A Quantitative Model for the Ventricular Response During Atrial Fibrillation

We review the principal statistical properties of the RR interval sequence during atrial fibrillation. A simple quantitative electrophysiologic model is presented which successfully accounts for these statistical features. In this model the atrioventricular junction (AVJ) is treated as a single cell equivalent characterized by a refractory period and spontaneous rate of phase 4 depolarization. The atria are presumed to bombard the AVJ with impulses that arrive randomly in time; each impulse induces a partial depolarization of the AVJ equivalent cell. We show that other models for the ventricular response during atrial fibrillation (e. g., concealed conduction models) do not adequately account for the salient statistical features of the RR interval sequence. The present model may be utilized to characterize a sequence of RR intervals recorded from a given individual in terms of the numerical magnitudes of the model's four parameters: the mean rate at which atrial impulses bombard the AVJ, the relative amplitude of the impulses, the relative rate of spontaneous phase 4 depolarization of the AVJ equivalent cell, and the refractory period of the AVJ equivalent cell. Such a characterization may be useful in studying mechanisms of drug action and interaction, as well as potentially offering a quantitative means for optimizing pharmacologic manangement of chronic atrial fibrillation.     

Computer modeling of ventricular rhythm during atrial fibrillation and ventricular pacing

We propose a unified atrial fibrillation (AF)-ventricular pacing (VP) (AF-VP) model to demonstrate the effects of VP on the ventricular rhythm during atrial fibrillation AF. In this model, the AV junction (AVJ) is treated as a lumped structure characterized by refractoriness and automaticity. Bombarded by random AF impulses, the AVJ can also be invaded by the VP-induced retrograde wave. The model includes bidirectional conduction delays in the AVJ and ventricle. Both refractory period and conduction delay of the AVJ are dependent upon its recovery time. The electrotonic modulation by blocked impulses is also considered in the model. Our simulations show that, with proper parameter settings, the present model can account for most principal statistical properties of the RR intervals during AF. We further demonstrate that the AV conduction property and the ventricular rate in AF depend on both AF rate and the degree of electrotonic modulation in the AVJ. Finally, we show that multilevel interactions between AF and VP can generate various patterns of ventricular rhythm that are consistent with previous experimental observations.     

Propagation pattern analysis during atrial fibrillation based on sparse modeling

In this study, sparse modeling is introduced for the estimation of propagation patterns in intracardiac atrial fibrillation (AF) signals. The estimation is based on the partial directed coherence function, derived from fitting a multivariate autoregressive model to the observed signal using least-squares (LS) estimation. The propagation pattern analysis incorporates prior information on sparse coupling as well as the distance between the recording sites. Two optimization methods are employed for estimation of the model parameters, namely, the adaptive group least absolute selection and shrinkage operator (aLASSO), and a novel method named the distance-adaptive group LASSO (dLASSO). Using simulated data, both optimization methods were superior to LS estimation with respect to detection and estimation performance. The normalized error between the true and estimated model parameters dropped from 0.20 ± 0.04 for LS estimation to 0.03 ± 0.01 for both aLASSO and dLASSO when the number of available data samples exceeded the number of model parameters by a factor of 5. For shorter data segments, the error reduction was more pronounced and information on the distance gained in importance. Propagation pattern analysis was also studied on intracardiac AF data, the results showing that the identification of propagation patterns is substantially simplified by the sparsity assumption.     

Estimation of the Threshold in Energy and Kurtosis Detectors for IR-UWB OOK Systems

In this paper, we propose a method to estimate the threshold value for energy detector (ED) and kurtosis detector (KD) in impulse radio ultra-wideband on-off keying (OOK) scheme. Estimation of threshold value is obtained from the parameters of the probability density functions (PDF) of two hypotheses in OOK signaling. Gaussian and lognormal distributions are used for ED receiver and KD receiver, respectively. Symbols are transmitted in packets and the PDF’s parameters of the hypotheses are estimated by using training symbols in each packet. The approximation of the threshold value is obtained by using these parameters. Simulation results show that the estimated threshold has high accuracy for different amounts of signal to noise ratios and integration intervals.     

Automated Assessment of Atrioventricular Conduction in the Heart

After a pacemaker impulse passes along the atrioventricular conducting pathway of the heart, that tissue resists subsequent conduction for a time interval known as the ``effective refractory period.' Effective refractory period (ERP) and transit time of conducted impulses vary in response to drug, hormonal and electrical influences and may be used as indicators of the immediate physiologic state of intracardiac conduction. The traditional manual technique for measurement of ERP and conduction time consists in repeatedly adjusting a pulse generator until conduction of a stimulus of minimum interval can be observed on a chart recorder or oscilloscope. In contrast to the slow, imprecise and tedious manual method, the automated system described here provides whole graphs of ERP and conduction times as functions of external pacing interval or intervals of induced premature beats with a minimum of operator intervention. Thus, atrioventricular conduction can be conveniently assessed by machine, while the human intellect is freed to dwell on the abstractions of interpreting results and controlling the overall measurement environment.     

Estimation of the directions of arrival of signals in unknowncorrelated noise. I. The MAP approach and its implementation

The authors propose a method of direction of arrival (DOA) estimation of signals in the presence of noise whose covariance matrix is unknown and arbitrary, other than being positive definite. They examine the projection of the data onto the noise subspace. The conditional probability density function (PDF) of the projected data given the signal parameters and the unknown projected noise covariance matrix is first formed. The a posteriori PDF of the signal parameters alone is then obtained by assigning a noninformative a priori PDF to the unknown noise covariance matrix and integrating out this quantity. A simple criterion for the maximum a posteriori (MAP) estimate of the DOAs of the signals is established. Some properties of this criterion are discussed, and an efficient numerical algorithm for the implementation of this criterion is developed. The advantage of this method is that the noise covariance matrix does not have to be known, nor must it be estimated     

A logical state model of circus movement atrial flutter. Role ofanatomic obstacles, anisotropic conduction and slow conduction zones oninduction, sustenance, and overdrive paced modulation of reentrantcircuits

Because the relative roles of anatomical obstacles, in combination with functional barriers, anisotropic conduction, and slow conduction can not be readily assessed with current electrophysiological techniques, an atrial activation model was developed to study the mechanisms of circus movement atrial flutter. A discrete state model consisting of 4,096 logically connected cardiac elements was used to simulate atrial activation; an inexcitable region simulating the inferior vena cava (IVC) was also incorporated in the model. Atrial flutter was induced by programmed premature stimulation, Anisotropic conduction velocity properties, regional variations in slow conduction, regional refractory gradients and stimulation parameters were specified for each simulation. The reentrant circuit generally consisted of a single reentrant impulse which circulated around a continuous line of functional bidirectional conduction block joined to the IVC. Rapid pacing, 5-30 ms shorter than the spontaneous reentrant cycle length, was applied to entrain and/or terminate the rhythm. The results of this study demonstrate that patterns of initiation, entrainment, termination and reinitiation of circus movement atrial flutter mimic results from in vivo activation mapping studies. The authors find that sustained circus movement atrial flutter circuits depend on: 1) natural anatomical obstacles to stabilize reentrant circuits, and 2) anisotropic conduction properties to reduce the degree of functional conduction block needed to maintain circus movement. Rapid pacing of simulated circus movement atrial flutter demonstrated that the entrainment criteria can be satisfied in a two-dimensional syncytium     

基于雨衰雪衰影响的空间信道统计模型

针对某些特殊的移动通信环境中信号的到达角度( TOA)、到达时间( AOA)等信道参数估计的复杂性,提出一种基于散射体均匀分布室外统计信道模型。该模型将树木中的树冠层作为散射体呈均匀分布在移动台与基站周围的椭圆形区域内,通过树冠层在雨雪环境中的相对介电常数来分析降雨降雪量对信号传输带来的影响,可以方便地估计出此室外微小区的空时信道参数,如AOA概率密度分布函数、TOA概率密度分布函数以及到达角度/到达时间( AOA/TOA )联合概率密度分布函数。仿真结果表明,信号的到达时间TOA以及到达角度AOA随着降雨降雪量的增大而增大,尤其当降雪量增大使得树冠层相对介电常数达到4.5时,信号的传输将被阻塞。该模型的信道参数估计结果符合理论与经验,扩展了移动无线通信信道模型的研究与应用。     

Minimum mean squared error impulse noise estimation andcancellation

Impulses are infrequent bursts of high amplitude noise. A wide-band communications or data acquisition receiver has a fast sampling rate, so it can capture many samples of each impulse waveform. The arrival of an impulse can be identified by its distinct waveform and amplitude. The paper models impulse waveforms as a vector subspace of low dimension. Formulas are derived for the minimum mean squared error (MMSE) estimation of the arrival time and amplitudes of impulses, given that a set of vectors that spans the subspace is known. Formulas are also derived for the adaptive MMSE estimation of a set of vectors that spans the subspace. The values of the mean squared error (MSE) of the amplitude estimates are determined. It is shown how the theory can be used to cancel impulse noise. Correlated impulse noise arriving at a reference input can be used to estimate and cancel the primary input impulse noise. The MMSE coefficients for impulse noise cancellation are derived and presented. Simulations are presented that use the equations and methods derived in the paper for modeling and canceling impulse noise measured on copper telephone loops for asymmetric digital subscriber lines (ADSL)     

Joint Estimation of TOA and DOA in IR‐UWB System Using Sparse Representation Framework

This paper addresses the problem of joint time of arrival (TOA) and direction of arrival (DOA) estimation in impulse radio ultra‐wideband systems with a two‐antenna receiver and links the joint estimation of TOA and DOA to the sparse representation framework. Exploiting this link, an orthogonal matching pursuit algorithm is used for TOA estimation in the two antennas, and then the DOA parameters are estimated via the difference in the TOAs between the two antennas. The proposed algorithm can work well with a single measurement vector and can pair TOA and DOA parameters. Furthermore, it has better parameter‐estimation performance than traditional propagator methods, such as, estimation of signal parameters via rotational invariance techniques algorithms matrix pencil algorithms, and other new joint‐estimation schemes, with one single snapshot. The simulation results verify the usefulness of the proposed algorithm.     

Research on the spatial characterization of a 3D UAV air-to-ground channel model

To cope with the problem that the distribution assumptions of arrival angle and departure angle in existing geometry-based stochastic modeling (GBSM) for the unmanned aerial vehicle (UAV) air-to-ground (A2G) channel are too ideal to describe the spatial statistical property of the UAV A2G propagation environment precisely,considering the three-dimensional (3D) cylindrical A2G channel model,the spatial geometric characteristics of scattering regions were investigated analytically as corresponding to the angles of arrival and departure in both elevation and azimuth planes,which derived the probability density function (PDF) for the distribution of each angle.The effects of various parameters of channel model on the PDF were studied and simulation results prove that the derived PDF can describe the spatial statistical properties of UAV A2G channels more accurately,and can support the modeling of A2G communication channels well.     

A novel method for detection of the transition between atrial fibrillation and sinus rhythm

Automatic detection of atrial fibrillation (AF) for AF diagnosis, especially for AF monitoring, is necessarily desirable for clinical therapy. In this study, we proposed a novel method for detection of the transition between AF and sinus rhythm based on RR intervals. First, we obtained the delta RR interval distribution difference curve from the density histogram of delta RR intervals, and then detected its peaks, which represented the AF events. Once an AF event was detected, four successive steps were used to classify its type, and thus, determine the boundary of AF: 1) histogram analysis; 2) standard deviation analysis; 3) numbering aberrant rhythms recognition; and 4) Kolmogorov-Smirnov (K-S) test. A dataset of 24-h Holter ECG recordings (n = 433) and two MIT-BIH databases (MIT-BIH AF database and MIT-BIH normal sinus rhythm (NSR) database) were used for development and evaluation. Using the receiver operating characteristic curves for determining the threshold of the K-S test, we have achieved the highest performance of sensitivity and specificity (SP) (96.1% and 98.1%, respectively) for the MIT-BIH AF database, compared with other previously published algorithms. The SP was 97.9% for the MIT-BIH NSR database.     

Estimation of multipath parameters in wireless communications

In a parametric multipath propagation model, a source is received by an antenna array via a number of rays, each described by an arrival angle, a delay, and a fading parameter. Unlike the fading, the angles and delays are stationary over long time intervals. This fact is exploited in a new subspace-based high-resolution method for simultaneous estimation of the angle/delay parameters from multiple estimates of the channel impulse response. A computationally expensive optimization search can be avoided by using an ESPRIT-like algorithm. Finally, we investigate certain resolution issues that take the fact that the source is bandlimited into account     

P-wave morphology assessment by a gaussian functions-based model in atrial fibrillation patients

Aim of this study was to present a P-wave model, based on a linear combination of Gaussian functions, to quantify morphological aspects of P-wave in patients prone to atrial fibrillation (AF). Five-minute ECG recordings were performed in 25 patients with permanent dual chamber pacemakers. Patients were divided into high-risk and low-risk groups, including patients with and without AF episodes in the last 6 mo preceding the study, respectively. ECG signals were acquired using a 32-lead mapping system for high-resolution biopotential measurement (ActiveTwo, Biosemi, The Netherlands, sample frequency 2 kHz, 24-bit resolution). Up to 8 Gaussian models have been computed for each averaged P-wave extracted from every lead. The P-wave morphology was evaluated by extracting seven parameters. Classical time-domain parameters, based on P-wave duration estimation, have been also estimated. We found that the P-wave morphology can be effectively modeled by a linear combination of Gaussian functions. In addition, the combination of time-domain and morphological parameters extracted from the Gaussian function-based model of the P-wave improves the identification of patients having different risks of developing AF.     

A multirate DSP model for estimation of discrete probability density functions

The problem of estimating a probability density function (PDF) from measurements has been widely studied by many researchers. Even though much work has been done in the area of PDF estimation, most of it was focused on the continuous case. We propose a new model-based approach for modeling and estimating discrete probability density functions or probability mass functions. This approach is based on multirate signal processing theory, and it has several advantages over the conventional histogram method. We illustrate the PDF estimation procedure and analyze the statistical properties of the PDF estimates. Based on this model, a novel scheme is introduced that can be used for estimating the PDF in the presence of noise. Furthermore, the proposed ideas are extended to the more general case of estimating multivariate PDFs. Finally, we also consider practical issues such as optimizing the coefficients of a digital filter, which is an integral part of the model. This allows us to apply the proposed model to solve real-world problems. Simulation results are given where appropriate in order to demonstrate the ideas.     

A new method for joint estimation of delay and Doppler from ambiguity function: combination of stochastic process and spatial processing for noise and clutter suppression

In this article, a new method is introduced for joint delay and Doppler estimation in ambiguity function (AF)-based radars. In this method, first each cell of AF is considered as a random variable and then a stochastic process is estimated for each cell based on its values during consecutive radar scans. In the second step, the AF is divided to high probability target and high probability clutter zones using parameters of the estimated stochastic processes. Finally, exact values of delay and Doppler of radar targets are extracted and localised from the divided AF by employing spatial processing techniques. Performance of the proposed method is evaluated in two different scenarios, corresponding to high and low speed targets, respectively. The obtained results show the greater ability of the suggested method in detection of the above types of targets compared to the present approaches. Furthermore, it can be shown that the proposed method causes more considerable improvement in the detection of low speed targets than high speed targets compared to available methods.     

基于SIRP法的相关韦布尔分布雷达杂波仿真

推导了韦布尔分布参量满足的最大似然(ML)估计方程和其为球不变随机矢量(SIRV)的条件,给出了模拟相关韦布尔分布雷达杂波的原理和流程。在此基础上,基于球不变随机过程(SIRP)法仿真了具有高斯谱的韦布尔分布及其特例——瑞利和指数分布,弥补了零记忆非线性(ZMNL)法不能独立控制边缘概率密度函数(PDF)与相关函数的不足。最后不仅验证了模拟数据的PDF与理论分布,估计的功率谱与理论谱的吻合程度,而且分析了ML估计的分布参数与真实值之间的相对误差。结果表明,基于SIRP法仿真相关韦布尔分布雷达杂波是有效的。     

An FIR Channel Estimation Filter with Robustness to Channel Mismatch Condition

This letter proposes a finite impulse response (FIR) channel estimation filter that has robustness against the channel mismatch due to the FIR structure. The channel impulse response is described with a complex state space model and then estimated from received data on the recent time interval. Numerical results show that the FIR channel estimation filter can provide more robust performance than conventional Kalman IIR filters when channel model parameters are not correct.     

Classification of nonstationary narrowband signals using segmented chirp features and hidden Gauss-Markov models

A method is provided for classifying finite-duration signals with narrow instantaneous bandwidth and dynamic instantaneous frequency (IF). In this method, events are partitioned into nonoverlapping segments, and each segment is modeled as a linear chirp, forming a piecewise-linear IF model. The start frequency, chirp rate, signal energy, and noise energy are estimated in each segment. The resulting sequences of frequency and rate features for each event are classified by evaluating their likelihood under the probability density function (PDF) corresponding to each narrowband class hypothesis. The class-conditional PDFs are approximated using continuous-state hidden Gauss-Markov models (HGMMs), whose parameters are estimated from labeled training data. Previous HGMM algorithms are extended by dynamically weighting the output covariance matrix by the ratio of the estimated signal and noise energies from each segment. This covariance weighting discounts spurious features from segments with low signal-to-noise ratio (SNR), making the algorithm more robust in the presence of dynamic noise levels and fading signals. The classification algorithm is applied in a simulated three-class cross-validation experiment, for which the algorithm exhibits percent correct classification greater than 97% as low as -7 dB SNR.     

The probability density of spectral estimates based on modifiedperiodogram averages

Welch's (1967) method for spectral estimation of averaging modified periodograms has been widely used for decades. Because such an estimate relies on random data, the estimate is also a random variable with some probability density function. Here, the PDF of a power estimate is derived for an estimate based on an arbitrary number of frequency bins, overlapping data segments, amount of overlap, and type of data window, given a correlated Gaussian input sequence. The PDFs of several cases are plotted and found to be distinctly non-Gaussian (the asymptotic result of averaging frequency bins and/or data segments), using the Kullback-Leibler distance as a measure. For limited numbers of frequency bins or data segments, the precise PDF is considerably skewed and will be important in applications such as maximum likelihood tests