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Ultrasonography is a well-defined and widely accepted technique in the settings of interventional procedures requiring peripheral venous access, either for the confirmation of the vein patency (with the compression test) or for guiding needle insertion. This report describes a case of unsuccessful guidewire passage through the right iliac vein in spite of successful ultrasonography-guided puncture of the femoral vein. On repeat duplex ultrasonography, the Doppler waveform showed a continuous pattern without respiratory phasicity, which was consistent with proximal venous occlusion. Venous Doppler signal waveform analysis can be helpful for ensuring downstream patency when planning long-distance catheterization via femoral venous access. [ABSTRACT FROM AUTHOR]

Doppler signal extraction through a synchronization technique is proposed for ultrasonic vibration measurement systems, which employ a pair of transducers and a vibrating object. Due to Doppler effect, the received ultrasonic signal is phase modulated. The moving window filter (MWF) tuned for the center frequency of the carrier converts phase-modulated signal into amplitude-modulated signal because of the carrier frequency variation. To retrieve the vibrating object information, the proposed technique based on MWF synchronizes its output with the phase-modulated received signal so that it can accurately track the carrier variation. The synchronization is achieved through adaptive sampling frequency adjustment. With ultrasonic carrier tracking, the Doppler signal is retrieved that carries the vibration amplitude and frequency information. The simulation results are validated through experimental investigation carried out for wide variation of the carrier by digital implementation. [ABSTRACT FROM PUBLISHER]

The line-of-sight velocity of scattering facets is related to the Doppler signals of X-band coherent marine radar from the oceanic surface. First, the sign Doppler Estimator is applied to estimate the Doppler shift of each radar resolution cell. And then, in terms of the Doppler shift, a retrieval algorithm extracting the vertical displacement of the sea surface has been proposed. The effects induced by radar look-direction and radar spatial resolution are both taken into account in this retrieval algorithm. The comparison between the sea surface spectrum of buoy data and the retrieved spectrum reveals that the function of the radar spatial resolution is equivalent to a low pass filter, impacting especially the spectrum of short gravity waves. The experimental data collected by McMaster IPIX radar are also used to validate the performance of the retrieval algorithm. The derived significant wave height and wave period are compared with the in situ measurements, and the agreement indicates the practicality of the retrieval technology. [ABSTRACT FROM AUTHOR]

In this paper, multi-resolution Fourier analysis, labeled NY-MFA, suitable for time-varying spectra is proposed. It is shown that increasing time resolution has no detrimental effects on frequency resolution and achieved resolution bounds are in excess of Gabor-Heisenberg limit. Observation results with their increased time resolution, discriminated and precise frequency contents justify thoroughly the assumption of stationarity and consequently approach more precisely the concept of frequency and its changes than wavelets and short-term Fourier transform can do. [ABSTRACT FROM AUTHOR]

The article discusses a study which described the development of a biomedical system based on Learning Vector Quantization Neural Network (LVQ NN) to classify the internal carotid artery Doppler signals obtained from several subjects. Topics covered include preference for using Doppler ultrasound for investigation of the artery conditions, the feature extraction and classification parts of the system and the effectiveness of the LVQ NN method.

Abstract: When the maximum likelihood approach (ML) is used during the calculation of the Discrete Hidden Markov Model (DHMM) parameters, DHMM parameters of the each class are only calculated using the training samples (positive training samples) of the same class. The training samples (negative training samples) not belonging to that class are not used in the calculation of DHMM model parameters. With the aim of supplying that deficiency, by involving the training samples of all classes in calculating processes, a Rocchio algorithm based approach is suggested. During the calculation period, in order to determine the most appropriate values of parameters for adjusting the relative effect of the positive and negative training samples, a Genetic algorithm is used as an optimization technique. The purposed method is used to classify the internal carotid artery Doppler signals recorded from 136 patients as well as of 55 healthy people. Our proposed method reached 97.38% classification accuracy with fivefold cross-validation (CV) technique. The classification results showed that the proposed method was effective for the classification of internal carotid artery Doppler signals. [Copyright &y& Elsevier]

Abstract: We developed a biomedical system based on Discrete Hidden Markov Model (DHMM). The aim of our system is to classify the internal carotid artery Doppler signals. We applied a fuzzy approach to DHMM. Thus we decreased information loss and increased the classification performance. Our system reached 97.38% of classification accuracy with 5 fold cross validation. These results showed that the Fuzzy Discrete Hidden Markov Model (FDHMM) method is effective for classification of internal carotid artery Doppler signals. [Copyright &y& Elsevier]

Abstract: A parametric model for the ultrasound signals from blood and tissue is developed and a new imaging method, knowledge-based imaging, is defined. This method utilizes the likelihood ratio function to classify blood and tissue signals. The method separates blood and tissue signals by the difference in movement patterns in addition to the difference in powers. The prior information about the levels of expected system white noise and clutter noise are utilized to enhance the image quality. The implementation of knowledge-based imaging is outlined, and some knowledge-based images with different parameter settings are visually compared with a second-harmonic image, a fundamental image and a bandwidth image. In order to understand the parameter estimation process a computer simulation is introduced to outline the differences between the imaging methods. The apparent error rates are calculated in any reasonable tissue to blood signal ratio, tissue to white noise ratio and clutter to white noise ratio. A discussion of further development of knowledge-based imaging is also described in this paper. [Copyright &y& Elsevier]

Summary: Background: Defect reconstruction according to the free-style concept applied to perforator flaps allows flap harvesting in any anatomical region where an audible Doppler signal of a perforator is detected. We report the results of a study in which local perforator flaps were selected for reconstruction in different anatomical areas and were harvested using the free-style concept. Methods: During a 2-year period, defect coverage was carried out in 21 patients (n =21) in the following anatomical areas: cervical (n =3), sternal/parasternal (n =4), axillary (n =2), tibial (n =5), trochanteric (n =2) and sacral/gluteal (n =5). The mean age of patients (15 male and six female) was 57.8 years. Flap selection was based solely on preoperative Doppler mapping in areas adjacent to soft-tissue defects. The mean follow-up period was 1 year. Results: All flaps survived, demonstrating postoperatively acceptable aesthetic results with good patient satisfaction. The donor sites were closed primarily in 17 patients; four patients required skin grafting. Two patients required surgical revision due to flap-margin dehiscence. There was no loss of function at donor sites. Increased flap mobility could be achieved through extended perforator dissection. One perforator-based flaps offered the widest arc of rotation serving as propeller flaps. If more than one perforator vessel was preserved, flap mobility was limited, but still allowed sufficient flap movement either as a rotation or advancement flap or as a combination of both. A classification is proposed according to the number of perforator vessels preserved and to the type of flap movement. Conclusions: The concept of free-style local perforator flaps represents a safe, versatile and reliable surgical procedure. It not only offers a greater freedom in flap selection but also provides good aesthetic results. The classification proposed might aid in the decision-making process involved in order to achieve adequate results with this procedure. [Copyright &y& Elsevier]

In this study, Doppler signals recorded from the internal carotid artery (ICA) of 97 subjects were processed by personal computer using classical and model-based methods. Fast Fourier transform (classical method) and autoregressive (model-based method) methods were selected for processing the ICA Doppler signals. The parameters in the autoregressive method were found by using maximum likelihood estimation. The Doppler power spectra of the ICA Doppler signals were obtained by using these spectral 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 stenosis and occlusion in the ICA. Reliable information on haemodynamic alterations in the ICA can be obtained by evaluation of these sonograms. [ABSTRACT FROM AUTHOR]

In this paper, we model the ultrasound signal produced by blood-flow through an artery. Our model assumes a number of moving scatterers (red-blood cells), which are randomly distributed in the vessel. The Doppler effect is then introduced by changing the received signal shape in accordance with a calculated value gained from the speed of the scatterers. Variations in the shape of the Doppler spectrum during the cardiac cycle were simulated by including spectrum shape variations as an input to the model. Our proposed model is validated and shown to produce promising results when compared to previous studies. [ABSTRACT FROM AUTHOR]

Abstract: In all measurement techniques one seeks accuracy and precision. In ultrasonic Doppler velocimetry, those qualities strongly depend on signal to noise ratio of the Doppler signal and on the performance of the velocity estimator. The most widely used estimation method in ultrasonic coherent Doppler velocimetry is the pulse pair method. Its success is due to the computation efficiency of the algorithm combined to an unbiased estimator. Unfortunately, for a wide range of experimental fluid flows, the pulse pair estimation is less efficient, especially for clear water or concentrated mud where the signal to noise ratio can be very low, or for highly turbulent flows where the Doppler signal has a broad spectrum. Our approach is based on the treatment of the Doppler spectral information. It uses a simple parametric identification inspired by theoretical models and experimental observations. It acts through noise subtraction and subsequent cutting. Thus, we have developed a fast velocity estimation algorithm superior to the pulse pair one in terms of accuracy. The robustness of the method was evaluated by adding different levels of white Gaussian noise to an experimental Doppler signal. The results demonstrate an increase of noise immunity up to one decade compared to the pulse pair method. [Copyright &y& Elsevier]

Abstract: In this study, a new approach based on adaptive neuro-fuzzy inference system (ANFIS) is presented for detection of ophthalmic arterial (OA) disorders. Decision making was performed in two stages: feature extraction using the discrete wavelet transform (DWT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Four types of OA Doppler signals were used as input patterns of the four ANFIS classifiers. To improve diagnostic accuracy, the fifth ANFIS classifier (combining ANFIS) was trained using the outputs of the four ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the impacts of features on the detection of OA disorders were obtained through analysis of the ANFIS. The performance of the ANFIS classifier was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS classifier has potential in detecting the OA disorders. [Copyright &y& Elsevier]

Abstract: In this paper, we present the probabilistic neural networks (PNNs) for the Doppler ultrasound blood flow signals. The ophthalmic arterial (OA) and internal carotid arterial (ICA) Doppler signals were decomposed into time–frequency representations using discrete wavelet transform (DWT) and statistical features were calculated to depict their distribution. Decision making was performed in two stages: feature extraction by computing the wavelet coefficients and classification using the classifier trained on the extracted features. The purpose was to determine an optimum classification scheme for this problem and also to infer clues about the extracted features. Our research demonstrated that the wavelet coefficients are the features which well represent the Doppler signals and the PNNs trained on these features achieved high classification accuracies. [Copyright &y& Elsevier]

In this study, Doppler signals were recorded from the output of carotid arteries of 40 subjects and transferred to a personal computer (PC) by using a 16-bit sound card. Doppler difference frequencies were recorded from each of the subjects, and then analyzed by using short-time Fourier transform (STFT) and the continuous wavelet transform (CWT) methods to obtain their sonograms. These sonograms were then used to determine the relationships of applied methods with medical conditions. The sonograms that were obtained by CWT method gave better results for spectral resolution than the STFT method. The sonograms of CWT method offer net envelope and better imaging, so that the measurement of blood flow and brain pressure can be made more accurately. Simultaneously, receiver operating characteristic (ROC) analysis has been conducted for this study and the estimation performance of the spectral resolution for the STFT and CTW has been obtained. The STFT has shown a 80. 45% success for the spectral resolution while CTW has shown a 89. 90% success. [Copyright &y& Elsevier]

In this study, ophthalmic arterial Doppler signals were obtained from 200 subjects, 100 of whom suffered from ocular Behcet disease while the rest were healthy subjects. An adaptive neuro-fuzzy inference system (ANFIS) was used to detect the presence of ocular Behcet disease. Spectral analysis of the ophthalmic arterial Doppler signals was performed by the fast Fourier transform method for determining the ANFIS inputs. The ANFIS was trained with a training set and tested with a testing set. All these data sets were obtained from ophthalmic arteries of healthy subjects and subjects suffering from ocular Behcet disease. Performance indicators and statistical measures were used for evaluating the ANFIS. The correct classification rate was 94% for healthy subjects and 90% for unhealthy subjects suffering from ocular Behcet disease. The classification results showed that the ANFIS was effective at detecting ophthalmic arterial Doppler signals from subjects with Behcet disease. [ABSTRACT FROM AUTHOR]

Abstract: In this study, ophthalmic arterial Doppler signals recorded from 214 subjects were processed 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 performing spectral analysis of the ophthalmic arterial Doppler signals. Doppler power spectral density estimates of the ophthalmic 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 detect variabilities such as stenosis, ocular Behcet disease, and uveitis disease in the physical state of ophthalmic arterial Doppler signals. These power spectra were then used to compare the applied methods in terms of their frequency resolution and the effects in detecting stenosis, Behcet disease and uveitis disease in ophthalmic arteries. [Copyright &y& Elsevier]

This paper illustrates the use of combined neural network (CNN) models to guide model selection for diagnosis of internal carotid arterial (ICA) disorders. The ICA Doppler signals were decomposed into time-frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The first level networks were implemented for the diagnosis of ICA disorders using the statistical features as inputs. To improve diagnostic accuracy, the second level network was trained using the outputs of the first level networks as input data. The CNN models achieved accuracy rates which were higher than that of the stand-alone neural network models. [ABSTRACT FROM AUTHOR]

Methods and instruments are considered for measuring the parameters of the acoustic output from a Doppler ultrasonic detector for fetal heartbeat frequency as corresponding to the specifications of international and national standards. [ABSTRACT FROM AUTHOR]

Abstract: The aim of this study is to evaluate the diagnostic accuracy of the recurrent neural networks (RNNs) trained with Levenberg–Marquardt algorithm on the Doppler ultrasound blood flow signals. The ophthalmic arterial (OA) and internal carotid arterial (ICA) Doppler signals were decomposed into time–frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The RNNs were implemented for diagnosis of OA and ICA diseases using the statistical features as inputs. We explored the ability of designed and trained Elman RNNs, combined with wavelet preprocessing, to discriminate the Doppler signals recorded from different healthy subjects and subjects suffering from OA and ICA diseases. The classification results demonstrated that the proposed combined wavelet/RNN approach can be useful in analyzing long-term Doppler signals for early recognition of arterial diseases. [Copyright &y& Elsevier]

Abstract: Artificial neural networks (ANNs) have been used in a great number of medical diagnostic decision support system applications and within feedforward ANNs framework there are a number of established measures such as saliency measures for identifying important input features. By identifying a set of salient features, the noise in a classification model can be reduced, resulting in more accurate classification. In this study, a signal-to-noise ratio (SNR) saliency measure was employed to determine saliency of input features of multilayer perceptron neural networks (MLPNNs) used in classification of Doppler signals. The SNR saliency measure determines the saliency of a feature by comparing it to that of an injected noise feature and the SNR screening method utilizes the SNR saliency measure to select a parsimonious set of salient features. Ophthalmic and internal carotid arterial Doppler signals were decomposed into time–frequency representations using discrete wavelet transform. Input feature vectors were extracted using statistics over the set of the wavelet coefficients. The MLPNNs used in classification of the ophthalmic and internal carotid arterial Doppler signals were trained for the SNR screening method. The application results of the SNR screening method to the ophthalmic and internal carotid arterial Doppler signals demonstrated that classification accuracies of the MLPNNs with salient input features are higher than that of the MLPNNs with salient and non-salient input features. [Copyright &y& Elsevier]

Abstract: This paper presented the assessment of feature extraction methods used in automated diagnosis of arterial diseases. Since classification is more accurate when the pattern is simplified through representation by important features, feature extraction and selection play an important role in classifying systems such as neural networks. Different feature extraction methods were used to obtain feature vectors from ophthalmic and internal carotid arterial Doppler signals. In addition to this, the problem of selecting relevant features among the features available for the purpose of classification of Doppler signals was dealt with. Multilayer perceptron neural networks (MLPNNs) with different inputs (feature vectors) were used for diagnosis of ophthalmic and internal carotid arterial diseases. The assessment of feature extraction methods was performed by taking into consideration of performances of the MLPNNs. The performances of the MLPNNs were evaluated by the convergence rates (number of training epochs) and the total classification accuracies. Finally, some conclusions were drawn concerning the efficiency of discrete wavelet transform as a feature extraction method used for the diagnosis of ophthalmic and internal carotid arterial diseases. [Copyright &y& Elsevier]

Abstract: In this study, a new approach based on adaptive neuro-fuzzy inference system (ANFIS) was presented for detection of internal carotid artery stenosis and occlusion. The internal carotid arterial Doppler signals were recorded from 130 subjects that 45 of them suffered from internal carotid artery stenosis, 44 of them suffered from internal carotid artery occlusion and the rest of them were healthy subjects. The three ANFIS classifiers were used to detect internal carotid artery conditions (normal, stenosis and occlusion) when two features, resistivity and pulsatility indices, defining changes of internal carotid arterial Doppler waveforms were used as inputs. To improve diagnostic accuracy, the fourth ANFIS classifier (combining ANFIS) was trained using the outputs of the three ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the impacts of features on the detection of internal carotid artery stenosis and occlusion were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of classification accuracies and the results confirmed that the proposed ANFIS classifiers have some potential in detecting the internal carotid artery stenosis and occlusion. The ANFIS model achieved accuracy rates which were higher than that of the stand-alone neural network model. [Copyright &y& Elsevier]

Mixture of experts (ME) is a modular neural network architecture for supervised learning. This paper illustrates the use of ME network structure to guide modelling Doppler ultrasound blood flow signals. Expectation–Maximization (EM) algorithm was used for training the ME so that the learning process is decoupled in a manner that fits well with the modular structure. The ophthalmic and internal carotid arterial Doppler signals were decomposed into time–frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The ME network structures were implemented for diagnosis of ophthalmic and internal carotid arterial disorders using the statistical features as inputs. To improve diagnostic accuracy, the outputs of expert networks were combined by a gating network simultaneously trained in order to stochastically select the expert that is performing the best at solving the problem. The ME network structure achieved accuracy rates which were higher than that of the stand-alone neural network models. [Copyright &y& Elsevier]

Mitral valve stenosis is an important heart condition that develops when the mitral valve fails to open as wide as it should. In this study, we have performed Eigenvector based Music, FFT based Welch, and AR based Burg methods of Doppler signals real clinical data from patients with mitral valve stenosis and healthy volunteers. Using these methods, we have not only utilized power spectral density distribution graphs, but also facilitated Doppler sonograms and estimation of SBI to obtain medical information about mitral stenosis and determination of spectral broadening in the presence of mitral stenosis. In conclusion, this research has shown that frequency level of maximum power spectral density is distinctive and there is an advantage in using the EIG‐Music over the FFT‐Welch and AR‐Burg for the determination of SBI. Using the Music method among them, the calculation of SBI may help determine mitral valve stenosis. [ABSTRACT FROM AUTHOR]

There are a number of different quantitative models that can be used in a medical diagnostic decision support system including parametric methods (linear discriminant analysis or logistic regression), nonparametric models (k nearest neighbor or kernel density) and several neural network models. The complexity of the diagnostic task is thought to be one of the prime determinants of model selection. Unfortunately, there is no theory available to guide model selection. This paper illustrates the use of combined neural network models to guide model selection for diagnosis of ophthalmic and internal carotid arterial disorders. The ophthalmic and internal carotid arterial Doppler signals were decomposed into time–frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The first-level networks were implemented for the diagnosis of ophthalmic and internal carotid arterial disorders using the statistical features as inputs. To improve diagnostic accuracy, the second-level networks were trained using the outputs of the first-level networks as input data. The combined neural network models achieved accuracy rates which were higher than that of the stand-alone neural network models. [Copyright &y& Elsevier]

Abstract: In this study, a new approach based on an adaptive neuro-fuzzy inference system (ANFIS) was presented for detection of ophthalmic artery stenosis. The ANFIS was used to detect ophthalmic artery stenosis when two features, resistivity and pulsatility indices, defining changes of ophthalmic arterial Doppler waveforms were used as inputs. The ophthalmic arterial Doppler signals were recorded from 115 subjects, of whom 52 suffered from ophthalmic artery stenosis and the rest were healthy. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the impacts of features on the detection of ophthalmic artery stenosis were obtained through analysis of the ANFIS. The performances of the classifiers were evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS classifier has potential in detecting the ophthalmic artery stenosis. [Copyright &y& Elsevier]

Abstract: In this study, Doppler signals recorded from internal carotid artery of 80 subjects were processed by PC-computer using autoregressive method and Doppler power spectra were obtained. The parameters of autoregressive method were estimated by different estimation methods such as Yule–Walker, covariance, modified covariance, Burg, least squares, and maximum likelihood estimation. Doppler spectra were then used to compare the applied estimation methods in terms of their frequency resolution and the effects in determination of stenosis in internal carotid arteries. The Cramer–Rao bounds were derived for the estimated autoregressive parameters of the internal carotid arterial Doppler signals and the performance evaluation of the estimation methods was performed using the Cramer–Rao bound values. Finally, the optimal autoregressive spectral estimation method for the internal carotid arterial Doppler signals was selected according to the computed Cramer–Rao bound values. [Copyright &y& Elsevier]