Saime Akdemir Akar

Spectral analysis of photoplethysmographic signals: The importance of preprocessing

Abstract Heart rate variability (HRV) is an important and useful index to assess the responses of the autonomic nervous system (ANS). HRV analysis is performed using electrocardiography (ECG) or photoplethysmography (PPG) signals which are typically subject to noise and trends. Therefore, the elimination of these undesired conditions is very important to achieve reliable ANS activation results. The purpose of this study was to analyze and compare the effects of preprocessing on the spectral analysis of HRV signals obtained from PPG waveform. Preprocessing consists of two stages: filtering and detrending. The performance of linear Butterworth filter is compared with nonlinear weighted Myriad filter. After filtering, two different approaches, one based on least squares fitting and another on smoothness priors, were used to remove trends from the HRV signal. The results of two filtering and detrending methods were compared for spectral analysis accomplished using periodogram, Welchs periodogram and Burgs method. The performance of these methods is presented graphically and the importance of preprocessing clarified by comparing the results. Although both filters have almost the same performance in the results, the smoothness prior detrending approach was found more successful in removing trends that usually appear in the low frequency bands of PPG signals. In conclusion, the results showed that trends in PPG signals are altered during spectral analysis and must be removed prior to HRV analysis.

Nonlinear analysis of EEGs of patients with major depression during different emotional states

BACKGROUND Although patients with major depressive disorder (MDD) have dysfunctions in cognitive behaviors and the regulation of emotions, the underlying brain dynamics of the pathophysiology are unclear. Therefore, nonlinear techniques can be used to understand the dynamic behavior of the EEG signals of MDD patients. METHODS To investigate and clarify the dynamics of MDD patients׳ brains during different emotional states, EEG recordings were analyzed using nonlinear techniques. The purpose of the present study was to assess whether there are different EEG complexities that discriminate between MDD patients and healthy controls during emotional processing. Therefore, nonlinear parameters, such as Katz fractal dimension (KFD), Higuchi fractal dimension (HFD), Shannon entropy (ShEn), Lempel-Ziv complexity (LZC) and Kolmogorov complexity (KC), were computed from the EEG signals of two groups under different experimental states: noise (negative emotional content) and music (positive emotional content) periods. RESULTS First, higher complexity values were generated by MDD patients relative to controls. Significant differences were obtained in the frontal and parietal scalp locations using KFD (p<0.001), HFD (p<0.05), and LZC (p=0.05). Second, lower complexities were observed only in the controls when they were subjected to music compared to the resting baseline state in the frontal (p<0.05) and parietal (p=0.005) regions. In contrast, the LZC and KFD values of patients increased in the music period compared to the resting state in the frontal region (p<0.05). Third, the patients׳ brains had higher complexities when they were exposed to noise stimulus than did the controls׳ brains. Moreover, MDD patients׳ negative emotional bias was demonstrated by their higher brain complexities during the noise period than the music stimulus. Additionally, we found that the KFD, HFD and LZC values were more sensitive in discriminating between patients and controls than the ShEn and KC measures, according to the results of ANOVA and ROC calculations. CONCLUSION It can be concluded that the nonlinear analysis may be a useful and discriminative tool in investigating the neuro-dynamic properties of the brain in patients with MDD during emotional stimulation.

Analysis of heart rate variability during auditory stimulation periods in patients with schizophrenia

The vulnerability–stress model is a hypothesis for symptom development in schizophrenia patients who are generally characterized by cardiac autonomic dysfunction. Therefore, measures of heart rate variability (HRV) have been widely used in schizophrenics for assessing altered cardiac autonomic regulations. The goal of this study was to analyze HRV of schizophrenia patients and healthy control subjects with exposure to auditory stimuli. More specifically, this study examines whether schizophrenia patients may exhibit distinctive time and frequency domain parameters of HRV from control subjects during at rest and auditory stimulation periods. Photoplethysmographic signals were used in the analysis of HRV. Nineteen schizophrenic patients and twenty healthy control subjects were examined during rest periods, while exposed to periods of white noise (WN) and relaxing music. Results indicate that HRV in patients was lower than that of control subjects indicating autonomic dysfunction throughout the entire experiment. In comparison with control subjects, patients with schizophrenia exhibited lower high-frequency power and a higher low-frequency to high-frequency ratio. Moreover, while WN stimulus decreased parasympathetic activity in healthy subjects, no significant changes in heart rate and frequency-domain HRV parameters were observed between the auditory stimulation and rest periods in schizophrenia patients. We can conclude that HRV can be used as a sensitive index of emotion-related sympathetic activity in schizophrenia patients.

Determination of optimal parameters for bilateral filter in brain MR image denoising

This is the first GA based-optimization study to find optimal parameters of bilateral filter.Both the simulated and clinical brain MR images were used for Rician noise removal.The preservation of edges and removal of noise were investigated for different noise levels.A better performance in computation time of our approach was observed.The quality of the denoised images with the proposed parameters was validated using quantitative metrics. Noise elimination is an important pre-processing step in magnetic resonance (MR) images for clinical purposes. In the present study, as an edge-preserving method, bilateral filter (BF) was used for Rician noise removal in MR images. The choice of BF parameters affects the performance of denoising. Therefore, as a novel approach, the parameters of BF were optimized using genetic algorithm (GA). First, the Rician noise with different variances (?=10, 20, 30) was added to simulated T1-weighted brain MR images. To find the optimum filter parameters, GA was applied to the noisy images in searching regions of window size 3×3, 5×5, 7×7, 11×11, and 21×21, spatial sigma 0.1-10 and intensity sigma 1-60. The peak signal-to-noise ratio (PSNR) was adjusted as fitness value for optimization.After determination of optimal parameters, we investigated the results of proposed BF parameters with both the simulated and clinical MR images. In order to understand the importance of parameter selection in BF, we compared the results of denoising with proposed parameters and other previously used BFs using the quality metrics such as mean squared error (MSE), PSNR, signal-to-noise ratio (SNR) and structural similarity index metric (SSIM). The quality of the denoised images with the proposed parameters was validated using both visual inspection and quantitative metrics. The experimental results showed that the BF with parameters proposed by us showed a better performance than BF with other previously proposed parameters in both the preservation of edges and removal of different level of Rician noise from MR images. It can be concluded that the performance of BF for denoising is highly dependent on optimal parameter selection.

Respiratory Variability during Different Auditory Stimulation Periods in Schizophrenia Patients

BACKGROUND Schizophrenic patients are known to have difficulty processing emotions and to exhibit impairment in stimuli discrimination. However, there is limited knowledge regarding their physiological responsivity to auditory stimuli. OBJECTIVES The purpose of this study was to compare the respiratory effects of two types of auditory stimuli with emotional content, classical Turkish music (CTM) and white noise (WN), on schizophrenia patients and healthy control subjects. METHODS Forty-six individuals participated in the experiment, and respiratory signals derived from a strain-gauge were recorded. Two important respiratory patterns, respiration rate and depth, were analyzed. RESULTS The results indicated that the patients presented a significantly higher respiration rate than control subjects during the initial baseline and WN exposure periods. Although CTM evoked an increase in respiration rates and a decrease in respiration depths in the control group, no significant differences were found during the stimulation periods in the patient group. The respiration rate was lower in the post-stimulation period than during the initial baseline period, and no respiration depth differences were found for the WN, music or post-stimulation periods in the schizophrenia group. Patients exhibited a greater respiration depth than the control subjects over all periods; however, a significant difference between the patient and control groups was obtained in the second resting condition and CTM exposure period. Furthermore, to analyze the effect of symptom severity on respiratory patterns, patients were divided into two classes according to their Positive and Negative Syndrome Scale score. CONCLUSIONS Further studies are needed to correlate respiratory differences with emotionally evocative stimuli and to refine our understanding of the dynamics of these types of stimuli in relation to clinical state and medication effects.

Nonlinear analysis of EEG in major depression with fractal dimensions

Major depressive disorder (MDD) is a psychiatric mood disorder characterized by cognitive and functional impairments in attention, concentration, learning and memory. In order to investigate and understand its underlying neural activities and pathophysiology, EEG methodologies can be used. In this study, we estimated the nonlinearity features of EEG in MDD patients to assess the dynamical properties underlying the frontal and parietal brain activity. EEG data were obtained from 16 patients and 15 matched healthy controls. A wavelet-chaos methodology was used for data analysis. First, EEGs of subjects were decomposed into 5 EEG sub-bands by discrete wavelet transform. Then, both the Katzs and Higuchis fractal dimensions (KFD and HFD) were calculated as complexity measures for full-band and sub-bands EEGs. Last, two-way analyses of variances were used to test EEG complexity differences on each fractality measures. As a result, a significantly increased complexity was found in both parietal and frontal regions of MDD patients. This significantly increased complexity was observed not only in full-band activity but also in beta and gamma sub-bands of EEG. The findings of the present study indicate the possibility of using the wavelet-chaos methodology to discriminate the EEGs of MDD patients from healthy controls.

Investigation of heart rate variability in major depression patients using wavelet packet transform.

Studies conducted in major depression (MD) patients have reported a high risk of cardiac morbidity as a result of the relationship between changed cardiovascular activity (CA) and autonomic dysfunctions. The investigation of heart rate variability (HRV) gives valuable idea about variances in autonomic CA of MD patients. To get this knowledge, frequency-domain HRV analysis is frequently performed using Fourier transformation (FT) or discrete-wavelet transformation (DWT) to decompose the data into high-frequency (HF) and low-frequency (LF) bands. Nevertheless, it has been reported that the FT is not useful for nonstationary HRV signals and the DWT does not ensure required frequency boundaries of each band. This study aims to compare the frequency-domain HRV features using wavelet-packet-transform (WPT) with absolutely excellent approximation to required band ranges between the controls and patients. In addition to LF and HF band energies, sympathovagal balance that indicates the variation of sympathetic and parasympathetic activities were compared between two groups. Patients had a significantly lower HF energy, higher values of LF energy and higher LF/HF ratio. Our results recommend that impairments in coordination between parasympathetic and sympathetic behavior in MD patients can be assessed by HRV analysis using WPT with high resolution decomposition for needed bands.

Analysis of sympathovagal balance in patients with major depressive disorder using wavelet packet transform.

Elevated rates of cardiac morbidity have been frequently reported in major depressive disorder (MDD) patients as a result of the relationship between autonomic dysfunctions and varied cardiovascular activity. Heart rate variability (HRV) analysis is an important and non-invasive way for assessing the variances in autonomic nervous system activity of MDD patients. In spectral domain, HRV analysis is usually done by either Fourier transformation (FT) or discrete wavelet transformation (DWT) to divide the data into lowfrequency (LF) and high-frequency (HF) bands. However, while FT is not a proper method for non-stationary HRV data, DWT does not exactly produce required frequency ranges of each LF and HF bands. The purpose of the present study is to investigate the spectral HRV measures obtained by wavelet packet transform (WPT) with absolutely excellent approximation to predefined frequency ranges of bands. Eighteen healthy controls and age- and gender-match eighteen patients with MDD were participated in this study. Sympathovagal balance (LF/HF ratio) that reflects the variation of sympathetic and parasympathetic activities was compared between two groups. Individuals with depression had a significantly higher LF/HF ratio. Our findings suggest that dysfunctions in coordination between sympathetic and parasympathetic nervous system activity in MDD patients can be evaluated by WPT based HRV analysis with high resolution decomposition for required LF and HF bands.

Wavelet-Welch methodology for analysis of EEG signals of schizophrenia patients

The goal of this study was to analyze whether schizophrenia patients may exhibit distinctive sub-band EEG features from control subjects compared both at rest and during auditory stimulation periods. EEG signals of thirty schizophrenic patients and age-gender matched healthy subjects were recorded from F3 left frontal region and analyzed using wavelet decomposition and Welch power spectral density (PSD) methods. Results show that PSD of all EEG sub-bands in patients was higher than that of control subjects during all periods of the experiment. Moreover, auditory stimuli evoked a significant decrease of Welch PSD of beta activity of EEG data in both groups. However, no significant change was found between stimulation periods in schizophrenia group. This methodology can be used to analyze EEG signals of schizophrenia patients to reach discriminative features between patients and controls.

Applications of gamma knife on the treatment of Obsessive Compulsive Disorder

Obsessive Compulsive Disorder (OCD) is a mental disease with neuropsychiatric influences, on which considerable researches have been done. OCD can rarely be treated with drugs. The obsessions and compulsions of patients with OCD may show up as recurrently hand washing, enumerating something again and again and etc. For a better treatment outcome of OCD, medication and psychotherapy should be applied to patients together. On the other hand, for refractory patients to these treatments, neurosurgery with Gama knife technique may be preferred. In this thesis; the surgical technique of Gama knife for OCD, which has not become widespread yet, and the results obtained from the patients after operation are examined.