Evaluation of Formalin-fixed Paraffin-embedded Tissue Samples Diagnosed by Histopathology as in Çorum.

Objective: This study aimed to detect the presence of spp. in formalin-fixed paraffin-embedded (FFPG) samples of hydatid cyst cases and to discuss the DNA isolation problems in FFPG samples.

Methods: FFPG samples of 47 cases diagnosed with hydatid cyst were included in this study.

The Usefulness of Admission Plasma NT-pro BNP Level to Predict Left Ventricular Aneurysm Formation after Acute ST-Segment Elevation Myocardial Infarction.

Background: Left ventricular aneurysm (LVA) is an important complication of acute myocardial infarction. In this study, we investigated the role of N- Terminal pro B type natriuretic peptide level to predict the LVA development after acute ST-segment elevation myocardial infarction (STEMI).

Methods: We prospectively enrolled 1519 consecutive patients with STEMI.

Habituation, sensitization, and Pavlovian conditioning.

In this brief review, I argue that the impact of a stimulus on behavioral control increase as the distance of the stimulus to the body decreases. Habituation, i.e.

Differentiation of two subtypes of adult hydrocephalus by mixture of experts.

This paper illustrates the use of mixture of experts (ME) network structure to guide model selection for diagnosis of two subtypes of adult hydrocephalus (normal-pressure hydrocephalus-NPH and aqueductal stenosis-AS). The ME is a modular neural network architecture for supervised learning. Expectation-Maximization (EM) algorithm was used for training the ME so that the learning process is decoupled in a manner that fits well with the modular structure.

Automatic detection of erythemato-squamous diseases using k-means clustering.

A new approach based on the implementation of k-means clustering is presented for automated detection of erythemato-squamous diseases. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. The studied domain contained records of patients with known diagnosis.

Adaptive neuro-fuzzy inference systems for automatic detection of breast cancer.

This paper intends to an integrated view of implementing adaptive neuro-fuzzy inference system (ANFIS) for breast cancer detection. The Wisconsin breast cancer database contained records of patients with known diagnosis. The ANFIS classifiers learned how to differentiate a new case in the domain by given a training set of such records.

Modified mixture of experts for diabetes diagnosis.

Diagnosis tasks are among the most interesting activities in which to implement intelligent systems. The major objective of the paper is to be a guide for the readers, who want to develop an automated decision support system for detection of diabetics and subjects having risk factors of diabetes. The purpose was to determine an optimum classification scheme with high diagnostic accuracy for this problem.

Statistics over features: EEG signals analysis.

This paper presented the usage of statistics over the set of the features representing the electroencephalogram (EEG) signals. Since classification is more accurate when the pattern is simplified through representation by important features, feature extraction and selection play an important role in classifying systems such as neural networks. Multilayer perceptron neural network (MLPNN) architectures were formulated and used as basis for detection of electroencephalographic changes.

Medical informatics: a model developed for diabetes education via telemedicine.

Fast developments in information and communication technology (ICT) have made it possible to develop new services for people. One of the most interesting areas is health care. Medical informatics is the discipline concerned with the systematic processing of data, information and knowledge in medicine and health care.

Eigenvector methods for automated detection of electrocardiographic changes in partial epileptic patients.

In this paper, the automated diagnostic systems trained on diverse and composite features were presented for detection of electrocardiographic changes in partial epileptic patients. In practical applications of pattern recognition, there are often diverse features extracted from raw data that require recognizing. Methods of combining multiple classifiers with diverse features are viewed as a general problem in various application areas of pattern recognition.

Analysis of spike-wave discharges in rats using discrete wavelet transform.

A feature is a distinctive or characteristic measurement, transform, structural component extracted from a segment of a pattern. Features are used to represent patterns with the goal of minimizing the loss of important information. The discrete wavelet transform (DWT) as a feature extraction method was used in representing the spike-wave discharges (SWDs) records of Wistar Albino Glaxo/Rijswijk (WAG/Rij) rats.

Adaptive neuro-fuzzy inference system for classification of ECG signals using Lyapunov exponents.

This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for classification of electrocardiogram (ECG) signals. Decision making was performed in two stages: feature extraction by computation of Lyapunov exponents and classification by the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, and atrial fibrillation beat) obtained from the PhysioBank database were classified by four ANFIS classifiers.

Measuring saliency of features using signal-to-noise ratios for detection of electrocardiographic changes in partial epileptic patients.

Medical diagnostic accuracies can be improved when the pattern is simplified through representation by important features. The feature vector, which is comprised of the set of all features used to describe a pattern, is a reduced-dimensional representation of that pattern. By identifying a set of salient features, the noise in a classification model can be reduced, resulting in more accurate classification.

Implementing eigenvector methods/probabilistic neural networks for analysis of EEG signals.

A new approach based on the implementation of probabilistic neural network (PNN) is presented for classification of electroencephalogram (EEG) signals. In practical applications of pattern recognition, there are often diverse features extracted from raw data which needs recognizing. Because of the importance of making the right decision, the present work is carried out for searching better classification procedures for the EEG signals.

Diverse and composite features for ECG signals processing.

The automated diagnostic systems employing diverse and composite features for electrocardiogram (ECG) signals were analyzed and their accuracies were determined. Because of the importance of making the right decision, classification procedures classifying the ECG signals with high accuracy were investigated. The classification accuracies of multilayer perceptron neural network (MLPNN), recurrent neural network (RNN), and mixture of experts (ME) trained on composite features and modified mixture of experts (MME) trained on diverse features were compared.

Usage of eigenvector methods to improve reliable classifier for Doppler ultrasound signals.

A new approach based on the implementation of the automated diagnostic systems for Doppler ultrasound signals classification with the features extracted by eigenvector methods is presented. In practical applications of pattern recognition, there are often diverse features extracted from raw data which needs recognizing. Because of the importance of making the right decision, the present work is carried out for searching better classification procedures for the Doppler ultrasound signals.

Recurrent neural networks with composite features for detection of electrocardiographic changes in partial epileptic patients.

The aim of this study is to evaluate the diagnostic accuracy of the recurrent neural networks (RNNs) with composite features (wavelet coefficients and Lyapunov exponents) on the electrocardiogram (ECG) signals. Two types of ECG beats (normal and partial epilepsy) were obtained from the MIT-BIH database. The multilayer perceptron neural networks (MLPNNs) were also tested and benchmarked for their performance on the classification of the ECG signals.

Statistics over features for internal carotid arterial disorders detection.

The objective of the present study is to extract the representative features of the internal carotid arterial (ICA) Doppler ultrasound signals and to present the accurate classification model. This paper presented the usage of statistics over the set of the extracted features (Lyapunov exponents and the power levels of the power spectral density estimates obtained by the eigenvector methods) in order to reduce the dimensionality of the extracted feature vectors. Since classification is more accurate when the pattern is simplified through representation by important features, feature extraction and selection play an important role in classifying systems such as neural networks.

Fuzzy similarity index for discrimination of EEG signals.

In this study, a new approach based on the computation of fuzzy similarity index was presented for discrimination of electroencephalogram (EEG) signals. The EEG, a highly complex signal, is one of the most common sources of information used to study brain function and neurological disorders. The analyzed EEG signals were consisted of five sets (set A-healthy volunteer, eyes open; set B-healthy volunteer, eyes closed; set C-seizure-free intervals of five patients from hippocampal formation of opposite hemisphere; set D-seizure-free intervals of five patients from epileptogenic zone; set E-epileptic seizure segments).

Probabilistic neural networks employing Lyapunov exponents for analysis of Doppler ultrasound signals.

The implementation of probabilistic neural networks (PNNs) with the Lyapunov exponents for Doppler ultrasound signals classification is presented. This study is directly based on the consideration that Doppler ultrasound signals are chaotic signals. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents.

Analysis of EEG signals by combining eigenvector methods and multiclass support vector machines.

A new approach based on the implementation of multiclass support vector machine (SVM) with the error correcting output codes (ECOC) is presented for classification of electroencephalogram (EEG) signals. In practical applications of pattern recognition, there are often diverse features extracted from raw data which needs recognizing. Decision making was performed in two stages: feature extraction by eigenvector methods and classification using the classifiers trained on the extracted features.

Detection of arterial disorders by spectral analysis techniques.

This paper intends to an integrated view of the spectral analysis techniques in the detection of arterial disorders. The paper includes illustrative information about feature extraction from signals recorded from arteries. Short-time Fourier transform (STFT) and wavelet transform (WT) were used for spectral analysis of ophthalmic arterial (OA) Doppler signals.

Combining eigenvector methods and support vector machines for detecting variability of Doppler ultrasound signals.

In this paper, the multiclass support vector machines (SVMs) with the error correcting output codes (ECOC) were presented for detecting variabilities of the multiclass Doppler ultrasound signals. The ophthalmic arterial (OA) Doppler signals were recorded from healthy subjects, subjects suffering from OA stenosis, subjects suffering from ocular Behcet disease. The internal carotid arterial (ICA) Doppler signals were recorded from healthy subjects, subjects suffering from ICA stenosis, subjects suffering from ICA occlusion.

Combining neural network models for automated diagnostic systems.

This paper illustrates the use of combined neural network (CNN) models to guide model selection for diagnosis of internal carotid arterial (ICA) disorders. The ICA Doppler signals were decomposed into time-frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The first level networks were implemented for the diagnosis of ICA disorders using the statistical features as inputs.

Wavelet-based neural network analysis of internal carotid arterial Doppler signals.

In this study, internal carotid arterial Doppler signals recorded from 130 subjects, where 45 of them suffered from internal carotid artery stenosis, 44 of them suffered from internal carotid artery occlusion and the rest of them were healthy subjects, were classified using wavelet-based neural network. Wavelet-based neural network model, employing the multilayer perceptron, was used for analysis of the internal carotid arterial Doppler signals. Multi-layer perceptron neural network (MLPNN) trained with the Levenberg-Marquardt algorithm was used to detect stenosis and occlusion in internal carotid arteries.