Ömer Halil Çolak

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.

Preprocessing effects in time--frequency distributions and spectral analysis of heart rate variability

Heart rate variability (HRV) is very significance noninvasive tool for autonomic nervous system (ANS) analysis. HRV signal includes both slowly changing components and rapidly changing transient events. This study presents effects of preprocessing of HRV in time-frequency analysis and spectral estimations. Preprocessing includes two levels as detrending of trend using smoothness prior method and correction of ectopics using integral pulse frequency modulation (IPFM). The datasets used in this study are obtained from the Spontaneous Ventricular Tachyarrhythmia (VTA) database. Datasets include least one ventricular tachyarrhythmia (VT) or ventricular fibrillation (VF) episode. Effects of preprocessing are investigated for time-frequency analysis using continuous wavelet transform (CWT) and spectrogram and for spectral analysis using periodogram, Welchs periodogram and Burgs periodogram. Performance of these methods in determination of VT or VF episode is analyzed. Importance of preprocessing is explained comparing of obtained 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.

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.

Estimation and evaluation of sub-bands on LF and HF base-bands in HRV for Ventricular Tachyarrhythmia patients

Heart Rate Variability (HRV) is an efficient tool for assessment of Sympathovagal Balance (SB) and classification of cardiac disturbances. However, its index may be not enough for classification and evaluation of some disease. This study presents 32 new sub-bands over LF and HF base-bands that are accepted in the literature. Moreover, it determines dominant sub-bands over both base-bands in VTA database. These sub-bands are obtained using Wavelet Packet Transform (WPT) and evaluated using Multilayer Perceptron Neural Networks (MLPNN). Results are compared with obtained results from normal datasets. The domination effects of these sub-bands are assessed according to comparison of each other related to MLPNN training and test accuracy percentages by selecting different width of windows. As a result, obtained results showed that the LF zone including LF1, LF2 and LF3 sub-bands on 0.0390625-0.0859375Hz frequency range is the most dominant over the LF base-band and, the HF zone including HF1, HF2 and HF3 on 0.1953125-0.28125Hz frequency range is the most dominant over the HF base-band. In normal datasets, distinctive domination effect has not been determined.

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.

Image-based Analysis of Emotional Facial Expressions in Full Face Transplants

In this study, it is aimed to determine the degree of the development in emotional expression of full face transplant patients from photographs. Hence, a rehabilitation process can be planned according to the determination of degrees as a later work. As envisaged, in full face transplant cases, the determination of expressions can be confused or cannot be achieved as the healthy control group. In order to perform image-based analysis, a control group consist of 9 healthy males and 2 full-face transplant patients participated in the study. Appearance-based Gabor Wavelet Transform (GWT) and Local Binary Pattern (LBP) methods are adopted for recognizing neutral and 6 emotional expressions which consist of angry, scared, happy, hate, confused and sad. Feature extraction was carried out by using both methods and combination of these methods serially. In the performed expressions, the extracted features of the most distinct zones in the facial area where the eye and mouth region, have been used to classify the emotions. Also, the combination of these region features has been used to improve classifier performance. Control subjects and transplant patients’ ability to perform emotional expressions have been determined with K-nearest neighbor (KNN) classifier with region-specific and method-specific decision stages. The results have been compared with healthy group. It has been observed that transplant patients don’t reflect some emotional expressions. Also, there were confusions among expressions.

Assessment of Emotional Expressions after Full-Face Transplantation

We assessed clinical features as well as sensory and motor recoveries in 3 full-face transplantation patients. A frequency analysis was performed on facial surface electromyography data collected during 6 basic emotional expressions and 4 primary facial movements. Motor progress was assessed using the wavelet packet method by comparison against the mean results obtained from 10 healthy subjects. Analyses were conducted on 1 patient at approximately 1 year after face transplantation and at 2 years after transplantation in the remaining 2 patients. Motor recovery was observed following sensory recovery in all 3 patients; however, the 3 cases had different backgrounds and exhibited different degrees and rates of sensory and motor improvements after transplant. Wavelet packet energy was detected in all patients during emotional expressions and primary movements; however, there were fewer active channels during expressions in transplant patients compared to healthy individuals, and patterns of wavelet packet energy were different for each patient. Finally, high-frequency components were typically detected in patients during emotional expressions, but fewer channels demonstrated these high-frequency components in patients compared to healthy individuals. Our data suggest that the posttransplantation recovery of emotional facial expression requires neural plasticity.