Etem Koklukaya

Wavelet neural network classification of EEG signals by using AR model with MLE preprocessing

Since EEG is one of the most important sources of information in therapy of epilepsy, several researchers tried to address the issue of decision support for such a data. In this paper, we introduce two fundamentally different approaches for designing classification models (classifiers); the traditional statistical method based on logistic regression and the emerging computationally powerful techniques based on artificial neural networks (ANNs). Logistic regression as well as feedforward error backpropagation artificial neural networks (FEBANN) and wavelet neural networks (WNN) based classifiers were developed and compared in relation to their accuracy in classification of EEG signals. In these methods we used FFT and autoregressive (AR) model by using maximum likelihood estimation (MLE) of EEG signals as an input to classification system with two discrete outputs: epileptic seizure or nonepileptic seizure. By identifying features in the signal we want to provide an automatic system that will support a physician in the diagnosing process. By applying AR with MLE in connection with WNN, we obtained novel and reliable classifier architecture. The network is constructed by the error backpropagation neural network using Morlet mother wavelet basic function as node activation function. The comparisons between the developed classifiers were primarily based on analysis of the receiver operating characteristic (ROC) curves as well as a number of scalar performance measures pertaining to the classification. The WNN-based classifier outperformed the FEBANN and logistic regression based counterpart. Within the same group, the WNN-based classifier was more accurate than the FEBANN-based classifier, and the logistic regression-based classifier.

Automatic seizure detection in EEG using logistic regression and artificial neural network

The detection of epileptiform discharges in the EEG is an important component in the diagnosis of epilepsy. In this study, multiple signal classification (MUSIC), autoregressive (AR) and periodogram methods were used to get power spectra in patients with absence seizure. The EEG power spectra were used as an input to a classifier. We introduce two fundamentally different approaches for designing classification models (classifiers); the traditional statistical method based on logistic regression (LR) and the emerging computationally powerful techniques based on artificial neural networks (ANNs). LR as well as multilayer perceptron neural network (MLPNN) based classifiers were developed and compared in relation to their accuracy in classification of EEG signals. The comparisons between the developed classifiers were primarily based on analysis of the receiver operating characteristic (ROC) curves as well as a number of scalar performance measures pertaining to the classification. The MLPNN based classifier outperformed the LR based counterpart. Within the same group, the MLPNN-based classifier was more accurate than the LR-based classifier.

Weak signal propagation through noisy feedforward neuronal networks.

We determine under which conditions the propagation of weak periodic signals through a feedforward Hodgkin–Huxley neuronal network is optimal. We find that successive neuronal layers are able to amplify weak signals introduced to the neurons forming the first layer only above a certain intensity of intrinsic noise. Furthermore, we show that as low as 4% of all possible interlayer links are sufficient for an optimal propagation of weak signals to great depths of the feedforward neuronal network, provided the signal frequency and the intensity of intrinsic noise are appropriately adjusted.

Efficient solution for frequency band decomposition problem using wavelet packet in HRV

Heart rate variability (HRV) is a very significant noninvasive tool for assessment of sympathovagal balance (SB) that reflects variation of parasympathetic and sympathetic activities in autonomic nervous system (ANS). Low frequency/high frequency (LF/HF) power ratio provides information about these activities. Because of nonstationary characteristic of HRV, analyses based on wavelet transform were typically preferred in previous studies. There is an important problem that required frequency ranges for LF and HF cannot be obtained using discrete wavelet transform (DWT). Different sampling frequencies do not remove this problem. In this study, a solution based on wavelet packet (WP) is presented for removing this problem. In addition, effect of WP on SB values is investigated. Method was applied to spontaneous ventricular tachyarrhythmia database and variation of energy values and LF/HF energy ratios were compared for DWT and WP. WP provides absolutely excellent approximation to required frequency bands and exposes different and impressive SB results.

Determination of sympathovagal balance in ventricular tachiarrythmia patients with implanted cardioverter defibrillators using wavelet transform and MLPNN

HRV is a nonstationary signal that includes sympathovagal balance (SB) information related to LF/HF ratio between the sympathetic and parasympathetic nervous systems. In this paper, a solution based on Daubechies wavelet transform (dbN) and multilayer perceptron neural network (MLPNN) has been presented for the determination of SB. HRV database obtained MIT-BIH arrhythmia database consisting of pairs of RR interval time series, recorded by implanted cardioverter defibrillators in 78 subjects. RMS values of approximation and detail components (Arms and Drms) obtained from dbN wavelet transform of HRV signals have been used as training data for MLPNN. Trains were realized in 5 different dbN with only Arms components, only Drms components and both of them and results were compared. Train accuracy and test accuracy results have been reached very successful percentage values that might be valuable for clinical applications.

Investigation of the relationship between anxiety and heart rate variability in fibromyalgia

BACKGROUND Fibromyalgia syndrome (FMS) is identified by widespread musculoskeletal pain, sleep disturbance, nonrestorative sleep, fatigue, morning stiffness and anxiety. Anxiety is very common in Fibromyalgia and generally leads to a misdiagnosis. Self-rated Beck Anxiety Inventory (BAI) and doctor-rated Hamilton Anxiety Inventory (HAM-A) are frequently used by specialists to determine anxiety that accompanies fibromyalgia. However, these semi-quantitative anxiety tests are still subjective as the tests are scored using doctor-rated or self-rated scales. METHOD In this study, we investigated the relationship between heart rate variability (HRV) frequency subbands and anxiety tests. The study was conducted with 56 FMS patients and 34 healthy controls. BAI and HAM-A test scores were determined for each participant. ECG signals were then recruited and 71 HRV subbands were obtained from these ECG signals using Wavelet Packet Transform (WPT). The subbands and anxiety tests scores were analyzed and compared using multilayer perceptron neural networks (MLPNN). RESULTS The results show that a HRV high frequency (HF) subband in the range of 0.15235Hz to 0.40235Hz, is correlated with BAI scores and another HRV HF subband, frequency range of 0.15235Hz to 0.28907Hz is correlated with HAM-A scores. The overall accuracy is 91.11% for HAM-A and 90% for BAI with MLPNN analysis. CONCLUSION Doctor-rated or self-rated anxiety tests should be supported with quantitative and more objective methods. Our results show that the HRV parameters will be able to support the anxiety tests in the clinical evaluation of fibromyalgia. In other words, HRV parameters can potentially be used as an auxiliary diagnostic method in conjunction with anxiety tests.

Determination of a New VLF Band in HRV for Ventricular Tachyarrhythmia Patients

This study presents a new very low frequency (VLF) band range in ventricular tachyarrhythmia patients and involves an approach for estimation of effect of VLF band on ventricular tachyarrhythmia patients. A model based on wavelet packets (WP) and multilayer perceptron neural network (MLPNN) is used for determination of effective VLF band in heart rate variability (HRV) signals. HRV is decomposed into sub-bands including very low frequency parts and variations of energy are analyzed. Domination test is done using MLPNN and dominant band is determined. As a result, a new VLF band was described in 0.0039063–0.03125 Hz frequency range. This method can be used for other bands or other arrhythmia patients. Especially, estimation of dominant band energy using this method can be helped to diagnose for applications where have important effect of characteristic band.

Measuring the Sympathetic Skin Response on Body and Using as Diagnosis-Purposed for Lung Cancer Patients by Artificial Neural Networks

In this study, the points of Sympathetic skin response that can be measured from different zones on body of healthy and patient subjects are determined. The Sympathetic skin responses on these points are obtained using a measurement device that is called Grass Model 7 Polygraph 1. The database is formed in Cerrahpaşa University, Faculty of Medicine and data is taken from healthy and patient subjects who are volunteer. Some parameters of the subjects which are more effective on SSR such as height, weight, age must be chosen between the specific limits to obtain results more clearly. The symmetric points on human body are chosen for the measurement. After that, the Sympathetic skin response values which are measured from a human body are simulated and tested by using artificial neural network toolbox on Matlab software. The structure of the chosen neural network is a multilayer feedforward neural network. According to simulation results, the application method as diagnosis-purposed of the lung cancer patients is explained by using the differences related to these values on the skin.

Physiological parameters as a tool in the diagnosis of fibromyalgia syndrome in females: A preliminary study.

AIMS Although fibromyalgia (FM) syndrome is associated with many symptoms, there is as yet no specific finding or laboratory test diagnostic of this syndrome. The physical examination and laboratory tests may be helpful in figuring out this syndrome. MATERIALS AND METHODS The heart rate, respiration rate, body temperature (TEMP), height, body weight, hemoglobin level, erythrocyte sedimentation rate, white blood cell count, platelet count (PLT), rheumatoid factor and C-reactive protein levels and electrocardiograms (ECG) of FM patients were compared with those of control individuals. In addition, the predictive value of these tests was evaluated via receiver operating characteristic (ROC) analysis. KEY FINDINGS The results showed that the TEMP and the PLT were higher in the FM group compared with the control group. Also, ST heights in ECGs which corresponds to a period of ventricle systolic depolarization, showed evidence of a difference between the FM and the control groups. There was no difference observed in terms of the other parameters. According to the ROC analysis, PLT, TEMP and ST height have predictive capacities in FM. SIGNIFICANCE Changes in hormonal factors, peripheral blood circulation, autonomous system activity disorders, inflammatory incidents, etc., may explain the increased TEMP in the FM patients. The high PLT level may signify a thromboproliferation or a possible compensation caused by a PLT functional disorder. ST depression in FM patients may interrelate with coronary pathology. Elucidating the pathophysiology underlying the increases in TEMP and PLT and the decreases in ST height may help to explain the etiology of FM.

An event driven slot allocation approach to TDMA based WSN MAC design and its effect on latency

Latency is a crucial design parameter for event driven WSN applications since as soon as a triggering event occurs, it is crucial to report it to the sink or cluster head immediately. Traditional TDMA based MAC protocols are not well appropriate for this kind of applications. In this paper, we introduce a new slot allocation approach which can be used in any TDMA based MAC design. It is shown that latency is reduced as a consequence of employing this approach compared to the conventional E-TDMA and BMA protocols.