Mehmet Kuntalp

Automatic detection of epileptiform events in EEG by a three-stage procedure based on artificial neural networks

This paper introduces a three-stage procedure based on artificial neural networks for the automatic detection of epileptiform events (EVs) in a multichannel electroencephalogram (EEG) signal. In the first stage, two discrete perceptrons fed by six features are used to classify EEG peaks into three subgroups: 1) definite epileptiform transients (ETs); 2) definite non-ETs; and 3) possible ETs and possible non-ETs. The pre-classification done in the first stage not only reduces the computation time but also increases the overall detection performance of the procedure. In the second stage, the peaks falling into the third group are aimed to be separated from each other by a nonlinear artificial neural network that would function as a postclassifier whose input is a vector of 41 consecutive sample values obtained from each peak. Different networks, i.e., a backpropagation multilayer perceptron and two radial basis function networks trained by a hybrid method and a support vector method, respectively, are constructed as the postclassifier and then compared in terms of their classification performances. In the third stage, multichannel information is integrated into the system for contributing to the process of identifying an EV by the electroencephalographers (EEGers). After the integration of multichannel information, the overall performance of the system is determined with respect to EVs. Visual evaluation, by two EEGers, of 19 channel EEG records of 10 epileptic patients showed that the best performance is obtained with a radial basis support vector machine providing an average sensitivity of 89.1%, an average selectivity of 85.9%, and a false detection rate (per hour) of 7.5.

A simple and low-cost Internet-based teleconsultation system that could effectively solve the health care access problems in underserved areas of developing countries.

In many developing countries including Turkey, telemedicine systems are not in wide use due to the high cost and complexity of the required technology. Lack of these systems however has serious implications on patients who live in rural areas. The objective of this paper is to present a simple and economically affordable alternative to the current systems that would allow experts to easily access the medical data of their remote patients over the Internet. The system is developed in client-server architecture with a user-friendly graphical interface and various services are implemented as dynamic web pages based on PHP. The other key features of the system are its powerful security features and platform independency. An academic prototype is implemented and presented to the evaluation of a group of physicians. The results reveal that the system could find acceptance from the medical community and it could be an effective means of providing quality health care in developing countries.

Heart rate normalization in the analysis of heart rate variability in congestive heart failure.

Abstract In this study, the effects of heart rate (HR) normalization in the analysis of the heart rate variability (HRV) were investigated to distinguish 29 patients with congestive heart failure from 54 healthy subjects in the control group. In the analysis performed, the best accuracy performances of optimal combination of standard short-term HRV measures and of HR-normalized short-term HRV measures are compared. A genetic algorithm is used to select the best features from among all possible combinations of these measures. A k-nearest-neighbour (KNN) classifier is used to evaluate the performances of the feature combinations in classifying these two data groups. The results imply that using both min—max and HR normalization improves the performance of the classification. The maximum accuracy is achieved as 93.98 per cent using k = 3 and k = 5 for the KNN classifier with the perfect positive predictivity values.

Arrhythmia classification using higher order statistics

In this work, the features are extracted for the arrhythmia classification from the electrocardiograph (ECG) signals by using Higher order statistics. K-nearest neighborhood algorithm is used as classifier. Cumulants are calculated from the raw signals obtained from consecutive sample values of each R peak in ECG signals and used as features. In addition to these features, different features obtained from the relations of cumulants are also used. Simulation results shows that features obtained from the relations among cumulants are more discriminative than the cumulants.

Development of a robotic-arm controller by using hand gesture recognition

This paper deals with the robotic arm controller using image processing in the field of Human-Machine Interaction (HMI). There are two different methods used to analyze to control the robotic arm, the main aim of them is getting the hand gesture information without using tool that helps the system to extract data easier (ex. glove or wrist band). After segmentation of the hand, the first method is comparing of all pre-stored data in the database at the Template Matching Algorithm, the second method is Signature Signal, distance signal between edge of the hand and center of hand, Signature Signal is used to find where the fingertips are and to count the number of them. Both methods have enough calculation speed to be used in continuous frame capturing sequence.

Classification of arrhythmias according to the energy spectral density features by using Kernel density estimation

In this work, the classification performances of different features of arrhythmias that are observed in electrocardiograph (ECG) records is analyzed by using Kernel density estimation. Energy spectral density features have been used in different ratios Then principal component analysis and sequential feature selection have been applied and cross-validation has been accomplished for these features.

Topographic analysis of morphologic features of ECG beats

In this work, the arrhythmias in the electrocardiograph (ECG) signals are analyzed by using Self Organizing Maps (SOM). Morphologic features obtained from consecutive sample values of each R peak are used for training the SOM networks. The maps are examined using U-matrix representation method. Consequently, the high dimensional data are examined in two dimensions. When the shapes of the distributions obtained by U-matrix representation are considered, it is realized that a simple linear classifier is not able to classify these patterns correctly.

Effect of the Input Window Size in Arrhythmia classification with Multilayer Perceptron Network Structures

In this work, the arrhythmias in the electrocardiograph (ECG) signals are analyzed by using multi-layer perceptron (MLP) network. For training MLP network back-propagation with adaptive learning rate method is used. Feature vectors obtained from consecutive sample values of each peak in different window sizes are normalized and used for training the networks. Performances of different classifiers are examined depending on the average value of sensitivity, specificity, selectivity and accuracy of the classifiers. The results show that for the proposed classifier the optimal feature vector is a 71-point vector with 35 before and 35 after the R peak point of the ECG.

Investigating the Effects of Wavelet Entropy in Heart Rate Variability Analysis for Diagnosing of Congestive Heart Failure

In this study, wavelet entropy, which is calculated from the wavelet transform coefficients obtained from heart rate variability data, is used to distinguish the control group from the patients with congestive heart failure. Wavelet entropies are obtained from 29 patients with congestive heart failure and 54 subjects in the control group. Standard heart rate variability measurements are also calculated for the whole dataset. Finally, linear discriminant analysis is used to evaluate the performance of measurements in classifying these two groups

Prediction of paroxysmal atrial fibrillation onset by using ECG

The advances in medical technologies increased the importance of a reliable arrhythmia predictor to predict the arrhythmia onset. The goal of this study is to predict paroxysmal atrial fibrillation (PAF) onset by using electrocardiograph (ECG) signals. 53 pairs of 30 minute ECG signals were used for this purpose. Complex correlation measures (CCM) were calculated for 5 minute ECG windows. It was observed that the standard deviation of CCM values obtained for records just before the PAF onset were significantly greater than the ones obtained away from PAF onset. Based on this observation, the records just before to PAF onset were correctly predicted with 72% success rate.