Yakup Kutlu

Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients

This paper describes feature extraction methods using higher order statistics (HOS) of wavelet packet decomposition (WPD) coefficients for the purpose of automatic heartbeat recognition. The method consists of three stages. First, the wavelet package coefficients (WPC) are calculated for each different type of ECG beat. Then, higher order statistics of WPC are derived. Finally, the obtained feature set is used as input to a classifier, which is based on k-NN algorithm. The MIT-BIH arrhythmia database is used to obtain the ECG records used in this study. All heartbeats in the arrhythmia database are grouped into five main heartbeat classes. The classification accuracy of the proposed system is measured by average sensitivity of 90%, average selectivity of 92% and average specificity of 98%. The results show that HOS of WPC as features are highly discriminative for the classification of different arrhythmic ECG beats.

A multi-stage automatic arrhythmia recognition and classification system

This paper describes an automatic classification system based on combination of diverse features for the purpose of automatic heartbeat recognition. The method consists of three stages. At the first stage, heartbeats are classified into 5 main groups defined by AAMI using optimal feature sets for each main group. At the second stage, main groups are classified into subgroups using optimal features for each subgroup. Then the third stage is added to the system for classifying beats that are labeled as unclassified beats in the first two classification stages. A diverse set of features including higher order statistics, morphological features, Fourier transform coefficients, and higher order statistics of the wavelet package coefficients are extracted for each different type of ECG beat. At the first stage, optimal features for main groups are determined by using a wrapper type feature selection algorithm. At the second stage, optimal features are similarly selected for discriminating each subgroup of the main groups. Then at the third stage, only raw data is used for classifying beats. In all stages, the classifiers are based on the k-nearest neighbor algorithm. ECG records used in this study are obtained from the MIT-BIH arrhythmia database. The classification accuracy of the proposed system is measured by sensitivity, selectivity, and specificity measures. The system is classified 16 heartbeat types. The measures of proposed system are 85.59%, 95.46%, and 99.56%, for average sensitivity, average selectivity, and average specificity, respectively.

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.

NEURAL NETWORK BASED CRYPTOGRAPHY

In this paper, neural network based cryptology is performed. The sys- tem consists of two stages. In the first stage, neural network-based pseudo-random numbers (NPRNGs) are generated and the results are tested for randomness us- ing National Institute of Standard Technology (NIST) randomness tests. In the second stage, a neural network-based cryptosystem is designed using NPRNGs. In this cryptosystem, data, which is encrypted by non-linear techniques, is subject to decryption attempts by means of two identical artificial neural networks (ANNs). With the first neural network, non-linear encryption is modeled using relation- building functionality. The encrypted data is decrypted with the second neural network using decision-making functionality.

A new approach to early diagnosis of congestive heart failure disease by using HilbertHuang transform

Congestive heart failure (CHF) is a degree of cardiac disease occurring as a result of the hearts inability to pump enough blood for the human body. In recent studies, coronary artery disease (CAD) is accepted as the most important cause of CHF. This study focuses on the diagnosis of both the CHF and the CAD. The Hilbert-Huang transform (HHT), which is effective on non-linear and non-stationary signals, is used to extract the features from R-R intervals obtained from the raw electrocardiogram data. The statistical features are extracted from instinct mode functions that are obtained applying the HHT to R-R intervals. Classification performance is examined with extracted statistical features using a multilayer perceptron neural network. The designed model classified the CHF, the CAD patients and a normal control group with rates of 97.83%, 93.79% and 100%, accuracy, specificity and sensitivity, respectively. Also, early diagnosis of the CHF was performed by interpretation of the CAD with a classification accuracy rate of 97.53%, specificity of 98.18% and sensitivity of 97.13%. As a result, a single system having the ability of both diagnosis and early diagnosis of CHF is performed by integrating the CAD diagnosis method to the CHF diagnosis method.

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.

ECG based Human identification using Logspace Grid Analysis of Second Order Difference Plot

In this study, Second Order Difference Plot (SODP) features are used for ECG based human identification. SODP is a method that allows to determine the features with the statistical analysis of the situations obtained from distributions and the distribution of each of successive points on an unstable and linear signals. ECG records of 90 individuals in Physionet ECG-Id database are used in the study. These records are divided into segments with logarithmic grid, number of points in each segment was examined. Extracted features are classified using 2 Fold Cross Validation with kNN classifier, speed and performance of identification system were investigated. As a result, ECG based Human identification using Logspace Grid Analysis of SODP was performed in a very short time with 91.52% success.

Classification of fish species with two dorsal fins using centroid-contour distance

Color, texture and shape are generally used features in order to recognise an object from an image. In this study centroid-contour distance method is used in order to classify fish species with two dorsal fins. Therefore, fish images with two dorsal fins were used from fish images database taken under specific conditions. Various image processing methods were applied on images in order to extract centroid-contour distances. These distances were used as features and Nearest Neighbour algorithm was used for classification. 15 species from 427 fish images were classified with 95% general accuracy achievement.

Orthogonal Extreme Learning Machine Based P300 Visual Event-Related BCI

Brain Computer Interface BCI is a type of human-computer relationship research that directly translates electrical activity of brain into commands that can rule equipment and create novel communication channel for muscular disabled patients. In this study, in order to overcome shortcoming of Singular Value Decomposition in Extreme Learning Machine, iteratively optimized neuron numbered QR Decomposition technique with different approaches are proposed. QR Decomposition Extreme Learning Machine technique based P300 event-related potential BCI application that achieves almost % 100 classification accuracy with milliseconds is presented. QR decomposition based ELM and novel feature extraction method named Multi Order Difference Plot MoDP techniques are milestones of proposed BCI system.