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

Discontinuous doppler signals simulating respiratory misregistration: Effect on autoregressive frequency spectra

In this study, we have produced discontinuous Doppler signals of carotid artery and internal jugular vein, simulating respiratory misregistration. The aim of the study is to observe the effect of signal discontinuity and its duration on power spectral density vs. frequency graphs obtained by Autoregressive Modeling. The signals were recorded from ten male volunteers. Signal interruption was performed by moving the sampling volume in and out of the vessel bidirectionally. To estimate the effect of on-line recording time and signal discontinuity on frequency spectra, we have worked on a control data of 30s with continuous signal, and three sets of data with artificially interrupted signals of 30, 60 and 90s duration. Maximum power spectral density, area under the power spectral density, and frequency level corresponding to maximum power spectral density were calculated on frequency spectra. The frequency level corresponding to maximum power spectral density provides the most statistically stable finding in our preliminary data. The signal duration of the signal had no significant effect on the statistical stability of the frequency level.

Comparison of fast Fourier transformation and autoregressive modelling as a diagnostic tool in analysis of lower extremity venous signals

In this study, we have compared the efficacy of autoregressive modelling (ARM) and fast Fourier transformation (FFT) of Doppler signals from lower extremity veins of healthy volunteers in various physiologic situations. Compared to FFT, ARM produced smooth spectra and less spectral broadening both in sonograms and power spectra. However, faulty positioning of the peaks along the time axis in FFT-derived power spectral density curves show that FFT is not a suitable method if these graphs are to be used as a diagnostic tool. Analysis of ARM-based venous sonograms and power spectral density graphs revealed that FFT should not be used in signals with high power spectral density levels and low-frequency bandwidth within limited segments of time.

Nonlinear multivariate analysis of neurophysiological signals

Multivariate time series analysis is extensively used in neurophysiology with the aim of studying the relationship between simultaneously recorded signals. Recently, advances on information theory and nonlinear dynamical systems theory have allowed the study of various types of synchronization from time series. In this work, we first describe the multivariate linear methods most commonly used in neurophysiology and show that they can be extended to assess the existence of nonlinear interdependence between signals. We then review the concepts of entropy and mutual information followed by a detailed description of nonlinear methods based on the concepts of phase synchronization, generalized synchronization and event synchronization. In all cases, we show how to apply these methods to study different kinds of neurophysiological data. Finally, we illustrate the use of multivariate surrogate data test for the assessment of the strength (strong or weak) and the type (linear or nonlinear) of interdependence between neurophysiological signals.

Spectral analysis of internal carotid arterial Doppler signals using FFT, AR, MA, and ARMA methods

In this study, Doppler signals recorded from internal carotid artery of 45 subjects were processed by PC-computer using classical (fast Fourier transform) and model-based (autoregressive, moving average, autoregressive moving average (ARMA) methods) methods. Power spectral density estimates of internal carotid arterial Doppler signals were obtained using these spectral analysis methods. The variations in the shape of the Doppler power spectra as a function of time were presented in the form of sonograms in order to determine the degree of internal carotid artery stenosis. These Doppler power spectra and sonograms were then used to compare the applied methods in terms of their frequency resolution and the impact on determining stenosis in internal carotid arteries. Based on the results, performance characteristics of the autoregressive and ARMA methods were found extremely valuable for spectral analysis of internal carotid arterial Doppler signals obtained from healthy subjects and unhealthy subjects having artery stenosis.

Comparison of eigenvector methods with classical and model-based methods in analysis of internal carotid arterial Doppler signals

Doppler ultrasound is known as a reliable technique, which demonstrates the flow characteristics and resistance of arteries in various vascular disease. In this study, internal carotid arterial Doppler signals recorded from 105 subjects were processed by PC-computer using classical, model-based, and eigenvector methods. The classical method (fast Fourier transform), two model-based methods (Burg autoregressive, least-squares modified Yule-Walker autoregressive moving average methods), and three eigenvector methods (Pisarenko, multiple signal classification, and Minimum-Norm methods) were selected for processing internal carotid arterial Doppler signals. Doppler power spectra of internal carotid arterial Doppler signals were obtained using these spectrum analysis techniques. The variations in the shape of the Doppler power spectra were examined in order to obtain medical information. These power spectra were then used to compare the applied methods in terms of their frequency resolution and the effects in determination of stenosis and occlusion in internal carotid arteries.

Application of classical and model-based spectral methods to ophthalmic arterial Doppler signals with uveitis disease

In this study, Doppler signals recorded from ophthalmic artery of 75 subjects were processed by PC-computer using classical and model-based methods. The classical method (fast Fourier transform) and three model-based methods (Burg autoregressive, moving average, least-squares modified Yule-Walker autoregressive moving average methods) were selected for processing ophthalmic arterial Doppler signals with uveitis disease. Doppler power spectra of ophthalmic arterial Doppler signals were obtained by using these spectrum analysis techniques. The variations in the shape of the Doppler spectra as a function of time were presented in the form of sonograms in order to obtain medical information. These Doppler spectra and sonograms were then used to compare the applied methods in terms of their frequency resolution and the effects in determination of uveitis disease.

Detection of ophthalmic artery stenosis by least-mean squares backpropagation neural network

Doppler ultrasound is a noninvasive technique that allows the examination of the direction, velocity, and volume of blood flow. In this study, ophthalmic artery Doppler signals were obtained from 105 subjects, 48 of whom had suffered from ophthalmic artery stenosis. A least-mean squares backpropagation neural network was used to detect the presence or absence of ophthalmic artery stenosis. Spectral analysis of ophthalmic artery Doppler signals was done by the Welch method for determining the neural network inputs. The network was trained, cross validated and tested with subject records from the database. Performance indicators and statistical measures were used for evaluating the neural network. Ophthalmic artery Doppler signals were classified with the accuracy varying from 88.9% to 90.6%.

Determination of stenosis and occlusion in arteries with the application of FFT, AR, and ARMA methods

Doppler ultrasound is a noninvasive technique that allows the examination of the direction, velocity, and volume of blood flow. Therefore, Doppler ultrasonography is known as reliable technique, which demonstrates the flow characteristics and resistance of arteries in various vascular disease. In this study, arterial Doppler signals recorded from 105 subjects were processed by PC-computer using fast Fourier transform, Burg autoregressive, and least squares modified Yule-Walker autoregressive moving average methods. Doppler power spectrums of arterial Doppler signals were obtained by using these spectrum analysis techniques. The variations in the shape of the Doppler power spectrums as a function of time were presented in the form of sonograms in order to obtain medical information. These sonograms were then used to compare the applied methods in terms of their frequency resolution and the effects in determination of stenosis and occlusion in arteries. Reliable information on hemodynamic alterations in arteries can be obtained by evaluation of these sonograms.

Determination of coronary failure with the application of FFT and AR methods

In this study, Doppler signals recorded from the output of carotid artery of 30 patients were transferred to a personal computer (PC) by using a 16-bit sound card. Doppler difference frequencies were recorded from each of the patients, and then analyzed using fast Fourier transform (FFT) and least squares autoregressive (AR) methods to obtain their sonograms. These sonograms are then used to compare with the applied methods in terms of medical evaluation.

Comparison of FFT and adaptive ARMA methods in transcranial Doppler signals recorded from the cerebral vessels

In this work, transcranial Doppler signals recorded from the temporal region of the brain on 35 patients were transferred to a personal computer by using a 16-bit sound card. Fast Fourier transform and adaptive auto regressive-moving average (A-ARMA) methods were applied to transcranial Doppler frequencies obtained from the middle cerebral artery in the temporal region. Spectral analyses were obtained to compare both methods for medical diagnoses. The sonograms obtained using A-ARMA method give better results for spectral resolution than the FFT method. The sonograms of A-ARMA method offer net envelope and better imaging, so that the determination of blood flow and brain pressure can be calculated more accurately. All diseases show higher resistance to flow than controls with no difference between males and females. Whereas values between disease classes differed, resistance within each class was remarkably constant.

Determination of Behcet disease with the application of FFT and AR methods

In this study, Doppler signals recorded from ophthalmic artery of 86 patients were processed by personal computer using fast Fourier transform, Burg autoregressive (AR), and least-squares AR methods. By using these spectrum analysis techniques, the variations in the shape of the Doppler spectrums as a function of time were presented in the form of sonograms in order to obtain medical information. These sonograms were then used to compare the applied methods in terms of their frequency resolution and the effects in determination of Behcet disease.

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.

Determination of aorta failure with the application of FFT, AR and wavelet methods to Doppler technique

In this study, Doppler signals recorded from the output of aorta valve of 24 patients were transferred to a personal computer (PC) by using a 16-bit sound card. Doppler difference frequencies were recorded from each of the patients, and then analyzed using fast Fourier transform, autoregressive and wavelet transform analyzer to obtain their sonograms. These sonograms are then used to compare with the applied methods in terms of medical evaluation.

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.

Design parameters of pulsed wave ultrasonic Doppler blood flowmeter

Pulse wave Doppler techniques promise the ability to make more effective measurements of dynamic cardiovascular functions. Quantitative measurement of blood flow can be achieved by using the pulse wave Doppler system. This paper discusses the design parameters of the pulse wave Doppler blood flowmeter (PW Doppler) with respect to the physiological system.

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.