here sleep spindles are automatically recognized by using time and frequency domain features belonging to EEG (Electroencephalograph) signals obtained from three patient subjects. In this study, the proposed method consists of two steps. In the first step, six time domain features have been extracted from raw EEG signals. As for the extraction of frequency domain features from raw EEG signals, Welch spectral analysis has been used and applied to raw EEG signals. By this way, 65 frequency domain features have been extracted and reduced from 65 to 4 features by using statistical measures including minimum, maximum, standard deviation, and mean values. Three feature sets including only time domain, only frequency domain, and both time and frequency domain features have been used and the numbers of these feature sets are 6, 4, and 10, respectively. In the second step, artificial neural network (ANN) with LM (Levenberg–Marquardt) has been used to classify the sleep spindles evaluated beforehand by sleep expert physicians. The obtained classification accuracies for three features sets in the classification of sleep spindles are 100%, 56.86%, and 93.84% by using LM-ANN (for ten node in hidden layer). The obtained results have presented that the proposed recognition system could be confidently used in the automatic classification of sleep spindles.

Rheumatoid arthritis (RA) is a multi-systemic autoimmune disease that leads to substantial morbidity and mortality. In this paper, as spectral analysis methods of Multiple Signal Classification (MUSIC) method is used in order to extract the significant features from the right and left hand Ulnar artery Doppler signals for the diagnosis of RA disease. The MUSIC method has been used as subspace method. To extract features from Doppler signals obtained from the right and left hand Ulnar arterial the MUSIC method model degrees of 5, 10, 15, 20, and 25 were used. Then, an adaptive network based fuzzy inference system (ANFIS) was applied to features extracted from the right and left hand Ulnar artery Doppler signals for classifying RA disease. The methods are not new, but the study has a novelty in that the application area of these methods is new. In the hybrid model, the combination of MUSIC and ANFIS yielded classification accuracies of 95% (for a model degree of 20) using the right hand Ulnar artery and classification accuracies of 91.25% (for a model degree of 10) using left hand Ulnar artery Doppler signals in the diagnosis of RA disease. The proposed approach has potential to help with the early diagnosis of RA disease for the specialists who study this subject.

Rheumatoid arthritis (RA) is a multi-systemic autoimmune disease that leads to substantial morbidity and mortality. In this paper, as spectral analysis methods of Multiple Signal Classification (MUSIC) method is used in order to extract the significant features from the right and left hand Ulnar artery Doppler signals for the diagnosis of RA disease. The MUSIC method has been used as subspace method. To extract features from Doppler signals obtained from the right and left hand Ulnar arterial the MUSIC method model degrees of 5, 10, 15, 20, and 25 were used. Then, an adaptive network based fuzzy inference system (ANFIS) was applied to features extracted from the right and left hand Ulnar artery Doppler signals for classifying RA disease. The methods are not new, but the study has a novelty in that the application area of these methods is new. In the hybrid model, the combination of MUSIC and ANFIS yielded classification accuracies of 95% (for a model degree of 20) using the right hand Ulnar artery and classification accuracies of 91.25% (for a model degree of 10) using left hand Ulnar artery Doppler signals in the diagnosis of RA disease. The proposed approach has potential to help with the early diagnosis of RA disease for the specialists who study this subject.

The cardiac, end-systolic and end-diastolic diameters values are very important m-mode cardiac parameters for infant, children, and adolescents, due to growing up body. These parameters, belonging to heart, must be known in order to make a decision about the subject. The expert decision occurs after comparing measured value to hard-copied charts. Hard-copied charts were prepared previously as a result of long statistical studies and these charts depend on a certain region. Our proposed method presents a valid virtual chart for the experts. The proposed method comprises of two stages: (i) data normalization based on euclidean distance (ii) normalized cardiac parameters predicting using adaptive neural fuzzy system. In order to present performance of the proposed method, mean absolute error, absolute deviation and two-fold cross-validation were used. In addition to performance criteria, different common normalization methods, z-score, decimal scaling and minimum-maximum normalization methods were used to compare. In this study, the aim is to create a valid virtual chart which helps the expert during making the decision about predicting end-systolic and end-diastolic cardiac m-mode values. The results were compared with real cardiac parameters by expert with 10years of medical experience.

Sleep scoring is one of the most important methods for diagnosis in psychiatry and neurology. Sleep staging is a time consuming and difficult task conducted by sleep specialists. The purposes of this work are to automatic score the sleep stages and to help to sleep physicians on sleep stage scoring. In this work, a novel data preprocessing method called k-means clustering based feature weighting (KMCFW) has been proposed and combined with k-NN (k-nearest neighbor) and decision tree classifiers to classify the EEG (electroencephalogram) sleep into six sleep stages including awake, N-REM (non-rapid eye movement) stage 1, N-REM stage 2, N-REM stage 3, REM, and non-sleep (movement time). First of all, frequency domain features belonging to sleep EEG signal have been extracted using Welch spectral analysis method and composed 129 features from EEG signal relating each sleep stages. In order to decrease the features, the statistical features comprising minimum value, maximum value, standard deviation, and mean value have been used and then reduced from 129 to 4 features. In the second phase, the sleep stages dataset with four features has been weighted by means of k-means clustering based feature weighting. Finally, the weighted sleep stages have been automatically classified into six sleep stages using k-NN and C4.5 decision tree classifier. In the classification of sleep stages, the k values of 10, 20, 30, 40, 50, and 60 in k-NN classifier have been used and compared with each other. In the experimental results, while sleep stages has been classified with 55.88% success rate using k-NN classifier (for k value of 40), the weighted sleep stages with KMCFW has been recognized with 82.15% success rate k-NN classifier (for k value of 40). And also, we have investigated the relevance between sleep stages and frequency domain features belonging to EEG signal. These results have demonstrated that proposed weighting method have a considerable impact on automatic determining of sleep stages. This system could be used as an online system in the automatic scoring of sleep stages and helps to sleep physicians in the sleep scoring process.

An increasing number of algorithms and applications have coming into scene in the field of artificial immune systems (AIS) day by day. Whereas this increase is bringing successful studies, still, AIS is not an effective problem solver in some problem fields such as classification, regression, pattern recognition, etc. So far, many of the developed AIS algorithms have used a distance or similarity measure as the case in instance based learning (IBL) algorithms. The efficiency of IBL algorithms lies mainly in the weighting scheme they used. This weighting idea was taken as the objective of our study in that we used genetic algorithms to determine the weights of attributes and then used these weights in our previously developed Artificial Immune System (AWAIS). We evaluated the performance of new configuration (GA-AWAIS) on two medical datasets which were Statlog Heart Disease and BUPA Liver Disorders dataset. We also compared it with AWAIS for those problems. The obtained classification accuracy was very good with respect to both AWAIS and other common classifiers in literature.

Generally, many classifier systems compel in the classification of multi-class problems. The aim of this study is to improve the classification accuracy in the case of multi-class classification problems. In this study, we have proposed a novel hybrid classification system based on C4.5 decision tree classifier and one-against-all approach to classify the multi-class problems including dermatology, image segmentation, and lymphography datasets taken from UCI (University of California Irvine) machine learning database. To test the proposed method, we have used the classification accuracy, sensitivity-specificity analysis, and 10-fold cross validation. In this work, firstly C4.5 decision tree has been run for all the classes of dataset used and achieved 84.48%, 88.79%, and 80.11% classification accuracies for dermatology, image segmentation, and lymphography datasets using 10-fold cross validation, respectively. The proposed method based on C4.5 decision tree classifier and one-against-all approach obtained 96.71%, 95.18%, and 87.95% for above datasets, respectively. These results show that the proposed method has produced very promising results in the classification of multi-class problems. This method can be used in many pattern recognition applications. In future, instead of C4.5 decision tree, other classification algorithms such as Bayesian learning, artificial immune system algorithms, artificial neural networks can be used.

Due to the fact that there exist only a small number of complex systems in artificial immune systems (AISs) that solve nonlinear problems, there is a need to develop nonlinear AIS approaches that would be among the well-known solution methods. In this study, we developed a kernel-based AIS to compensate for this deficiency by providing a nonlinear structure via transformation of distance calculations in the clonal selection models of classical AIS to kernel space. Applications of the developed system were conducted on Statlog heart disease dataset, which was taken from the University of California, Irvine Machine-Learning Repository, and on Doppler sonograms to diagnose atherosclerosis disease. The system obtained a classification accuracy of 85.93% for the Statlog heart disease dataset, while it achieved a 99.09% classification success for the Doppler dataset. With these results, our system seems to be a potential solution method, and it may be considered as a suitable method for hard nonlinear classification problems.

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.

In this paper, we have made medical application of a new artificial immune system named the information gain-based artificial immune recognition system (IG-AIRS) which is minimized the negative effects of taking into account all attributes in calculating Euclidean distance in shape-space representation which is used in many artificial immune systems. For medical data, microorganism dataset was applied in the performance analysis of our proposed system. Microorganism dataset was obtained using Cyranose 320 electronic nose. Our proposed system reached 92.35% classification accuracy with five-fold cross validation method. This result ensured that IG-AIRS would be helpful in classification of microorganism species based on laboratory tests, and would open the way to various microorganism species determine support by using electronic nose.