Determination of Respiratory Parameters by Means of Hurst Exponents of the Respiratory Sounds and Stochastic Processing Methods.

OBJECTIVES: System approach to the human respiratory system and input/output signals which characterize the system properties were not explored in detail in the literature. The aim of this study is to propose a combination of methods to investigate the indirect relationship between the fractal properties of Respiratory Signals (RS) and Respiratory Sound Signals (RSS) and the clinically measured respiratory parameters. METHODS: We used Hurst exponent to reveal the fractal properties of RS and RSS and to estimate the pressures in the respiratory system. The combination of well-known statistical signal processing methods and optimization were applied to the experimentally acquired 23 records. Pearson correlation coefficient and Bland-Altman analysis were the chosen validation methods. RESULTS: Considerable amounts of Hurst exponent values of RSS were found to be between 0.5 and 1, which means increasing trend or decreasing trend can be seen in RSS with fractional Gaussian process properties. Results of the pressure estimator revealed that internal pressure due to tissue viscoelasticity is higher than the pressure due to static elasticity. Feature power and skewness also provided distinctive results for all recordings. CONCLUSION: Hurst exponent values of the RSS are fruitful representation of the signals which bring the underlaying system characteristics into the surface. We illustrated that required number of sensors can be reduced in the feature calculation to ease implementation effort on the hardware of the handheld devices. SIGNIFICANCE: Bland-Altman plots were very successful to demonstrate the connection between the sets of measured respiratory parameters and calculated features.

Analysis of linear lung models based on state-space models.

BACKGROUND AND OBJECTIVES: Linear parametric respiratory system models have been used in the model-based analysis of the respiratory system. Although there are studies exploring the physiological correctness and fitting accuracy of the models, they are not analysed in terms of interaction between parameters and dynamics of the model. In this study we propose to use state-space modelling to yield the time-varying nature of the system incorporated by the parameters. METHODS: We tested controllability, observability and stability characteristics of the equation of motion, 2-comp. parallel, 2-comp. series, viscoelastic, 6-element and mead models while using the parameters given in the literature. In the sensitivity analysis we proposed to use dual Desensitized Linear Kalman Filter (DKF) and Extended Kalman Filter (EKF) method. In this method, state error covariance revealed the parameter sensitivities for each model. RESULTS: Results showed that all models, except 2-comp. parallel and mead models, are both controllable and observable models. On the other hand all models, except mead model, are stable models. Regarding to the sensitivity analysis, dual DKF - EKF method estimated states of the models successfully with a low estimation error. Sensitivity analysis results showed that airway parameters have higher effects on the state estimation than the other parameters have. CONCLUSION: We proved that state-space evaluation of the previously proposed parametric models of the respiratory system led us to quantitative and qualitative assessments of the respiratory models. Moreover parameter values found in the literature have different effects on the models.

Disturbance in long distance gamma coherence in bipolar disorder.

The aim of this study was to investigate long distance event-related gamma (28-48 Hz) coherence in mania before and after valproate monotherapy. Gamma coherence in response to visual oddball paradigm in ten medication-free, manic patients was studied before and after six weeks of valproate monotherapy in comparison to ten controls. Inter-hemispheric F(3)-F(4), C(3)-C(4), T(3)-T(4), T(5)-T(6), P(3)-P(4), O(1)-O(2) and intra-hemispheric F(3)-P(3), F(4)-P(4), F(3)-T(5), F(4)-T(6), F(3)-O(1), F(4)-O(2), C(3)-O(1), C(2)-O(4) electrode pairs were included in the analysis. Repeated measures ANOVA revealed a significant difference between groups with regard to pre-treatment coherence values (p: 0.018). The coherence to the target stimuli at the right fronto-temporal location was significantly reduced by 35.41% in the patients compared to controls (p: 0.003). Patients showed significantly lower pre-treatment coherence values in response to non-target stimuli compared to controls at the right fronto-temporal (28.51%, p: 0.004), right fronto-occipital (23.71%, p: 0.024), and right centro-occipital (25.69%, p: 0.029) locations. After six weeks of valproate monotherapy, manic symptoms improved significantly. Post-treatment change in target and non-target coherence values was statistically non-significant. EEG coherence is a measure of functional connectivity in the brain. Event-related gamma oscillations are essential for brain electrical activity. The results show that acute mania presents right sided long distance connectivity disturbance, thus pointing to the potential importance of measuring oscillatory responses in the search for consistent neurobiological markers in such a complicated condition as bipolar disorder.

Decrease of evoked delta, theta and alpha coherences in Alzheimer patients during a visual oddball paradigm.

In this study event related coherence of patients with Alzheimer type of dementia (AD) was analyzed by using a visual oddball paradigm as stimuli. A total of 21 mild probable AD subjects (10 untreated, 11 treated) were compared with a group of 19 healthy controls. The members of the groups had their EEG recorded from 12 electrodes by means of a visual oddball paradigm. The evoked coherence was analyzed for delta (1-3.5 Hz), theta (4-7 Hz) and alpha (8-13 Hz) frequency ranges for inter-hemispheric (F3-F4, C3-C4, T3-T4, T5-T6, P3-P4, O1-O2) and long range intra-hemispheric (F3-P3, F4-P4, F3-T5, F4-T6, F3-O1, F4-O2) electrode pairs. The control group showed higher values of evoked coherence in "delta", "theta" and "alpha" bands in the left fronto-parietal electrode pairs in comparison with the untreated AD group (p<0.01 for all frequency bands). Furthermore, the control group showed higher values of evoked coherence in the left fronto-parietal electrode pair in theta frequency band (p<0.01) and higher values of evoked coherence in the right fronto-parietal electrode pair in delta band (p<0.01) when compared to treated AD group. The only significant difference between the treated and untreated AD groups was in the alpha band. The treated AD group showed higher values of evoked coherence at the left fronto-parietal pair in alpha band in comparison to the untreated AD group (p<0.01). During a working memory process the coherence in the left fronto-parietal electrode pair (F3-P3) of AD patients is significantly decreased, thus indicating reduced connectivity between frontal and parietal sites.

Fingerprint image enhancement using CNN filtering techniques.

Due to noisy acquisition devices and variation in impression conditions, the ridgelines of fingerprint images are mostly corrupted by various kinds of noise causing cracks, scratches and bridges in the ridges as well as blurs. These cause matching errors in fingerprint recognition. For an effective recognition the correct ridge pattern is essential which requires the enhancement of fingerprint images. Segment by segment analysis of the fingerprint pattern yields various ridge direction and frequencies. By selecting a directional filter with correct filter parameters to match ridge features at each point, we can effectively enhance fingerprint ridges. This paper proposes a fingerprint image enhancement based on CNN Gabor-Type filters.