Süleyman Bilgin

Temperature and burn injury prediction of human skin exposed to microwaves: a model analysis

A one-dimensional multi-layer model is presented to characterize skin temperature rises and burn processes resulting from skin exposure to microwaves. Temperature variations and damage function analyses in the skin tissue exposed to microwaves were predicted depending on blood perfusion rate, thermal conductivity, power density, and exposure time. Thermal wave model was applied and the bio-heat transfer equation was solved using the finite difference time domain method. The thermal wave model of bio-heat transfer predicts a lower temperature rise than a model that uses Pennes’ equation. When approaching steady state, the solutions overlaps with that obtained using the Pennes’ equation. The results obtained may help to analyze the consequences of short-time high-power MW exposures in biological tissues.

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.

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.

Correlation of BAI psychological test scores with heart rate variability using wavelet packet transform and artificial neural networks in fibromyalgia syndrome

Fibromyalgia syndrome which is appeared in the form of common pain in women is a musculoskeletal disorder. Heart rate variability (HRV) is a signal as measured time between each successive QRS time obtained from ECG signal. HRV parameters are associated with autonomic nervous system in literature. FMS affects patients psychology. Consequently some psychological tests are applied to patients for evaluation of psychological effects. Beck Anxiety Inventory (BAI) Test being applied as writing and speaking is a test consisting of 21 questions. In the study, HRV signals obtained from FMS patients and control group are decomposed into wavelet packets using Wavelet Packet Transform (WPT) and frequency bands which related to autonomic nervous system are included to study. Wavelet Packets within these frequency bands are applied to inputs of multilayer perceptron artificial neural networks (MLPNN). BAI psychological test scores are included as target values for MLPNN and each input is trained and tested as relating to target values. According to obtained accuracy values, the packets within Very Low Frequency (VLF) band has minimum accuracy values, however the packets within Low Frequency + High Frequency (LF+HF) has the best accuracy values. This study is targeted on presenting a solution for clinical studies which are evaluated using psychological test scores by physiological support.

Naive Bayes classification of neurodegenerative diseases by using discrete wavelet transform

The main objective of this study is the detection and analysis of some neurodegenerative disorders. It is possible to determine the classification of Especially ALS (Amyotrophic Lateral Sclerosis), PD (Parkinsons Disease) and HD (Huntingtons Disease) with respect to analysis of gait signals. Records obtained from gait signals can be converted the statistical values using DWT (Discrete Wavelet Transform). And also neurodegenerative gait signals are compared to control signals recorded from healthy subjects in the study. Other studies have been carried out for 5 minutes recordings in the literature. The innovative aspect of this study is to obtain the results of the recording by taking for 1 minute. So, this approach proposes a short measurement time for classification of neurodegenerative diseases. As a result, the classification of neurodegenerative diseases is obtained from gait signals using DWT and Naive Bayes method in the study and the discrimination of ALS disease can be achieved by using this method.

Assesment of spasticity via patellar pendulum and muscular-skeletal modelling

Spasticity is a movement disorder developing after upper motor neuron lesion. It is the tonus increase associated with movement velocity of extremity muscles, and thus it affects the daily lives of patients. It is quite a common disease as it develops due to lesions in brain and spinal cord. This study defines the electrophysiological characteristics of Patellar T reflex and patellar pendulum which triggered by patellar T reflex. Spasticity changes the responses by utilizing movement features of knee joint during the reflex and pendulum. Muscular-Skeletal simulation is employed to validate and visualize spasticity condition by calculating muscular forces and activation levels compared to EMG data. Also the study attempts to develop a measurement method to be used in spasticity.

Image-processing based determination of kinesiologic parameters of spasticity patients

In this study, it is aimed to obtain Patella T-Reflex kinesiologic parameters on spasticity patients by using image processing methods and thereby extracting objective data for the evaluation of spasticity patients. For this reason, the movement of Patella T-reflex response to knee joint was recorded with the help of a camera after three LED markers placed on the knee joint. For the correctness of the pattern of the movements, data were compared with two-axis goniometer of Biometric Ltd company and thereby it was shown that the reflex patterns are identical. Thereon, using the software we have developed, the Patella T-reflex kinesiologic parameters were calculated and data were obtained for objective evaluation of the Spasticity patients.

Detection of frequency sub-bands on Heart Rate Variability in Supra-ventricular Tachyarrhythmia patients using artificial neural networks

Supra-ventricular Tachyarrhythmia (SVTA) called as disturbances of heart around atria and Atrioventricular (AV) node is one of the most common heart arrhythmias. Heart Rate Variability (HRV) is a pointer for classification of Autonomic Nervous System (ANS) and Heart Arrhythmias. Wavelet Packet Transform (WPT) is an efficient tool for HRV like non stationary signals. This study presents critical frequency intervals for HRV analysis in SVTA patients and the effectiveness of these frequency intervals on base-bands. In the study, sub-frequency regions on HRV in MIT-BIH SVTA database obtained from half-hour ECG recordings of 78 patients are calculated and analyzed. Each data is decomposed in sub-frequency region using WPT with 8 levels and their domination effects of each sub-frequency region on its base-band are evaluated using Multi Layer Perceptron Neural Networks (MLPNN). While 0.0546875 – 0.078125 Hz on Low Frequency band (LF) points the highest accuracy value, subfrequency bands including 0.1171875 – 0.15625 Hz frequency interval have the lowest accuracy values. However, accuracy values of sub-frequency regions on High Frequency (HF) band points near values each other and the detection of dominant sub-bands got harder. In this band, while sub-frequency bands including 0.15625 – 0.2734375 Hz frequency interval point high accuracy value, sub-frequency bands including 0.25 – 0.328125 Hz frequency interval have lower accuracy values.

The impact of Daubechies Wavelet performances on Ventricular Tachyarrhythmia Patients for determination of dominant frequency bands in HRV

Heart rate variability is an important tool for cardiac diagnosis. In this work, the band analysis of Very Low Frequency (VLF) is performed using Wavelet Packet Transform (WPT) on VTA (Ventricular Tachyarrhythmia) Patients. In order to determine the performance of Daubechies Wavelets, the energy value of each nodes is computed using db4, db8, db12, db16 and db20 WPTs. The energy characteristic of main VLF band is estimated from sub-bands using Multilayer Perceptron Neural Network and dominant sub-bands of VLF are obtained. The dominant band is determined and the performance of Daubechies Wavelets is compared in VLF band.

Analyze of sympathovagal balance on ventricular tachyarrhythmia patients using wavelet packet transform

Heart rate variability is a important physiological data that presents information about sympathetic or parasympathetic domination in autonomic nervous system. Sympathovagal balance (SB) can be described as the power rate of low frequency/high frequency. In this study, we have investigated SB on ventricular tachyarrhythmia (VTA) patients and compared with normal data obtained from same patients. In VTA, min value and max value have been determined as 0.1236 and 11.0855. In normal data, SB min value is 0.3640 and SB max value is 6.5239.