A novel methodology for assessing the bounded-input bounded-output instability in QT interval dynamics: application to clinical ECG with ventricular tachycardia

The goal of this paper is to present a new methodology for assessing the bounded-input bounded-output (BIBO) stability in QT interval (QTI) dynamics from clinical ECG. The ECG recordings were collected from 15 patients who experienced ventricular tachycardia (VT). Ten-minute-long ECG recordings extracted immediately before the onset of a chosen VT, one per patient, were assembled into a VT group, while the control group comprised 10-min-long ECGs extracted 1 h before VT onset and at least 1 h after any prior arrhythmic event. Each 10-min recording was subdivided into 1-min ECG recordings (minECGs). The QTI dynamics of each minECG was defined as a function of several prior QTIs and RR intervals; the BIBO stability of this function was then assessed in the z -domain. The number of minECGs with unstable QTI dynamics (N (us)) and the frequency of premature activations (PA), f (PA) , were counted for each ECG recording and were compared between the VT and control groups. The results show that the present methodology successfully captured the instability in QTI dynamics leading to VT onset in the studied population. Significantly larger N (us) was found in the VT group compared against the control and a positive correlation between N (us) and f (PA) was identified in both groups.     

Estimation and modeling of QT-interval adaptation to heart rate changes

This paper introduces a new method for QT-interval estimation. It consists in a batch processing mode of the improved Woody's method. Performance of this methodology is evaluated using synthetic data. In parallel, a new model of QT-interval dynamics behavior related to heart rate changes is presented. Since two kinds of QT response have been pointed out, the main idea is to split the modeling process into two steps: 1) the modeling of the fast adaptation, which is inspired by the electrical behavior at the cellular level relative to the electrical restitution curve, and 2) the modeling of the slow adaptation, inspired by experimental works at the cellular level. Both approaches are based on a low-complexity autoregressive process whose parameters are estimated using an unbiased estimator. This new modeling of QT adaptation, combined with the presented QT-estimation process, is applied to several ECG recordings with various heart rate variability dynamics. Its potential is then illustrated on ECG recorded during rest, atrial fibrillation episodes, and exercise. Meaningful results in agreement with physiological knowledge at the cellular level are obtained.     

Observer of autonomic cardiac outflow based on blind source separation of ECG parameters

We present a novel method which provides an observer of the autonomic cardiac outflow using heartbeat intervals (RR) and QT intervals. The model of the observer is inferred from qualitative physiological knowledge. It consists in a problem of blind source separation of noisy mixtures which is resolved by a simple and robust algorithm. The robustness of the algorithm has been assessed by numerical simulations in adverse noisy environments. In clinical applications, we have validated the observer on subjects exposed to experimental conditions known to elicit sympathetic or parasympathetic response.     

Quantification of restitution dispersion from the dynamic changes of the T-wave peak to end, measured at the surface ECG

Action potential duration restitution (APDR) curves present spatial variations due to the electrophysiological heterogeneities present in the heart. Enhanced spatial APDR dispersion in ventricle has been suggested as an arrhythmic risk marker. In this study, we propose a method to noninvasively quantify dispersion of APDR slopes at tissue level by making only use of the surface electrocardiogram (ECG). The proposed estimate accounts for rate normalized differences in the steady-state T-wave peak to T-wave end interval (T(pe)). A methodology is developed for its computation, which includes compensation for the T(pe) memory lag after heart-rate (HR) changes. The capability of the proposed estimate to reflect APDR dispersion is assessed using a combination of ECG signal processing, and computational modeling and simulation. Specifically, ECG recordings of control subjects undergoing a tilt test trial are used to measure that estimate, while its capability to provide a quantification of APDR dispersion at tissue level is assessed by using a 2-D ventricular tissue simulation. From this simulation, APDR dispersion, denoted as ?α(SIM), is calculated, and pseudo-ECGs are derived. Estimates of APDR dispersion measured from the pseudo-ECGs show to correlate with ?α(SIM), being the mean relative error below 5%. A comparison of the ECG estimates obtained from tilt test recordings and the ?α(SIM) values measured in silico simulations at tissue level show that differences between them are below 20 % , which is within physiological variability limits. Our results provide evidence that the proposed estimate is a noninvasive measurement of APDR dispersion in ventricle. Additional results from this study confirm that T(pe) adapts to HR changes much faster than the QT interval.     

An improved morphological approach to background normalization of ECG signals

This paper describes an improved morphological approach to remove baseline wander from neonatal electrocardiogram (ECG) signals, with particular emphasis on preserving the ST segment of the original signal. The algorithm consists of two stages of morphological processing. First, the QRS complex and impulsive noise component due to skeletal muscle contractions etc., are detected and removed from the input signal. Second, the corrected QT interval (QTc) and RR interval are used to determine a structuring element. With this structuring element, the same morphological operation as in the first stage is then applied to the QRS-removed signal to obtain and remove the baseline wander. The performance of the algorithm is evaluated with simulated and real ECGs. Compared with an existing morphological method, there is a substantial improvement, especially in reducing distortion of the baseline waveform within the PR and QT intervals.     

On optimizing the backoff interval for random access schemes

To improve channel throughput and the fairness of random access channels, we propose a new backoff algorithm, namely, the sensing backoff algorithm (SBA). A novel feature of the SBA scheme is the sensing mechanism, in which every node modifies its backoff interval according to the results of the sensed channel activities. In particular, every active node sensing a successful transmission decreases its backoff interval by an additive factor of the transmission time of a packet. In order to find the optimum parameters for the SBA scheme, we have studied the optimum backoff intervals as a function of different numbers of active nodes (N) in a single transmission area with pure ALOHA-type channels. We find that the optimum backoff interval should be 4N times the packet transmission time when the random access channel operates under a pure ALOHA scheme. Based on this result, we have calculated numerically the optimum values of the parameters for SBA, which are independent of N. The SBA scheme operates close to the optimum backoff interval. Furthermore, its operation does not depend on a knowledge of N. The optimum backoff interval and the SBA scheme have been studied also by simulation. It is shown that the SBA scheme out-performs other backoff schemes, such as binary exponential backoff (BEB) and multiplicative increase linear decrease (MILD). As a point of reference, the SBA scheme offers a channel capacity of 0.19 when N is 10, while the MILD scheme can only offer 0.125. The performance gain is about 50%.     

Application of linear and nonlinear time series modeling to heartrate dynamics analysis

The linear autoregressive (AR) model is often used to investigate the pathophysiologic mechanisms controlling heart rate (HR) dynamics. This study implemented parametric models new to this field to determine if a more appropriate HR dynamics modeling structure exists. The linear AR and autoregressive-moving average (ARMA) models, and the nonlinear polynomial autoregressive (PAR) and bilinear (BL) models were fit to instantaneous HR time series obtained from nine subjects in the supine position. Model orders were determined by the Akaike Information Criteria (AIC). Model residual variance was used as the primary intermodel comparison criterion, with significance evaluated by a λ² distributed statistic. The BL model best represented the HR dynamics, as its residual variance was significantly (p<0.05) smaller than that of the corresponding AR model for nine out of nine data sets. In all cases, the BL model had a smaller residual variance than either the ARMA or PAR models. The bilinear model was ineffective at data forecasting, however, the authors show that this cannot reflect BL model validity because poor prediction is inherent to the BL model structure. The apparent superiority of the nonlinear bilinear model suggests that future heart rate dynamics studies should put greater emphasis on nonlinear analyses     

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.     

IEEE 802.11n: Joint modulation‐coding and guard interval adaptation scheme for throughput enhancement

IEEE 802.11n is a high‐speed wireless broadband local area networking standard. IEEE 802.11n‐based devices are using some kind of adaptive modulation‐coding (AMC) scheme to adjust its transmission rate according to the radio channel condition. In these devices, however, the concept of guard interval adaptation is not been considered. Normally, orthogonal frequency division multiplexing (OFDM) technology‐based systems are using the guard interval much greater than the length of the channel impulse response. However, many previous works have shown that the choice of the larger guard interval is inefficient in terms of achievable throughput. IEEE802.11n supports using two guard intervals (short = 400 ns or long = 800 ns). Indeed, the shorter guard interval evidently results in intersymbol interference (ISI) and intercarrier interference (ICI), but the gain offered by shortened guard interval may exceed the loss caused by interference. In this paper, we propose a novel but simple solution for the guard interval adaptation joint with an adaptive modulation‐coding scheme to optimize the throughput performance of a wireless local area network (WLAN) system. This paper aims to analyze the effect of joint adaptive modulation‐coding and the guard interval (JAMCGI) algorithm on the WLAN system under bit‐error‐rate (BER) constraints. Simulation results and their analysis show a significant increase in the throughput performance of the WLAN system with our proposed algorithm.     

Selecting sampling interval of transient response for the improved Prony method

A sampling interval selection scheme for use with the improved Prony method is proposed. Since natural frequency extraction is a numerically ill-conditioned problem, a small change in the sampling interval results in a large change in the extracted natural frequencies. From the perturbation of the eigenvectors of the matrix, the accuracy criterion of the extracted natural frequencies is derived. The proposed scheme is validated using natural frequency extraction from the synthetic response, the method of moments (MM) response, and the measured response.     

Prediction of short cardiovascular variability signals based on conditional distribution

A new approach measuring the predictability of a process is proposed. The predictor is defined as the median of the distribution conditioned by a sequence of L - 1 previous samples (i.e., a pattern). A function referred to as the corrected mean squared predictor error is defined to prevent the perfect adequacy to the data (i.e., the decrease to zero of the prediction error), thus avoiding to divide the whole set of data in learning and test sets. This function exhibits a minimum and this minimum is taken as a measure of predictability of the series. The use of the minimization procedure avoids to fix a priori the pattern length L. This approach permits one a reliable measure of predictability on short data sequences (around 300 samples). Moreover, this method, in connection with a surrogate data approach, is useful to detect nonlinear dynamics. The analysis indicates that, in simulated and real data, predictability and nonlinearity measures provide different information. The application of this approach to the analysis of cardiovascular variability series of the heart period (RR interval) and systolic arterial pressure (SAP) shows: 1) SAP series is more predictable than RR interval series; 2) predictability of the RR interval series is larger during tilt, during controlled respiration at 10 breaths/min (bpm) and after high-dose administration of atropine; 3) SAP series is dominated by linear correlation; 4) RR interval series exhibits nonlinear dynamics during controlled respiration at 10 bpm and after low-dose administration of atropine, while it is linear during sympathetic activation produced by tilt and after peripheral parasympathetic blockade caused by high-dose administration of atropine.     

快速反射镜研究现状及未来发展

针对某红外搜索系统快速反射镜设计需求,研究基于十字簧片传动结构与音圈致动器的快速反射镜机电联合仿真技术。建立快速反射镜的机电参数化模型,采用有限元分析法构建柔性结构传动刚度模型,同时建立音圈制动器的电磁驱动模型,并进行关键参数的迭代设计确定最优参数;以Matlab/Simulink为联合仿真平台,建立反射镜动力学仿真接口与电磁驱动仿真接口,结合经典控制模型实现对反射镜机构的联合仿真,并获得系统动态响应的仿真结果。最后通过实验测试验证50 Hz成像周期下回扫补偿残差与相位滞后,其中实测回扫补偿残差0.0365 mrad,相位滞后2.6 ms,虽然高于仿真分析结果但能够满足工程应用的需求;并对系统的开环频响曲线进行对比,中低频幅值响应误差不超过10%。仿真和实验结果表明,该联合仿真技术对于快速反射镜的设计与优化具有重要的理论指导意义。     

Adaptive recovery of a chirped signal using the RLS algorithm

This paper studies the performance of the recursive least squares (RLS) algorithm in the presence of a general chirped signal and additive white noise. The chirped signal, which is a moving average (MA) signal deterministically shifted in frequency at rate ψ, can be used to model a frequency shift in a received signal. General expressions for the optimum Wiener-Hopf coefficients, one-step recovery and estimation errors, noise and lag misadjustments, and the optimum adaptation constant (β_opt) are found in terms of the parameters of the stationary MA signal. The output misadjustment is shown to be composed of a noise (ξ₀Mβ/2) and lag term (κ/(β²ψ²)), and the optimum adaptation constant is proportional to the chirp rate as ψ^2/3 . The special case of a chirped first-order autoregressive (AR1) process with correlation (α) is used to illustrate the effect the bandwidth (1/α) of the chirped signal on the adaptation parameters. It is shown that unlike for the chirped tone, where the β_opt increases with the filter length (M), the adaptation constant reaches a maximum for M near the inverse of the signal correlation (1/α). Furthermore, there is an optimum filter length for tracking the chirped signal and this length is less than (1/α)     

QT variability and HRV interactions in ECG: quantification and reliability

In this paper, a dynamic linear approach was used over QT and RR series measured by an automatic delineator, to explore the interactions between QT interval variability (QTV) and heart rate variability (HRV). A low-order linear autoregressive model allowed to separate and quantify the QTV fractions correlated and not correlated with HRV, estimating their power spectral density measures. Simulated series and artificial ECG signals were used to assess the performance of the methods, considering a respiratory-like electrical axis rotation effect and noise contamination with a signal-to-noise ratio (SNR) from 30 to 10 dB. The errors found in the estimation of the QTV fraction related to HRV showed a nonrelevant performance decrease from automatic delineation. The joint performance of delineation plus variability analysis achieved less than 20% error in over 75% of cases for records presenting SNRs higher than 15 dB and QT standard deviation higher than 10 ms. The methods were also applied to real ECG records from healthy subjects where it was found a relevant QTV fraction not correlated with HRV (over 40% in 19 out of 23 segments analyzed), indicating that an important part of QTV is not linearly driven by HRV and may contain complementary information.     

A note on the confidence interval for the availability ratio

If one is interested in determining the worth of equipment to perform a given task, often the primary concerns are reliability, maintainability, and availability. Availability consider both reliability and maintainability since it is a measure of the ratio of the operating time of the system to the operating time plus the downtime or time to repair. One method commonly used to establish a confidence interval for availability is to establish a confidence interval for the meantime between failure of the equipment and then determine a confidence interval for the meantime to repair the equipment. These two confidence intervals may then be utilized to obtain an “at least” type confidence interval for availability. This approach is not entirely satisfactory since points outside of both of the above mentioned intervals can give points inside the confidence interval for availability. This paper proposes a method for establishing an exact confidence interval for availability under the assumption that the time between failures and the time to repair are independent gamma and lognormal random variables respectively. The method requires computations of the distribution of , where Ut X²(q) and . In order to make the results usuable, tables of the cumulative distribution of W are included.     

Analysis of systems subject to parameter uncertainties: Application of interval analysis

The effects of system parameter uncertainties on system performance are always of great concern to the system designer. It is desirable to knowa priori estimates of the system response, subject to parameter uncertainties. In the following we propose using interval analysis techniques to establish estimates of system performance (e.g., envelopes of time response and frequency response). The results which we obtain constitute generalizations of existing work. Specifically, existing results address systems which are described by linear ordinary differential equations which are endowed with a single parameter belonging to an interval. In the present results we address systems described by linear or nonlinear ordinary difference equations endowed with more than one parameter belonging to intervals.Supported by the National Science Foundation under Grant ECS 88-02924.     

Characterization of interdependency between intracranial pressure and heart variability signals: a causal spectral measure and a generalized synchronization measure

Causal coherence and generalized synchronization (GS) index were extracted from beat-to-beat mean intracranial pressure (ICP) and intervals between consecutive normal sinus heart beats (RR interval) that were recorded from 12 patients undergoing normal pressure hydrocephalus diagnosis. Data were organized into two groups including an ICP B-Wave group and a baseline control group. Maximal classic coherence (CC) between ICP and RR interval within [0.04, 0.15] Hz was found to be significantly greater than zero for both B-Wave and control groups with B-Wave CC greater than that of the baseline group. Causal coherence analysis further revealed that feedforward coherence due to RR interval's effect on ICP always exists for both B-Wave and baseline ICP state and no significant difference exists between two groups. On the other hand, feedback coherence from ICP to RR interval was enhanced during the occurrence of B-Wave. This finding regarding the enhanced directional, from ICP to RR interval, coupling between ICP and RR interval was also confirmed by a modified GS measure.     

区间数TOPSIS法在战场目标优选中的应用

针对作战指挥决策的实际特点,运用区间数和TOPSIS方法探索战场目标优选问题.通过对影响目标优选因素的全面分析,构建了战场目标优选的评价指标体系.在以区间数形式给出各指标评价值的基础上,应用TOPSIS法建立了目标优选的评估模型,并通过实例对该模型的有效性和实用性进行了验证.该方法能够有效结合定性与定量分析,充分综合各指标评价信息,为战场目标优选提供了一种较为有效的方法和途径.     

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.     

Differentially coherent communication with multiple-symbolobservation interval

A novel differential detection technique for multiple phase shift keying (MPSK), which uses multiple-symbol observation interval, is proposed and its performance is analyzed. The proposed technique detects the transmitted symbol with accumulation of the differential signal vector projected on the previously received symbol. As with the multiple-symbol differential detection (MSDD) technique, the performance of the proposed scheme fills the gap between the conventional differential and the ideal coherent detection. The amount of improvement depends on the number of phases M and the number of additional symbol intervals N of observation. Performance analysis and simulation show considerable improvement with only 1 or 2 additional symbol intervals of observation. In addition, the complexity increases linearly with observation interval and is smaller than that of Divsalar and Simon (1990) and of Edbauer (1992)