Effect of ensemble averaging on amplitude and feature variabilities of Doppler spectrograms recorded in the lower limb arteries

The objective of the present study was to analyse the effect of averaging Doppler blood flow signals in lower limb arteries on amplitude and feature variabilities. Doppler signals recorded in 41 iliac and 35 superficial femoral arteries having different categories of stenosis were averaged over 1–15 cardiac cycles. Based on the relative decreasing rate of an index of variability, results indicated that amplitude variability of the spectrograms was exponentially reduced to 30, 6 and 1 per cent when averaging five, ten and 15 cardiac cycles, respectively. Nine diagnostic features were extracted from Doppler spectrograms and their variations from one cardiac cycle to the next quantified. Based on the relative decreasing rate of these variations, results indicated that feature variability was exponentially reduced to 30, 4 and 1 per cent when averaging five, ten and 15 cardiac cycles, respectively. The effect of averaging on the discriminant power of the features to separate the different categories of stenosis was also investigated by performing t-test analyses. Results showed that averaging between five and ten cardiac cycles provided the better discriminant power for most cases, whereas averaging over more than ten cardiac cycles was of little benefit. Based on the spectral analysis technique used in the present study, we conclude that averaging over ten cardiac cycles is sufficient for the analysis of Doppler spectrograms recorded in the lower limbs.     

音频多普勒信号的多特征分类决策

本文将音频多普勒信号的几种特征提取方法结合起来 ,用多元分析的方法对血流状况进行多特征的分类决策 ,所用的特征提取方法包括 :传统的声谱参数法 ,音频信号的零极点模型法 ,分形特征分析法及Teager能量法。将这种多特征分类决策法用于 71例胎儿脐血流音频多普勒信号的分析 ,结果表明 :该方法对胎儿宫内异常等疾病具有敏感性 ,可望为医学临床提供辅助诊断手段     

The effect of averaging cardiac Doppler spectrograms on the reduction of their amplitude variability

The effect of averaging cardiac Doppler spectrograms on the reduction of their amplitude variability was investigated in 30 patients. Beat-to-beat variations in the amplitude of Doppler spectrograms were also analysed. The quantification of amplitude variability was based on the computation of the area under the absolute value of the derivative function of each spectrum composing mean spectrograms. Fast Fourier transform using a Hanning window was used to compute Doppler spectra. Results obtained over systolic and diastolic periods showed that the reduction of amplitude variability followed an exponentially decreasing curve characterised by the equation f(r)=100e^−β(r−1), where r is the number of cardiac cycles, β the exponentially decreasing rate, and 100 the normalised variability for r=1. In systole, the decreasing rate β was 0·165, whereas in diastole it was 0·225. Reductions of the variability in systole for a number of cardiac cycles of 5, 10, 15, and 20 were 48, 77, 90 and 96 per cent, respectively. In diastole, reductions of the variability for the same numbers of cardiac cycles were 59, 87, 96 and 99 per cent, respectively. Based on these results, it can be concluded that no significant improvement in the reduction of amplitude variability may be obtained by averaging more than 20 cardiac cycles.     

小波变换去噪方法在多谱勒胎儿心率提取中的应用研究

为了从超声多谱勒胎音信号中提取胎音信号，获得平滑的胎心率曲线，并计算胎心率，必须去除信号提取过程中的各种干扰和噪声。对平均频移算法获得的多谱勒信号的平均频率曲线采用小波变换中的五层强制去噪方法，使后续的相关计算能得到平滑的胎音信号，从而方便计算胎儿的心率。通过对实际超声多谱勒胎音信号的处理，获得了较好的结果，提取出了稳定、平滑的胎心率曲线，较准确地计算出了胎心率。     

Heterogeneous computer network for real-time hemodynamic signal processing

A computer network is described that allows real-time processing, graphical monitoring and off-line analysis of blood pressure, nervous activity and Doppler signals recorded in conscious rats. Real-time processing is performed by an acquisition station using a powerful microprocessor, allowing extraction and storage of several characteristic parameters from each cardiac cycle and real-time graphical monitoring. The experimenter can thereby follow the time evolution of the hemodynamic parameters. Experimental data are sent through the local network to a workstation that ensures off-line processing such as chronograms, histograms and statistical analyses.     

An automated feature extraction and emboli detection system based on the PCA and fuzzy sets

The Doppler ultrasound technique is commonly used to detect emboli in the cerebral circulation. Here an automated feature extraction and emboli detection system is proposed based on the principal components analysis (PCA) and fuzzy sets. In the system, two features, R(ry) and k, are extracted by the PCA method. Meanwhile, MMR and sigma(f min) are obtained with the traditional temporal processing and spectrogram analysis, respectively. Normal blood flow signals are firstly distinguished from abnormal signals by MMR. Then signals containing emboli and disturbance noises are further differentiated by other features based on fuzzy sets. From experiments with computer-simulated and clinical Doppler ultrasound signals, it is shown that features extracted from the PCA method achieve better classification performance than those of traditional methods. The fuzzy-based detection system not only obtains high classification accuracy but is more applicable in clinical diagnosis.     

Performance of short-time spectral parametric methods for reducing the variance of the Doppler ultrasound mean instantaneous frequency estimation

To achieve an accurate estimation of the instantaneous turbulent velocity fluctuations downstream of prosthetic heart valves in vivo, the variability of the spectral method used to measure the mean frequency shift of the Doppler signal (i.e. the Doppler velocity) should be minimised. This paper investigates the performance of various short-time spectral parametric methods such as the short-time Fourier transform, autoregressive modelling based on two different approaches, autoregressive moving average modelling based on the Steiglitz-McBride method, and Prony's spectral method. A simulated Doppler signal was used to evaluate the performance of the above mentioned spectral methods and Gaussian noise was added to obtain a set of signals with various signal-to-noise ratios. Two different parameters were used to evaluate the performance of each method in terms of variability and accurate matching of the theoretical Doppler mean instantaneous frequency variation within the cardiac cycle. Results show that autoregressive modelling outperforms the other investigated spectral techniques for window lengths varying between 1 and 10 ms. Among the autoregressive algorithms implemented, it is shown that the maximum entropy method based on a block data processing technique gives the best results for a signal-to-noise ratio of 20 dB. However, at 10 and 0 dB, the Levinson-Durbin algorithm surpasses the performance of the maximum entropy method. It is expected that the intrinsic variance of the spectral methods can be an important source of error for the estimation of the turbulence intensity. The range of this error varies from 0.38% to 24% depending on the parameters of the spectral method and the signal-to-noise ratio.     

A computer simulation model for Doppler ultrasound signals from pulsatile blood flow in stenosed vessels

A computer simulation model based on an analytic flow velocity distribution is proposed to generate Doppler ultrasound signals from pulsatile blood flow in the vessels with various stenosis degrees. The model takes into account the velocity field from pulsatile blood flow in the stenosed vessels, sample volume shape and acoustic factors that affect the Doppler signals. By analytically solving the Navier-Stokes equations, the velocity distributions of pulsatile blood flow in the vessels with various stenosis degrees are firstly calculated according to the velocity at the axis of the circular tube. Secondly, power spectral density (PSD) of the Doppler signals is estimated by summing the contribution of all scatterers passing through the sample volume grouped into elemental volumes. Finally, Doppler signals are generated using cosine-superposed components that are modulated by the PSD functions that vary over the cardiac cycle. The results show that the model generates Doppler blood flow signals with characteristics similar to those found in practice. It could be concluded that the proposed approach offers the advantages of computational simplicity and practicality for simulating Doppler ultrasound signals from pulsatile blood flow in stenosed vessels.     

Improvement in Doppler ultrasound human foetal heart rate records by signal correlation

An improved method for the determination of foetal heart rate from Doppler ultrasound signals is described and evaluated. It determines the most probable pulse interval from the recurrence times of multiple echoes generated by each cardiac pulse. The method, when optimised, is shown to offer an improvement over current systems, especially in reduced signal loss.     

Performance of short-time spectral parametric methods for reducing the variance of the Doppler ultrasound mean instantaneous frequency estimation

To achieve an accurate estimation of the instantaneous turbulent velocity fluctuations downstream of prosthetic heart valves in vivo, the variability of the spectral method used to measure the mean frequency shift of the Doppler signal (i.e. the Doppler velocity) should be minimised. This paper investigates the performance of various short-time spectral parametric methods such as the short-time Fourier transform, autoregressive modelling based on two different approaches, autoregressive moving average modelling based on the Steiglitz-McBride method, and Prony's spectral method. A simulated Doppler signal was used to evaluate the performance of the above mentioned spectral methods and Gaussian noise was added to obtain a set of signals with various signal-to-noise ratios. Two different parameters were used to evaluate the performance of each method in terms of variability and accurate matching of the theoretical Doppler mean instantaneous frequency variation within the cardiac cycle. Results show that autoregressive modelling outperforms the other investigated spectral techniques for window lengths varying between 1 and 10 ms. Among the autoregressive algorithms implemented, it is shown that the maximum entropy method based on a block data processing technique gives the best results for a signal-to-noise ratio of 20 dB. However, at 10 and 0 dB, the Levinson-Durbin algorithm surpasses the performance of the maximum entropy method. It is expected that the intrinsic variance of the spectral methods can be an important source of error for the estimation of the turbulence intensity. The range of this error varies from 0.38% to 24% depending on the parameters of the spectral method and the signal-to-noise ratio.     

Classification of carotid artery stenosis of patients with diabetes by neural network and logistic regression

The blood flow hemodynamics of carotid arteries were obtained from carotid arteries of 168 individuals with diabetes using the 7.5 MHz ultrasound Doppler M-unit. Fast Fourier Transform (FFT) methods were used for feature extraction from the Doppler signals on the time-frequency domain. The parameters, obtained from the Doppler sonograms, were applied to the mathematical models that were constituted to analyze the effect of diabetes on internal carotid artery (ICA) stenosis. In this study, two different mathematical models such as the traditional statistical method based on logistic regression and a Multi-Layer Perceptron (MLP) neural network were used to classify the Doppler parameters. The correct classification of these data was performed by an expert radiologist using angiograpy before they were executed by logistic regression and MLP neural networks. We classified the carotid artery stenosis into two categories such as non-stenosis and stenosis and we achieved similar results (correctly classified (CC) = 92.8%) in both mathematical models. But, as the degree of stenosis had been increased to 4 (0-39%, 40-59%, 60-79% and 80-99% diameter stenosis), it was found that the neural network (CC = 73.9%) became more efficient than the logistic regression analysis (CC = 67.7%). These outcomes indicate that the Doppler sonograms taken from the carotid arteries may be classified successfully by neural network.     

脉冲多普勒超声速度频谱图像中速度脉动信号提取方法探讨

针对目前脉冲多普勒超声（PDU）速度频谱图像中速度及其脉动信号提取方法存在的数据量不足、工作量大和可重复性低的情况，本研究结合前期研究获得的成果和PDU速度频谱构成的基本原理，在Visual Basics平台上，初步设计开发了一套具有一定自动化程度的PDU速度频谱图像处理软件。其速度、雷诺正应力（RNS）的计算结果与粒子成像流速仪（PIV）检测结果之间具有较好的相关性（r=0．93和0．78）。研究结果表明：该软件在一定程度上增加了信号处理的有效数据量、降低了工作量，提高了结果的可重复性。可以成为今后临床医学超声速度频谱图像分析的一种有效的手段。     

Feature extraction from Doppler ultrasound signals for automated diagnostic systems

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.     

Application of FFT analyzed cardiac Doppler signals to fuzzy algorithm

Doppler signals, recorded from the output of tricuspid, mitral, and aorta valves of 60 patients, were transferred to a personal computer via 16-bit sound card. The fast Fourier transform (FFT) method was applied to the recorded signal from each patient. Since FFT method inherently cannot offer a good spectral resolution at highly turbulent blood flows, it sometimes leads to wrong interpretation of cardiac Doppler signals. In order to avoid this problem, firstly six known diseased heart signals such as hypertension, mitral stenosis, mitral failure, tricuspid stenosis, aorta stenosis, aorta insufficiency were introduced to fuzzy algorithm. Then, the unknown heart diseases from 15 patients were applied to the same fuzzy algorithm in order to detect the kinds of diseases. It is observed that the fuzzy algorithm gives true results for detecting the kind of diseases.     

New reactive hyperaemia parameters computed thanks to wavelets in laser Doppler studies

Many studies have been carried out on reactive hyperaemia parameters in laser Doppler flowmetry (LDF) analysis. For the diagnosis of peripheral arterial occlusive diseases, the most important parameters are the time to peak flow (tpLDF) and the peak flow value (pLDF). However, the determination of these parameters is very often subjective due to the presence of marked oscillations on LDF signals. The present work shows that the de-noising of the concentration of moving blood cell signals (CMBC) can be of great interest in determining new reliable reactive hyperaemia parameters (tpCMBC and pCMBC). 40 CMBC signals recorded on 10 healthy subjects and on 10 patients with peripheral arterial occlusive diseases are thus smoothed using wavelets. For a 1 min occlusion, the parameters computed are tpCMBC_healthy = 20.9 s, pCMBC_healthy = 40.3 arbitrary units (a.u.), tpCMBC_patient = 31.2 S, pCMBC_patients = 31.5 a.u. After 2 min of occlusion, tpCMBC_healthy = 28.7 s, pCMBC_healthy = 61.0 a.u., tpCMBC_patient = 38.8 s, pCMBC_patient = 41.7 a.u. The 2 new parameters, tpCMBC and pCMBC, are computed on de-noised signals. Therefore, they can improve the diagnosis of peripheral arterial occlusive diseases.     

The information content of Doppler ultrasound signals from the fetal heart

Knowledge of the content of Doppler ultrasound signals from the fetal heart is essential if the performance of fetal heart rate (FHR) monitors based upon this technology is to be improved. For this reason instrumentation was constructed to enable the simultaneous collection of Doppler audio signals and the transabdominal fetal ECG (for signal registration), with a total of 22 recordings being made with an average length of around 20 minutes. These data demonstrate the transient nature of the Doppler audio data with wide variations in the signal content observable on a beat-to-beat basis. Short-time Fourier analysis enabled the content of the Doppler signals to be linked to six cardiac events, four valve and two wall motions, with higher frequency components being associated with the latter. This differing frequency content together with information regarding the direction of movement that can be discerned from Doppler signals provided a potential means of discriminating between these six events (which are unlikely to all contribute to the Doppler signal within the same cardiac cycle). Analysis of 100 records showed that wall contractions generate the most prominent signals, with atrial contraction recognisable in all records and ventricular wall contraction in 95% (although its amplitude is only around 30% of that of the atrial signal). Valve motion, with amplitudes between 15 and 25% that of the atrial wall signal, were visible in 75% of records. These results suggest means by which the six events that contribute to the Doppler signal may be distinguished, providing information that should enable an improvement in the current performance of Doppler ultrasound-based FHR monitors.     

Usage of eigenvector methods to improve reliable classifier for Doppler ultrasound signals

A new approach based on the implementation of the automated diagnostic systems for Doppler ultrasound signals classification with the features extracted by eigenvector methods is presented. In practical applications of pattern recognition, there are often diverse features extracted from raw data which needs recognizing. Because of the importance of making the right decision, the present work is carried out for searching better classification procedures for the Doppler ultrasound signals. Decision making was performed in two stages: feature extraction by the eigenvector methods and classification using the classifiers trained on the extracted features. The aim of the study is classification of the Doppler ultrasound signals by the combination of eigenvector methods and the classifiers. The present research demonstrated that the power levels of the power spectral density (PSD) estimates obtained by the eigenvector methods are the features which well represent the Doppler ultrasound signals and the probabilistic neural networks (PNNs), recurrent neural networks (RNNs) trained on these features achieved high classification accuracies.     

Detection of fetal breathing and cardiac movements and rhythms in ultrasonic Doppler signal recorded on the surface of the maternal abdomen

It is proposed to use information on the direction of reflector movement and extensive filtering in the detection of fetal breathing and cardiac movements in the ultrasonic Doppler signal recorded on the surface of the material abdomen. The method appears fairly insensitive to spurious signals and allows those of interest to be distinguished without any reference technique. A decision rule for breathing and cardiac rhythm detection, incorporating movement direction, amplitude, shape and periodicity criteria, is also proposed.     

Analysis and processing of laser Doppler perfusion monitoring signals recorded from the beating heart

Laser Doppler perfusion monitoring (LDPM) can be used for monitoring myocardial perfusion in the non-beating heart. However, the movement of the beating heart generates large artifacts. Therefore the aim of the study was to develop an LDPM system capable of correlating the laser Doppler signals to the cardiac cycle and to process the signals to reduce the movement artifacts. Measurements were performed on three calves, both on the normal beating heart and during occlusion of the left anterior descending coronary artery (LAD). The recorded LDPM signals were digitally processed and correlated to the sampled ECG. Large variations in the output (perfusion) and DC signals during the cardiac cycle were found, with average coefficients of variation of 0.36 and 0.14 (n-14), respectively. However, sections with a relatively low, stable output signal were found in late diastole, where the movement of the heart is at a minimum. Occlusion of the LAD showed the importance of recording the laser Doppler signals at an appropriate point in the cardiac cycle, in this case late systole, to minimise movement artifacts. It is possible to further reduce movement artifacts by increasing the lower cutoff frequency when calculating the output signal.     

The PULSAR Radar Instrument and Its Use for Comprehensive Examination of Health Status

Medical application of the PULSAR Doppler radar millimeter-wavelength range measuring system is discussed. The possibility for remote measurement and analysis of heart rate and equilibrium function parameters is demonstrated. Various physiological parameters are measured synchronously in the single beam of the radar. To estimate the person’s functional condition, use of fractal parameters defined on phase plane of normal and pathological zones is suggested. An adaptable method for processing signals reflected from the patient is offered. The method reduces measurement errors.