Time frequency based coherence analysis between EEG and EMG activities in fatigue duration.

In voluntary movements, functional role of synchronized neuronal activity in the human motor system is important to detect and diagnose of the some diseases. In some previous studies, EEG signals and responses belong to an exercise are examined and an increased EEG activity reported in alpha frequency band. The reason of this is not clear whether a change is a direct result of the exhaustion or whether it is an adaptation. Time frequency based coherence analysis may be excellent tools to asses the fatigue stages. The experiment was planned with three fatigue stage and the cortical-muscular synchronizations were observed and examined. Simultaneously cortical electroencephalography (EEG) activities and electromyography (EMG) activities that are activated by phasic voluntary movements are recorded for 10 healthy young person and relation of the coherence between the signals are observed in time frequency domain. There is a decreasing significant coherence activity in third fatigue stage against to first and second fatigue stages. Time frequency based coherence analysis is a good method to explore motor cortex control of muscle activity in the fatigued persons. Time frequency based coherence analysis gives useful result for recordings of simultaneously cortical activity EEG and EMG during a phasic voluntary movement to determination of fatigue levels.

AR spectral analysis of EEG signals by using maximum likelihood estimation.

In this study, EEG signals were analyzed using autoregressive (AR) method. Parameters in AR method were realized by using maximum likelihood estimation (MLE). Results were compared with fast Fourier transform (FFT) method. It is observed that AR method gives better results in the analysis of EEG signals. On the other hand, the results have also showed that AR method can also be used for some other researches and diagnosis of diseases.

Application of periodogram and AR spectral analysis to EEG signals.

In this study, in order to analyze the EEG signal, the conventional and modern spectral methods were investigated. Interpretation and performance of these methods were detected for clinical applications. For this purpose EEG data obtained from different persons were processed by PC computer using periodogram and AR model algorithms. Periodogram and AR modeling approaches were compared for their resolution and interpretation performance. It was determined that the AR approach is better for the use in clinical and research areas, because of the clear spectra that are obtained by it.

Order determination in autoregressive modeling of diastolic heart sounds.

Previous studies demonstrated that spectral analysis of diastolic heart sounds may provide valuable information for the detection of coronary artery disease. Although parametric modeling methods were successfully used to achieve this goal, and showed considerable performance, the accuracy and precision of the analysis is strongly dependent on the model order selected. In order to investigate the effects of model order selection on the analysis, diastolic heart sound recorded from both normal and diseased patients were analyzed using the AR modeling, which is computationally the most efficient parametric spectral analysis method. The model order were determined by using four different model order selection criteria. The results showed that the four criteria yielded different order for the same data set. On the other hand, different criteria showed different performance in different measurement conditions. Effect of arbitrary order selection was also discussed. As a result, an optimal AR model order that may be used for every case was determined.

Application of autoregressive and Fast Fourier Transform spectral analysis to tricuspid and mitral valve stenosis.

Tricuspid and mitral valve flow area was determined from an apical four-chamber view. Doppler signals were recorded from normal subjects and patients with tricuspid and mitral valve stenosis by using a pulsed Doppler unit. The location of sample volume was chosen at the ventricular side of the valve orifice and within the right ventricular tract. This was done with the aid of an integrated cardiac imaging facility. The analog signal at the output of the Doppler unit was sampled and digitized using an analog/digital interface board and transferred to a personal computer. The data were then analyzed using the Fast Fourier Transform (FTT) and autoregressive (AR) modeling methods of spectral analysis and all the sonograms were obtained. Statistical comparison between the FFT and AR methods was made. The results show that the AR method offers a superior performance over the FFT method as regards the assessment of tricuspid and mitral valve stenosis.

A study on 3D Monte Carlo modeling of photon propagation through tissue.

Monte Carlo techniques have become popular in modeling of the random events with advantage of powerful computing systems. Especially they have been applied to simulate processes involving random behavior such as diffusion of the Gamma rays through matter and electron concentrations in semiconductors. Recent medical innovations such as Computer Automated Tomography (CAT) and Positron Emission Tomography (PET) are ideal for Monte Carlo modeling techniques. This paper presents derivation and results of the three dimensional Monte Carlo simulation. The results are presented for tissue interactions with photons at wavelength of 207 nm X rays and 630 nm red light. Resulting values of model tissue have been interpolated and mapped into two-dimensional pattern in gray tone. Combination of two-dimensional patterns allow a reconstruction in three dimensions.

Comparison of FFT- and AR-based sonogram outputs of 20 MHz pulsed Doppler data in real time.

Real time sonogram outputs of autoregressive (AR) and Fast Fourier Transform (FFT) spectral analysis of 20 MHz pulsed ultrasonic Doppler blood flowmeter are presented. Data obtained from coronary, renal, iliac, digital and mesenteric arteries were processed using AR- and FFT-based spectral analysis techniques and interpretable sonograms were constructed. In comparison with the FFT-based sonogram outputs. AR-based sonogram outputs for 20 MHz pulsed Doppler data provide better results. Hence, the AR modeling is strongly recommended for small vessels with diameters between 1 and 2 mm.

Autoregressive-based sonogram outputs of 20 MHz pulsed Doppler data.

The sonogram outputs of autoregressive (AR) based spectral analysis of a 20 MHz pulsed ultrasonic Doppler blood flowmeter are presented. The data obtained from coronary and iliac arteries were processed using AR-based spectral analysis technique, and then the interpretable sonograms by the surgeons were constructed. When the sonogram outputs were compared to other sonograms which were analysed using the other techniques such as fast Fourier transform (FFT), it was observed that AR-based sonograms for 20 MHz pulsed Doppler data have provided better results. Therefore, the technique is strongly recommended in the examining of small vessels which are 1 to 2 mm in diameter.

Application of autoregressive analysis to 20 MHz pulsed Doppler data in real time.

The real time application of autoregressive (AR) spectral analysis to a 20-MHz pulsed Doppler blood flowmeter is presented. The system consists of a TMS 320C25 digital signal processor with a 80286 based PC/AT microcomputer and associated interfacing circuitry. The AR method was used for in vivo spectral analysis of the signals obtained from a 20-MHz pulsed Doppler flowmeter in real time. The data obtained from digital and coronary arteries were processed using both AR and FFT spectral analysis methods. Also the data obtained from a stenosis coronary artery under surgical operation were processed using both methods. When the results are compared, it is seen that autoregressive analysis has given better results. Therefore the technique can be used in the examining of small vessels such as renal, iliac, mesenteric, coronary and digital arteries.