Noise-resistant pulse oximetry using a synthetic reference signal

We describe a noise-resistant pulse oximetry algorithm suited to both signal reconstruction and oxygen saturation estimation. The algorithm first detects relatively clean signal sections from which the heart rate is estimated. The heart rate is used to construct a synthetic reference signal that matches an idealized pulse signal. An adaptive filter continuously processes the sensor signals, reconstructing signals in a linear subspace defined by the reference signal. A projective subspace algorithm is then applied to find the oxygenation level of the blood. We show that under specific circumstances this algorithm solves the sufficiency condition for signal reconstruction in linear saturation estimators. The core principle of using a frequency modulated synthetic reference signal can be applied to adaptive filtering of other physiological signals controlled by the heartbeat, such as blood pressure and electrocardiogram.     

基于改进差分阈值算法的心电检测技术研究

针对差分阈值算法中固定阈值的局限性,文中提出了一种基于自适应波峰阈值和R波间隔阈值的算法。该算法结合心电信号特点自动选择波峰阈值,并选择R波间隔阈值,提高了算法的自适应性和准确率。文中以MIT-BIH心律失常数据库中的心电信号作为实验样本,采用带通滤波与小波阈值滤波相结合的方法完成心电信号去噪,采用改进差分自适应阈值算法对心电信号进行波形检测。实验结果表明,该算法能够将心电信号R波的检测准确率提升到99.57%,有效减少了误检、漏检问题的发生,并可准确完成心率、心率变异性、身体疲劳度、精神疲劳度计算和常见心律失常分类。     

基于生成对抗网络的动态心电信号QRS波检测

心电信号分析是预防心血管疾病的重要举措,QRS波的精确检测不仅是心电信号处理的关键步骤且对心率计算和异常情况分析具有重要作用.针对动态心电信号存在信号质量差或异常节奏波形导致常用QRS波检测方法精度较低的问题,本文提出了 一种基于生成对抗网络新型QRS波检测算法.该算法以Pix2Pix网络为基础,生成网络采用U-Net...     

Pulse morphology visualization and analysis with applications in cardiovascular pressure signals

We present a new analysis and visualization method for studying the functional relationship between the pulse morphology of pressure signals and time or signal metrics such as heart rate, pulse pressure, and means of pressure signals, such as arterial blood pressure and central venous pressure. The pulse morphology is known to contain potentially useful clinical information, but it is difficult to study in the time domain without the aid of a tool such as the method we present here. The primary components of the method are established signal processing techniques, nonparametric regression, and an automatic beat detection algorithm. Some of the insights that can be gained from this are demonstrated through the analysis of intracranial pressure signals acquired from patients with traumatic brain injuries. The analysis indicates the point of transition from low-pressure morphology consisting of three distinct peaks to a high-pressure morphology consisting of a single peak. In addition, we demonstrate how the analysis can reveal distinctions in the relationship between morphology and several signal metrics for different patients.     

Statistical modeling of cardiovascular signals and parameter estimation based on the extended Kalman filter

Cardiovascular signals such as arterial blood pressure (ABP), pulse oximetry (POX), and intracranial pressure (ICP) contain useful information such as heart rate, respiratory rate, and pulse pressure variation (PPV). We present a novel state-space model of cardiovascular signals and describe how it can be used with the extended Kalman filter (EKF) to simultaneously estimate and track many cardiovascular parameters of interest using a unified statistical approach. We analyze data from four databases containing cardiovascular signals and present representative examples intended to illustrate the versatility, accuracy, and robustness of the algorithm. Our results demonstrate the ability of the algorithm to estimate and track several clinically relevant features of cardiovascular signals. We illustrate how the algorithm can be used to elegantly solve several actively researched and clinically significant problems including heart and respiratory rate estimation, artifact removal, pulse morphology characterization, and PPV estimation.     

Blind separation of asymmetric PCMA signal based on soft information joint correction

On the basis of a blind separation structure with low complexity,an efficient blind separation algorithm based on soft information joint correction was proposed for asymmetric PCMA to improve the demodulation performance of strong and weak signals.By utilizing the demodulation mutual influence between the strong and weak signals,this algorithm tried to correct the receiving symbols of strong signal with high error probability.Comparing the symbol constellation quality (soft information) of signals before and after correction,it can be decided whether the hard decision values of strong and weak signals need to be modified,which efficiently reduce the demodulation error rate of strong and weak signals.The simulation results show that,the demodulation error rate of strong and weak signals can be reduced by nearly two orders of magnitude after joint correction especially when the signal to noise ratio of strong signal is higher than 17 dB,and the computational complexity of this algorithm is lower than that of the conventional reconstruction cancellation algorithm.     

卫星通信信号调制识别技术研究

通信信号的调制方式识别是非协作通信领域的一个研究热点,它需要在没有任何先验条件,且环境复杂有噪声的条件下,不依赖于其它的先验知识,确定目标信号的调制方式。然后就可提取载频、码速率、滚降系数等参数,为信号的进一步深入研究提供依据。分析了几种经常用于卫星通信的MPSK调制信号和高阶调制信号16APSK,提出了一种基于高阶累积量与频谱特征的识别算法。在特定的数据长度和信噪比条件下,理论分析与仿真验证均验证了该算法易于实现、识别率高（≥95％）。     

An Analysis of Interference Mitigation Capability of Low Duty-Cycle UWB Communications in the Presence of Wideband OFDM System

Low duty-cycle (LDC) algorithm is interference mitigation technique, which can reduce the average interference to the existing radio systems by lowering pulse repetition interval or pulse occupation time. In this paper, the coexistence environment between low data rate ultra wideband (UWB) communication system such as wireless sensor network and the existing wideband system using orthogonal frequency division multiplexing (OFDM) such as 4th generation mobile cellular system (4G), worldwide interoperability for microwave access (WiMAX), and field pickup unit (FPU) is considered. In order to analyze the interference mitigation capability of LDC algorithm with impulse based UWB (LDC-UWB) system, the frame error rate (FER) of wideband OFDM system is examined for two types of LDC-UWB system: the signal with random polarity such as binary pole signals and without random polarity such as mono pole signals. We present that LDC algorithm is an efficient interference mitigation technique for low data rate UWB communication via computer simulations regardless of definitions of transmitted energy of UWB communication system, and also that the signal with random polarity is suitable for LDC-UWB system to mitigate interference to the other radio systems. We further investigate the adequate duty-cycle of LDC-UWB system for each definition of transmitted power of UWB communication.     

Impairment-aware ordered scheduling in dual-header optical burst switched networks

Optical burst switching (OBS) is a promising technology for bridging the gap between optical wavelength switching and optical packet switching. Optical signal transmission quality is subject to various types of physical impairment introduced by optical fibers, switching elements, or other network components. The signal degradation due to physical impairment may be significant enough such that the bit-error rate of received signals is unacceptably high at the destination, rendering the signal to not usable. In this article, based on earlier study, we study the burst-scheduling problem in OBS networks using two control packets for each data burst, taking into account physical impairment effects. We propose a burst-scheduling algorithm that accommodates incoming bursts by primary path routing, deflection routing, and burst scheduling. We design an admission control mechanism to use network resources efficiently. At an OBS node, the proposed algorithm schedules bursts for transmission by searching for available resources as well as verifying signal quality. Our simulation results demonstrate that the proposed algorithm is effective in terms of reducing the burst-blocking probability.     

基于压电薄膜的非接触式人体生理信号监测椅

对人体的心率和呼吸信号进行日常监测有助于人体健康生活管理,该文设计了一种非接触式生理信号监测系统。通过装有聚偏氟乙烯(PVDF)压电薄膜传感器的新型椅子结构感知人体振动产生的微小压力作用,并将振动信号传送给上位机软件;从振动信号中提取心冲击信号与呼吸信号,并通过LabVIEW显示波形变化,可以完成心率和呼吸的实时监测。结果表明,装置监测的心率值与血氧脉搏仪相比,其平均误差为2.013％,平均呼吸率误差为4.88％,具有较好的实用性。     

Robust estimation of fetal heart rate variability using Doppler ultrasound

This paper presents a new measure of heart rate variability (HRV) that can be estimated using Doppler ultrasound techniques and is robust to variations in the angle of incidence of the ultrasound beam and the measurement noise. This measure employs the multiple signal characterization (MUSIC) algorithm which is a high-resolution method for estimating the frequencies of sinusoidal signals embedded in white noise from short-duration measurements. We show that the product of the square-root of the estimated signal-to-noise ratio (SNR) and the mean-square error of the frequency estimates is independent of the noise level in the signal. Since varying angles of incidence effectively changes the input SNR, this measure of HRV is robust to the input noise as well as the angle of incidence. This paper includes the results of analyzing synthetic and real Doppler ultrasound data that demonstrates the usefulness of the new measure in HRV analysis.     

基于互补对称滤波器的APCMA信号的盲分离算法

针对小站信号带宽大于主站信号的新非对称成对载波多址（Asymmetric Paired Carrier Multiple Access,APCMA）信号背景,在一种低复杂度的盲抵消结构基础上,提出基于互补对称滤波器的盲分离算法.该算法采用互补对称滤波器从时频域上将混合信号分解为同时含主站信号与小站信号,以及只含小站信号的两路分量信号,在保证采样率不变的情况下对含主站信号的信号分量做进一步的信号分离,通过两路分量信号的同步处理确保了分离之后的两路信号分量中小站信号的可加性和完整性.仿真结果表明,与原低复杂度算法相比,本文算法有效地提升了混合信号的分离性能.此外,本文提出的具有信息不变性的互补对称滤波器有较广泛的应用前景,可用于宽带多信号抵消、信道估计以及隐蔽传输下的扩频检测等问题中.     

Spectrum estimation using multirate observations

In this paper, we are interested in estimating the power spectral density of a stationary random signal x(n) when the signal itself is not available but some low-resolution measurements derived from it are observed. We consider a model where x(n) is being measured using a set of linear multirate sensors. Each sensor outputs a measurement signal v/sub i/(n) whose sampling rate is only a fraction of the sampling rate assumed for the original signal. Based on this model, we pose the following problem: Given certain autocorrelation coefficients of the observable signals v/sub i/(n), estimate the power spectral density of the original signal x(n). It turns out that this problem is ill-posed. We suggest to resolve this issue by using the principle of maximum entropy (ME). We address technical difficulties associated with the ME solution and then devise a practical algorithm for its approximate computation. We demonstrate the viability of this algorithm through simulation examples.     

DOA估计算法评价准则在不同功率入射信号下的修正

针对复杂电磁环境中信号功率对入射信号波达方向(DOA)估计的影响问题进行研究,发现用于DOA估计算法性能分析的经典评价准则对不同功率入射信号存在局限性.针对该问题,首先证明了强信号功率会影响弱信号DOA估计性能,得到强信号功率增加会导致弱信号功率克拉美罗界上升,即弱信号DOA估计的均方根误差增加.然后分析了 DOA估计...     

Event-synchronous cancellation of the heart interference inbiomedical signals

A two-pass adaptive filtering algorithm is proposed for cancellation of recurrent interferences such as the heart interference in biomedical signals. In the first pass, an average waveform in one period of the interference is estimated by event-synchronous (QRS-synchronous) averaging of the corrupted signal. In a second pass, an adaptive Schur recursive least squares (RLS) lattice filter is used to cancel the interference by using the event synchronously repeated estimated average waveform of the interference as an artificial reference signal. One key feature of this approach is that the ECG is only used for QRS synchronization and not directly as a reference signal for adaptive filtering. Thus the proposed algorithm can be applied to interference problems where ECG and true interference are almost synchronous but show considerably different waveforms. This is usually the case with the heart interference in biomedical signals. Both off-line and real-time implementations of the event synchronous interference canceller are described. The method is applied to the cancellation of the heart interference in magnetoencephalogram (MEG) signals and to the effective isolation of ventricular extrasystoles (VES) in magnetocardiogram (MCG) signals. Experimental results are shown. The new method typically attenuates the amplitudes of R-wave and T-wave interference components by an amplitude factor of 30 without influencing the MEG events of interest     

Signal separation using fractional sampling in multiusercommunications

Described is a decorrelating filter that separates signals in a multiuser environment by relying on the relative delays to be sufficiently distinct. The input signal is fractionally sampled to allow for the differentiation of the user delays. Both zero-forcing and minimum mean-square-error versions of this filter are derived and probability of error expressions are given. The algorithm successfully separates unknown digital signals by using the known received pulse shapes and the symbol rate. A delay-division multiple-access (DDMA) scheme based on this signal decorrelator is proposed that will allow signals to be transmitted without spreading the signal spectrum. It is shown that in a noisy environment the signal-to-noise ratio penalty for DDMA can be severe yet the bandwidth advantage is very pronounced compared to spread-spectrum systems and is similar to other bandwidth efficient schemes. The performance of a code-division multiple-access (CDMA) system using this signal decorrelator is also given. The decorrelator can be used as a blind multiuser detector or as a preprocessor to enhance the performance of a CDMA system     

SAGE Algorithm for Semi-Blind Channel Estimation and Symbol Detection for STBC MIMO OFDM Systems

In this paper, a novel semi-blind channel estimation and symbol detection based on space-alternating generalized expectation-maximization (SAGE) algorithm are proposed for space time block coded (STBC) multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM) systems. At the receiver, the transmitted signals of all users are added. By utilization of SAGE algorithm, this superimposed received signals are decomposed into their signal components. Then, the channel is estimated at pilot positions. After that, the channel estimation in the other positions are obtained by interpolation and symbol detection is done by utilizing the estimated channel. In SAGE algorithm, selection of initial value is very important for the convergence. We discuss the appropriate range for selection of initial value in this paper. Simulation results show that by increasing the number of users, the slope of bit error rate curve increases with same initial value and the channel estimation becomes worse by increasing the iteration of SAGE algorithm if the initial value is not selected in the proposed rang.     

基于小波与独立分量分析的语音信号分离算法研究

混合语音信号的分离是盲源分离的重要内容,也是信号处理领域中的一个难题.对含噪声的混合信号采用小波滤波对信号进行去噪预处理,再采用基于信息极大分离算法提取信号的独立分量.实验结果表明,与传统的滤波方法相比,该算法在消除噪声的同时,对其他信号的细节几乎没有破坏,能够很好地分离频率相同或者相近的语音信号,而且去噪性能也比传统的滤波方法好.     

基于时延估计算法的透明转发器的实现方法

讨论了转发器实现收发不间断的方法,提出了在自适应噪声相消的系统上,将简化的分数阶傅里叶变换理论应用于时延估计,进而将干扰信号重构抵消。推导了该算法,并提出基于该算法实现收发同时进行的转发器系统,即透明转发器。给出本系统模型框图,该透明转发器采用最小均方(LMS)算法建立自适应系统控制结构,能够通过自适应滤波器将自发干扰信号减除,并将不相关的背景噪声抵消。最后利用 MATLAB 软件仿真了基于该算法的透明转发器在具体信号上的运用,实验结果表明该方法实现了不间断转发功能,并且系统结构简单、易实现。     

An automatic beat detection algorithm for pressure signals

Beat detection algorithms have many clinical applications including pulse oximetry, cardiac arrhythmia detection, and cardiac output monitoring. Most of these algorithms have been developed by medical device companies and are proprietary. Thus, researchers who wish to investigate pulse contour analysis must rely on manual annotations or develop their own algorithms. We designed an automatic detection algorithm for pressure signals that locates the first peak following each heart beat. This is called the percussion peak in intracranial pressure (ICP) signals and the systolic peak in arterial blood pressure (ABP) and pulse oximetry (SpO2) signals. The algorithm incorporates a filter bank with variable cutoff frequencies, spectral estimates of the heart rate, rank-order nonlinear filters, and decision logic. We prospectively measured the performance of the algorithm compared to expert annotations of ICP, ABP, and SpO2 signals acquired from pediatric intensive care unit patients. The algorithm achieved a sensitivity of 99.36% and positive predictivity of 98.43% on a dataset consisting of 42,539 beats.