Development of methods to analyse transcranial Doppler ultrasound signals recorded in microgravity

During space flights, several clinical syndromes may be the result of changes in cerebral circulation. The purpose of the paper is to describe the development and initial evaluation of a system for recording, processing and displaying transcranial Doppler ultrasound (TCD) waveforms from the middle cerebral artery (MCA) in microgravity. Volunteers were repeatedly subjected to 15–20 s intervals of microgravity (‘near zero gravity’) during flights on the KC-135 military aircraft. Continuous TCD recordings from the MCA were stored on magnetic tape. The paper describes the system that was developed to digitise the Doppler ultrasound data and markers that corresponded to the various levels of microgravity, obtain the maximum and mean Doppler waveforms, identify the waveforms and quantify them. The results demonstrate the feasibility of making TCD recordings in a microgravity environment and illustrate excellent performance of the system and its ease of operation. Quantitative waveform analysis of the recordings from the first subject studied in the supine position showed statistically significant changes in MCA velocity waveforms during microgravity.     

Introduction of an embolus detection system based on analysis of the transcranial Doppler audio-signal

A new embolus detection system (EDS) is presented, built with the intention of detecting ongoing cerebral embolization in patients at risk of transient ischaemic attacks or stroke. It is based on the analysis of the audio-Doppler signal of a transcranial Doppler machine. The algorithm of the EDS estimates the intensity, duration and zero-crossing dynamics of the audio signal. The EDS has a multi-layer neural network which classifies events into micro-emboli signals (MES) or artefacts. The decision-making component of the software has been validated against human experts. Data from patients in the post-operative phase of carotid surgery were used for the validation process. The results showed agreement in MES and artefact classification of > 93%. Apart from a monitoring display, the monitoring system includes a verification unit that allows the user to listen and to look at all data of individual MES and artefacts. Moreover, the system allows the user to record, store and re-calculate all data files. Data are stored using European Data Format, which allows data transportation over the Internet. The EDS may have a potential in stroke risk stratification, evaluating the effect of novel anti-thrombotic therapies, and in peri-operative and remote monitoring of carotid endarterectomy.     

Real-time system for robust spectral parameter estimation in Doppler signal analysis

In assessing the level of stenosis in extracranial Doppler analysis, spectral analysis has until now been used qualitatively, for the most part. Owing to the many variables affecting the measurements (mainly noise level and instrument setting made subjectively by the operator), the reliability of the inferences on the degree of stenosis is not clearly definable. Under such conditions the need arises for algorithms and systems that can estimate spectral parameters with a higher degree of accuracy, to verify whether reliable inferences can indeed by made or if this technique is only a qualitative one. In the paper a real-time spectral analysis system is described. The system relies on a new spectral estimation algorithm which gives estimates with good robustness with respect to noise. Moreover, a clear measurement procedure which eliminates the many subjective factors affecting the estimates has also been proposed and used. The system has been evaluated with simulated signals and in clinical trials and has shown better performance than the commonly used commercial analysers.     

Real-time CHF detection from ECG signals using a novel discretization method

This study proposes a new method, equal frequency in amplitude and equal width in time (EFiA-EWiT) discretization, to discriminate between congestive heart failure (CHF) and normal sinus rhythm (NSR) patterns in ECG signals. The ECG unit pattern concept was introduced to represent the standard RR interval, and our method extracted certain features from the unit patterns to classify by a primitive classifier. The proposed method was tested on two classification experiments by using ECG records in Physiobank databases and the results were compared to those from several previous studies. In the first experiment, an off-line classification was performed with unit patterns selected from long ECG segments. The method was also used to detect CHF by real-time ECG waveform analysis. In addition to demonstrating the success of the proposed method, the results showed that some unit patterns in a long ECG segment from a heart patient were more suggestive of disease than the others. These results indicate that the proposed approach merits additional research.     

Spectral broadening of ophthalmic arterial Doppler signals using STFT and wavelet transform

In this study, short-time Fourier transform (STFT) and wavelet transform (WT) were used for spectral analysis of ophthalmic arterial Doppler signals. Using these spectral analysis methods, 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 sonograms were then used to compare the applied methods in terms of their frequency resolution and the effects in determination of spectral broadening in the presence of ophthalmic artery stenosis. A qualitative improvement in the appearance of the sonograms obtained using the WT over the STFT was noticeable. Despite the qualitative improvement in the individual sonograms, no quantitative advantage in using the WT over the STFT for the determination of spectral broadening index was obtained due to the poorer variance of the wavelet transform-based spectral broadening index and the additional computational requirements of the wavelet transform.     

Detection of early atherosclerosis by analysis of ultrasonic Doppler signals produced by mural flow disturbances

The paper describes an in vitro study using a multi-gate Doppler ultrasound system to investigate flow disturbances in a blood analogue caused by small stenoses (2–25% cross-sectional area reduction), using steady flow (100–600 ml min⁻¹) in a 6 mm diameter rigid artery model. The results indicate that stenoses greater than 5% were detectable.     

Probabilistic neural networks employing Lyapunov exponents for analysis of Doppler ultrasound signals

The implementation of probabilistic neural networks (PNNs) with the Lyapunov exponents for Doppler ultrasound signals classification is presented. This study is directly based on the consideration that Doppler ultrasound signals are chaotic signals. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. Decision making was performed in two stages: computation of Lyapunov exponents as representative features of the Doppler ultrasound signals and classification using the PNNs trained on the extracted features. The present research demonstrated that the Lyapunov exponents are the features which well represent the Doppler ultrasound signals and the PNNs trained on these features achieved high classification accuracies.     

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.     

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.     

Online measurement of cardiac indices from frequency transformed TAV Doppler ultrasound signals

A system is described for the analysis of blood flow signals in the aortic artery which enables indices of stroke volume and cardiac output to be derived. A commercial Doppler ultrasound monitor is used, the demodulated return signals are digitised, and frequency analysis is performed in real time using an FFT signal processing circuit. A Z-80 microprocessor controls the synchronisation of the data acquisition, transformation and display of the signals. Algorithms have been developed to identify the maximum velocity profile of each heartbeat and perform the necessary calculations to produce indices of stroke volume and cardiac output. The system has been evaluated against an existing offline method for a series of recordings on normal subjects and has demonstrated good repeatability. It has also been used in a clinical study on the effects of anaesthesia on chronic spinal injury patients. The results have shown that the system can be used to follow serial changes in cardiac performance within individual subjects.     

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.     

超声多普勒血流和管壁搏动信号的分离研究

目的针对超声多普勒血流检测中,传统的高通滤波法在滤除管壁搏动信号的同时也会滤除低频血流信号的问题,本研究提出一种以心电信号（electrocardiography,ECG）作为参考信号的自适应滤波的方法消除管壁干扰。方法包括两方面：其一,采用心电信号作为参考信号对超声多普勒信号进行自适应滤波;其二,采用多级自适应滤波并选择不同的参考信号的滤波方案。分别使用上述方法和高通滤波法对仿真的超声多普勒信号进行处理,并将结果进行比较。结果与传统的高通滤波法相比,该方法在有效抑制管壁搏动信号的同时保留一部分低频血流信号成分。结论该方法能较准确地提取出完整的血流超声多普勒信号,具有一定的临床应用价值。     

基于微电子阻抗技术实时细胞监测系统在细胞凋亡检测时点选择中的应用

目的 探讨基于微电子阻抗技术的实时细胞监测系统用于确定细胞凋亡最佳检测时间点.方法 应用顺铂诱导胶质瘤细胞U251凋亡,使用实时细胞监测系统连续74 h监测不同药物浓度作用于U251后细胞指数的动态变化,根据药物作用时间与细胞指数关系曲线确定凋亡最佳检测时间点,采用流式细胞仪分析细胞凋亡率.结果 通过实时监测发现,2.5μg/mL、5 μg/mL、10μg/mL顺铂分别在作用49 h、60 h和74 h,U251细胞指数值达到最低;在这些时间点,流式细胞仪检测细胞凋亡率分别达到高峰(15.6±1.4)％,(34.2±7.6)％,(63.9±4.6)％.结论 应用实时细胞监测系统可进行细胞凋亡最佳检测时间点的辅助判断.     

Empirical Mode Decomposition of simulated and real ultrasonic Doppler signals of periodic fetal activity

Simulated signals comprising components (trains of Gaussian packets) resulting from cardiac movements and from pseudorespiratory movements with added white noise were submitted to Empirical Mode Decomposition. The increase of sampling frequency fs (from 0.5 kHz to 5 kHz) for given signal to noise ratio SNR moves signal components toward higher order intrinsic mode functions (IMFs) and increases their number. The increase of the SNR (from −5 dB to 10 dB, fixed fs) moves the signal components to lower order IMFs. The separation of components is most efficient for SNR ≥ 5 dB and fs not exceeding 1 kHz, for lower SNRs fs should be at least 2 kHz. SNR = ∞ results in erroneous decomposition and therefore limited noise level is beneficial. Recommended number of sifting iterations is 10. Fetal data obtained using 2 MHz emission frequency and sampled at 2 kHz were decomposed. The cardiac signal always appears in IMF3, frequently also in IMF1 and IMF2. The pseudobreathing signal, appearing mainly in IMF4-6, is easy to separate. Signals resulting from fetal displacements due to maternal respiration appear in IMF7 or IMF8. The EMD performs better than the classic linear filtering as a tool for separation of the pseudorespiration signals and provides inferior results in terms of separation of the cardiac signals.     

Quantification of Doppler signal in polycystic ovary syndrome using three-dimensional power Doppler ultrasonography: a possible new marker for diagnosis.

BACKGROUND: Differences in the ovarian stromal blood flow of women embarking on an IVF treatment, as assessed on day 2 or 3 of the menstrual cycle, using three-dimensional (3D) power Doppler ultrasonography to quantify the blood flow and vascularization, were compared. METHODS: The women were divided into two groups: group 1, 54 women with regular, ovulatory menstrual cycles and normal ovaries on ultrasound scan; and group 2, 25 women with polycystic ovary syndrome (PCOS). RESULTS: The quantification of Doppler signal in the ovarian stroma appeared to be greater in the PCOS group compared with the normal group. The mean of ovarian volume was significantly higher (P < 0.05) in women with PCOS compared with the normal ovaries. The vascularization flow index (VFI), flow index (FI), and vascularization index (VI), were significantly higher (P < 0.05) in the women with PCOS compared with the women with normal ovaries. CONCLUSIONS: This observation may help to explain the excessive response often seen during gonadotrophin administration in women with PCOS. We believe that a quantification study of the vascular flow, including the VI, FI, and VFI of the entire ovarian stroma using 3D power Doppler, is more accurate than the previously reported quantification analysis using 2D imaging, and may be a new parameter to assist in the ultrasound diagnosis of PCOS.     

A new pulsatile flow visualization method using a photochromic dye with application to Doppler ultrasound

A nonintrusive method for the visualization of pulsatile flow velocity profiles is described. The method is based on the use of a photochromic dye that is added to the fluid being studied and a nitrogen laser which excites the dye producing a marker “line” whose movement can be photographed. A microcomputer is used as a system controller, to coordinate the system timing and to manage the data transfer. The method used for analysis of the photographs to determine the velocity profiles is described. Examples are presented of instantaneous velocity profiles obtained from velocity waveforms that are similar to those of the femoral artery. In addition, application of the system for studying the relationship between Doppler ultrasound spectral recordings and flow velocity profiles is discussed.     

多普勒血流信号中频谱展宽效应产生的原因及其影响因素

在连续波多普勒血流测量系统中，实际接收到的频移信号是血流束祺矢量的综合反映，是速度矢量在各方面上产生频移信号的叠加，本文建立一个模型来分析在连续波多产为勒血流测量系统中频谱展宽产生的原因以及其影响因素，并且对于圆形探头给出了详细的讨论结果。     

Frequency analysis of high-intensity transient signals of transcranial Doppler ultrasound in patients supported with a left ventricular assist device

Left ventricular assist devices (LVADs) have become more important than ever for patients with terminal heart disease. The chronic shortage of donors for heart transplantation may enhance the importance of LVADs as destination therapy. To prevent cerebral complications, the evaluation of high-intensity transient signals (HITS) of transcranial Doppler (TCD) ultrasound is important. In the present study, HITS were measured on seven occasions in patients supported with LVADs. The high-frequency counts in HITS were greater than the low-frequency counts (47 ± 42 vs. 9 ± 9 in 3 min, P = 0.025), and declined further with oxygen delivery to the patient. The high frequency of signals may imply air microemboli, and the counts may increase with time. Presented in part at the 2007 Joint Congress of the 45th Annual Conference of the Japanese Society for Artificial Organs and the 2nd Annual Conference of the International Federation for Artificial Organs, Osaka     

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.