Estimation of aortic compliance using magnetic resonance pulse wave velocity measurement

A method for compliance estimation employing magnetic resonance pulse wave velocity measurement is presented. Time-resolved flow waves are recorded at several positions along the vessel using a phase contrast sequence, and pulse wave velocity is calculated from the delay of the wave onsets. Using retrospective cardiac gating in combination with an optically decoupled electrocardiogram acquisition, a high temporal resolution of 3 ms can be achieved. A phantom set-up for the simulation of pulsatile flow in a compliant vessel is described. In the phantom, relative errors of pulse wave velocity estimation were found to be about 15%, whereas in a volunteer, larger errors were found that might be caused by vessel branches. Results of pulse wave velocity estimation agree with direct aortic distension measurements which rely on a peripheral estimate of aortic pressure and are therefore less accurate. Studies in 12 volunteers show values of pulse wave velocity consistent with the literature; in particular the well-known increase in pulse wave velocity with age was observed. Preliminary results show that the method can be applied to aortic aneurysms.     

Non-linear analysis of the arterial pulsatile flow: assessment of a model allowing a non-invasive ultrasonic functional exploration.

Ultrasonic measurements and modelling of blood flow in large vessels allows non-invasive evaluation of clinically interesting hemodynamic variables. To this aim, a non-linear mathematical model for the pulsatile arterial flow is proposed using the approximation of "local flow" theory. The model requires only measurements of instantaneous radius and centre-line blood velocity, and the knowledge of the tube distensibility to calculate blood velocity profiles, pressure gradient and wall shear stress. Evaluation of the proposed model using experimental data obtained from the literature proved that it can provide reliable results. In addition, as shown by assessing significance of various non-linear terms, results did not significantly change when a linear pressure-radius relationship was used instead of a non-linear relationship. Also, the model was found to be moderately sensitive to arterial tapering. Thus, the proposed model is suitable for a non-invasive clinical arterial exploration since it only requires three measurements which can be easily and precisely obtained in vivo using ultrasonic methods: the instantaneous radius, the centre-line velocity and the mean pulse wave velocity, this last variable characterizing the tube distensibility when assuming a linear pressure-radius relationship.     

基于神经网络的脉搏波信号血压检测算法

脉搏波信号蕴含大量的人体生理与病理信息,与血压的变化息息相关,利用其特征参数可以无创连续检测血压。神经网络因其极强的学习能力、泛化能力以及可以充分逼近任意复杂的非线性关系而被应用于脉搏波血压提取算法中。本研究介绍了脉搏波特征参数,并简述了基于脉搏波特征参数进行血压测量的研究进展,详细叙述了基于神经网络的脉搏波特征参数血压检测算法,最后对不同神经网络模型的优缺点进行分析,并对基于神经网络的脉搏波特征参数血压监测算法的研究方向进行展望。     

Unconstrained detection of respiration rhythm and pulse rate with one under-pillow sensor during sleep

A completely non-invasive and unconstrained method is proposed to detect respiration rhythm and pulse rate during sleep. By employing wavelet transformation (WT), waveforms corresponding to the respiration rhythm and pulse rate can be extracted from a pulsatile pressure signal acquired by a pressure sensor under a pillow. The respiration rhythm was obtained by an upward zero-crossing point detection algorithm from the respiration-related waveform reconstructed from the WT 2⁶ scale approximation, and the pulse rate was estimated by a peak point detection algorithm from the pulse-related waveform reconstructed from the WT 2⁴ and 2⁵ scale details. The finger photo-electric plethysmogram (FPP) and nasal thermistor signals were recorded simultaneously as reference signals. The reference pulse rate and respiration rhythm were detected with the peak and upward zero-crossing point detection algorithm. This method was verified using about 24 h of data collected from 13 healthy subjects. The results showed that, compared with the reference data, the average error rates were 3.03% false negative and 1.47% false positive for pulse rate detection in the extracted pulse waveform. Similarly, 4.58% false negative and 3.07% false positive were obtained for respiration rhythm detection in the extracted respiration waveform. This study suggests that the proposed method is suitable, in sleep monitoring, for the diagnosis of sleep apnoea or sudden death syndrome.     

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.     

A Novel Approach to Monitor Nonstationary Dynamics in Physiological Signals: Application to Blood Pressure,Pulse Oximeter,and Respiratory Data

We propose a parametric time-varying (TV) algorithm which utilizes sinusoids as the basis functions which are then projected onto sets of Legendre and Walsh functions for the purpose of monitoring nonstationary dynamics. The proposed algorithm is a general-purpose algorithm that has the potential to be widely applicable to various physiological signals, but is especially well-suited for tracking blood pressure (BP), pulse oximeter, and respiratory signals, as they all exhibit periodic oscillations with TV dynamics. The proposed algorithm’s efficacy was verified using both simulation examples and application to experimental data from all of the above-mentioned sources. Our results show that the method can: (1) accurately monitor abrupt frequency changes even when the data are contaminated with significant noise, (2) accurately monitor the BP and pulse oximeter signals, and (3) provide accurate estimation of respiratory rates derived directly from pulse oximeter recordings.     

脉搏波的无创检测方式

脉搏波的波形特征与心血管疾病密切相关,其检测方式和传感器的选择都会对脉搏波的检测结果产生重大影响。目前脉搏波无创检测用传感器类型包括压力传感器、压电传感器和光电传感器(反射式和透射式)。检测方式有桡动脉压力脉搏波检测和指端容积脉搏波检测。实际运用结果显示不同的传感器有不同的性能指标和适应范围,检测方式也会对脉搏波的检测产生很大影响。尽管压电传感器和光电传感器都能采集信号良好的脉搏波,但压力传感器由于误差大、强噪声和检测部位难寻等原因已经逐渐被淘汰,此外临床实验证实了桡动脉压力脉搏波检测容易受检测部位和检测个体的影响,指端容积脉搏波具有检测稳定、重复性好、易于操作等优点。     

Feature extraction of ECG signal

This paper deals with new approaches to analyse electrocardiogram (ECG) signals for extracting useful diagnostic features. Initially, elimination of different types of noise is carried out using maximal overlap discrete wavelet transform (MODWT) and universal thresholding. Next, R-peak fiducial points are detected from these noise free ECG signals using discrete wavelet transform along with thresholding. Then, extraction of other features, viz., Q waves, S waves, P waves, T waves, P wave onset and offset points, T wave onset and offset points, QRS onset and offset points are identified using some rule based algorithms. Eventually, other important features are computed using the above extracted features. The software developed for this purpose has been validated by extensive testing of ECG signals acquired from the MIT-BIH database. The resulting signals and tabular results illustrate the performance of the proposed method. The sensitivity, predictivity and error of beat detection are 99.98%, 99.97% and 0.05%, respectively. The performance of the proposed beat detection method is compared to other existing techniques, which shows that the proposed method is superior to other methods.     

基于指尖视频图像的自适应脉搏信号提取算法研究

针对手指视频图像R分量饱和失真现象,本文提出了一种基于迭代的阈值分割算法,自适应生成R分量待检测区域,通过计算待检测区域灰度均值,从而提取出人体脉搏信号。原始脉搏信号存在基线漂移及高频噪声,结合脉搏信号特征,设计了零相位数字滤波器来滤除噪声干扰。在不同智能手机上采集了指尖视频图像,利用本文提出的算法提取出了待检测区域。考虑到每次测量时指尖压力会有所不同,本文对不同压力下提取的脉搏信号做了对比分析。为了验证本文提出的算法在心率检测方面的准确性,做了心率检测对比实验。结果表明,本文提出的算法能准确提取出人体心率信息,同时具备一定的可移植性,为进一步在智能手机平台上开发生理监测应用提供了一定的理论帮助。     

Objective motor response onset detection in surface myoelectric signals

Precise detection of discrete motor events like the onsets of voluntary muscle contractions is a prerequisite for various psychophysiological approaches in sensorimotor system analysis. In biomedical research and clinical diagnosis, motor events frequently are determined from surface electromyographic (SEMG) signals by some computerized detection algorithm. However, little is known about the reliability and accuracy of these methods, which frequently rely on intuitive and heuristic criteria. Therefore, the systematic approach to computerized detection of discrete motor events from SEMG signals presented by this paper fills a basic gap in EMG signal processing. Based upon a dynamic process model for the SEMG signal, a formal detection scheme is established which incorporates the essential processing modules common to the majority of algorithms. In addition, using concepts of statistically optimal change detection in random processes, a new model-based algorithm is presented which serves as a reference for optimum performance. The validity of this concept is demonstrated for the specific example of accurate detection of muscle activation onsets in rapid voluntary contractions; the estimation error (i.e., the deviation between estimated and "true" onset time) was evaluated by statistical simulations for three representative methods. Results show a substantial decrease of performance of traditional methods in the case of highly variable dynamic muscle activation profiles and/or superimposed activation patterns (e.g., due to a secondary motor task simultaneously executed by the same muscle). The model-based approach provided significantly more accurate results, even when the exact model parameters were unknown but estimated from the SEMG signal actually measured. It is concluded that the detection algorithm has to be critically taken into consideration during interpretation of motor events resolved from SEMG signals. The process model together with the corresponding statistically optimal detector represents an efficient tool for selecting appropriate detection algorithms for a particular experimental condition, and it allows a quantitative assessment of their performance.     

Unsupervised movement onset detection from EEG recorded during self-paced real hand movement

This article presents an unsupervised method for movement onset detection from electroencephalography (EEG) signals recorded during self-paced real hand movement. A Gaussian Mixture Model (GMM) is used to model the movement and idle-related EEG data. The GMM built along with appropriate classification and post processing methods are used to detect movement onsets using self-paced EEG signals recorded from five subjects, achieving True–False rate difference between 63 and 98%. The results show significant performance enhancement using the proposed unsupervised method, both in the sample-by-sample classification accuracy and the event-by-event performance, in comparison with the state-of-the-art supervised methods. The effectiveness of the proposed method suggests its potential application in self-paced Brain-Computer Interfaces (BCI).     

基于脉搏波传导时间的血压测量方法

目的:设计一种连续血压测量方法,在无创的条件下能够实时监测受试者的血压。方法:对耳后动脉和趾背动脉的脉搏波进行特征识别,计算两个脉搏波之间的传播时间,并根据受试者的血液密度、血管内径、血管壁厚度等参数计算脉搏波传导速度,然后在传统脉搏波传导时间算法的基础上,增加受试者身高和体重,计算出人体的血压。结果:该算法得到的血压结果与真实值较为接近,并且实时性较好。结论:改进后的基于脉搏波传导时间的血压测量方法可用于血压的实时测量,为临床诊断提供帮助。     

Non-invasive measurement of pulmonary arterial pressure: I. A haemodynamic modelling approach

In order to monitor pulmonary arterial pressure (P) by any non-invasive imaging technique, a haemodynamic model of blood flow kinetics and wall mechanics has been developed. It is a one-dimensional model of pulsatile flow in an elastic pulmonary arterial trunk, assuming that blood is an incompressible fluid and viscous effects are negligible. The equations are P(t)-Pd = rho c2lnS(t)/Sd-1/2pw-2(t) Pd = (Sd/Ss)1/2Pp where, at any time of the ejection phase of systole, P(t), S(t) and w(t) are the pulmonary arterial pressure, cross-sectional area of the pulmonary artery and blood velocity averaged on the cross section S, respectively, PP is the pulse pressure, the difference between the peak systolic pressure and the diastolic pressure Pd; rho is blood density, c pulse wave velocity, and Ss and Sd are maximum (systolic) and minimum (diastolic) values of the cross-sectional area S. Using these equations, P(t) can be calculated if the three parameters, i.e. c, S(t) and w(t) are measured. So far, it has been impossible to measure the pulse wave velocity c non-invasively. We have investigated the calculation of c from S(t) and w(t) using the equation of continuity in the absence and presence of reflected pressure waves. The hypotheses of the haemodynamic model are discussed.     

Pulsatile pressure and flow in the mesenteric vascular bed of the cat

Summary The transmission of arterial pressure and flow pulse through the mesenteric vascular bed was studied in 18 experiments on cats. Pressures were measured in the superior mesenteric artery and in small mesenteric veins, red blood cell flow velocities in mesenteric microvessels smaller than 60 diameter. Venous pressures were found to show heart beat synchronous oscillatory components of 0.2–0.5mmHg amplitude. Venous pressure pulses were delayed in time in comparison to the arterial pressure pulses: mean transit times varied between 85 and 110 msec. Blood flow velocities in arterioles and venules were generally pulsatile, the amplitude of the arteriolar pulses averaging 52.5% of mean velocity, of the venular pulses 32.5%. The velocity pulses were found to be similar in shape as flow pulses in larger arteries. Infusion of vasoactive drugs showed transmission of arterial pulses to be inversely dependent upon vascular resistance. It is concluded that the concept of complete damping of the arterial pulse during the passage of blood through the intestinal vascular bed cannot be maintained. Two different mechanisms of pulse transmission are discussed: direct hydraulic transmission through the capillary network and transmission across the vascular wall from the arteriole to the venule.Supported by USPHS Grant HE-08977.     

A new approach for identification of heartbeats in multimodal physiological signals

In this paper, a technique is proposed for detection of heartbeats in multimodal data. Recording of multiple physiological signals from the same subject is common practice nowadays. Multiple physiological signals are generally available but are processed separately without taking into consideration information from other relevant signals. The heartbeats are generally detected from R peaks in electrocardiogram (ECG) signal, however, if ECG is noisy, other signals reflecting the cardiac activity may be used for identifying heartbeats. This paper describes a new method for detection of heartbeats using ECG and arterial blood pressure (ABP) signals. The physiological data are segmented into various fragments and signal quality is determined using the judgment of noise level. If the ECG data fragment is noisy, heartbeats are computed from the ABP fragment. The evaluation was performed on training data set of computing in cardiology challenge 2014. The proposed methodology has resulted in better detection accuracy as compared to the unimodal methods.     

Regression analysis for peak designation in pulsatile pressure signals

Following recent studies, the automatic analysis of intracranial pressure (ICP) pulses appears to be a promising tool for forecasting critical intracranial and cerebrovascular pathophysiological variations during the management of many disorders. A pulse analysis framework has been recently developed to automatically extract morphological features of ICP pulses. The algorithm is able to enhance the quality of ICP signals, to segment ICP pulses, and to designate the locations of the three ICP sub-peaks in a pulse. This paper extends this algorithm by utilizing machine learning techniques to replace Gaussian priors used in the peak designation process with more versatile regression models. The experimental evaluations are conducted on a database of ICP signals built from 700 h of recordings from 64 neurosurgical patients. A comparative analysis of different state-of-the-art regression analysis methods is conducted and the best approach is then compared to the original pulse analysis algorithm. The results demonstrate a significant improvement in terms of accuracy in favor of our regression-based recognition framework. It reaches an average peak designation accuracy of 99% using a kernel spectral regression against 93% for the original algorithm.     

Clinical achievements of impedance analysis

Various models and derived measures of arterial function have been proposed to describe and quantify pulsatile hemodynamics in humans. A major distinction can be drawn between lumped models based on circuit theory that assume infinite pulse wave velocity versus distributed, propagative models based on transmission line theory that acknowledge finite wave velocity and account for delays, wave reflection, and spatial and temporal pressure gradients within the arterial system. Although both approaches have produced useful insights into human arterial pathophysiology, there are important limitations of the lumped approach. The arterial system is heterogeneous and various segments respond differently to cardiovascular disease risk factors including advancing age. Lumping divergent change into aggregate summary variables can obscure abnormalities in regional arterial function. Analysis of a limited number of summary variables obtained by measuring aortic input impedance may provide novel insights and inform development of new treatments aimed at preventing or reversing abnormal pulsatile hemodynamics.     

Wavelet based ST-segment analysis

A novel algorithm for ST-segment analysis is developed using the multi-resolution wavelet approach. The system detects the QRS complexes and analyses each beat using the wavelet transform to identify the characteristic points (fiducial points). These fiducial points are, iso-electric level, the J point, and onsets and offsets of the QRS complex and T wave. The algorithm determines the T onset by looking for a point of inflection between the J point and the T peak. Furthermore, detection of characteristic points by the wavelet technique reduces the effect of noise. The results show that the proposed approach gives very accurate ST levels, as compared to the conventional (empirical) technique, at higher heart rates and with different morphologies. The algorithm detects the ST-segment length in 92.3% beats with an error of 4 ms, and in 97.3% beats the error is within 8 ms. The algorithm has been implemented on a TMS320C25 based add-on DSP card connected to a PC to provide the on-line analysis and display of ST-segment data.     

高频超声灰阶血流成像系统的研究与实现

目的 探索高频超声的血流成像方法 和血流图像处理方法 ,以实现血流成像系统.方法 根据红细胞对高频超声的散射规律,使用20 MHz超声的机械、线性扫描探头,在一条扫描线上发射多次脉冲,利用脉冲回波相减法提取血流信息.使用模拟血管进行血流成像,并对血流图像进行分析处理.结果 用该高频超声灰阶血流成像系统对模拟血流进行成像,得到的血流图像边缘清晰,并且在对血流图像进行处理后,能很好地滤除血管周围组织产生的伪像,以增强血流信息,从而更好地观察血流形态.结论 在高频超声条件下,使用单脉冲即可提取较强的浅表血流信号,并通过滤除伪像,能实现高频超声条件下的灰阶血流成像.     

Subcutaneous blood pressure monitoring with an implantable optical sensor

We introduce a minimally invasive, implantable system that uses pulse transit time to determine blood pressure. In contrast to previous approaches, the pulse wave is detected by a photoplethysmographic (PPG) signal, acquired with high quality directly on subcutaneous muscle tissue. Electrocardiograms (ECG) were measured with flexible, implantable electrodes on the same tissue. PPG detection is realized by a flat 20 mm x 6 mm optoelectronic pulse oximeter working in reflection mode. The optical sensor as well as the ECG electrodes can be implanted using minimally invasive techniques, with only a small incision into the skin, making long-term monitoring of blood pressure in day-to-day life for high-risk patients possible. The in vivo measurements presented here show that the deviation to intra-arterial reference measurements of the systolic blood pressure in a physiologically relevant range is only 5.5 mmHg, demonstrated for more than 12 000 pulses. This makes the presented sensor a grade B blood pressure monitor.