Estimation of QRS complex power spectra for design of a QRS filter

We present power spectral analysis of ECG waveforms as well as isolated QRS complexes and episodes of noise and artifact. The power spectral analysis shows that the QRS complex could be separated from other interfering signals. A bandpass filter that maximizes the signal (QRS complex)-to-noise (T-waves, 60 Hz, EMG, etc.) ratio would be of use in many ECG monitoring instruments. We calculate the coherence function and, from that, the signal-to-noise ratio. Upon carrying out this analysis on experimentaly obtained ECG data, we observe that a bandpass filter with a center frequency of 17 Hz and a Q of 5 yields the best signal-to-noise ratio.     

Digital fractional order operators for R-wave detection in electrocardiogram signal

In this study, we present an effective R-wave detection method in the QRS complex of the electrocardiogram (ECG) based on digital differentiation and integration of fractional order. The detection algorithm is performed in two steps. The pre-processing step is based on a fractional order digital band-pass filter whose fractional order is obtained by maximising the signal to noise ratio of the ECG signal, followed by a five points differentiator of fractional order 1.5 then the squaring transformation and the smoothing are used to generate peaks corresponding to the ECG parts with high slopes. The detection step is a new and simple strategy which is also based on fractional order operators for the localisation of the R waves. The MIT/BIH arrhythmia database is used to test the effectiveness of the proposed method. The algorithm has provided very good performance and has achieved about 99.86% of the detection rate for the standard database. The results obtained are presented, discussed and compared to the most recent and efficient R-wave detection algorithms.     

A real-time microprocessor QRS detector system with a 1-ms timingaccuracy for the measurement of ambulatory HRV

The design, test methods, and results of an ambulatory QRS detector are presented. The device is intended for the accurate measurement of heart rate variability (HRV) and reliable QRS detection in both ambulatory and clinical use. The aim of the design work was to achieve high QRS detection performance in terms of timing accuracy and reliability, without compromising the size and power consumption of the device. The complete monitor system consists of a host computer and the detector unit. The detector device is constructed of a commonly available digital signal processing (DSP) microprocessor and other components. The QRS detection algorithm uses optimized prefiltering in conjunction with a matched filter and dual edge threshold detection. The purpose of the prefiltering is to attenuate various noise components in order to achieve improved detection reliability. The matched filter further improves signal-to-noise ratio (SNR) and symmetries the QRS complex for the threshold detection, which is essential in order to achieve the desired performance. The decision for detection is made in real-time and no search-back method is employed. The host computer is used to configure the detector unit, which includes the setting of the matched filter impulse response, and in the retrieval and postprocessing of the measurement results. The QRS detection timing accuracy and detection reliability of the detector system was tested with an artificially generated electrocardiogram (EGG) signal corrupted with various noise types and a timing standard deviation of less than 1 ms was achieved with most noise types and levels similar to those encountered in real measurements. A QRS detection error rate (ER) of 0.1 and 2.2% was achieved with records 103 and 105 from the MIT-BIH Arrhythmia database, respectively     

Restoration of lossy compressed noisy images

Noise degrades the performance of any image compression algorithm. However, at very low bit rates, image coders effectively filter noise that may he present in the image, thus, enabling the coder to operate closer to the noise free case. Unfortunately, at these low bit rates the quality of the compressed image is reduced and very distinctive coding artifacts occur. This paper proposes a combined restoration of the compressed image from both the artifacts introduced by the coder along with the additive noise. The proposed approach is applied to images corrupted by data-dependent Poisson noise and to images corrupted by film-grain noise when compressed using a block transform-coder such as JPEG. This approach has proved to be effective in terms of visual quality and peak signal-to-noise ratio (PSNR) when tested on simulated and real images.     

Rejection of multiple tone interference in DS spread-spectrumsystems employing minimum redundant transversal filters

A direct-sequence spread-spectrum (DSSS) receiver using a new transversal filter structure called minimum redundant transversal filter (MRTF), for combating narrow-band interference is proposed. The signal-to-noise (SNR) ratio improvement factors are derived under the conditions of multiple tone interference. The effectiveness of the new suppression filter is examined and the results obtained reveal the fact that the MRTF structure leads to superior performance especially when the frequencies of the jamming tones are near the carrier frequency of the modulated signal     

Computer analysis of the electrocardiogram during esophageal pacingcardiac stress

It has been estimated that 15 to 30% of patients with suspected or known coronary artery disease are unable to perform an adequate exercise stress test due to a variety of reasons such as obesity, poor physical condition, claudication, etc. Transesophageal atrial pacing has been proposed as a noninvasive alternative for inducing cardiac stress in patients who cannot exercise. Although computer analysis is commonly employed to analyze the electrocardiogram (ECG) during the conventional exercise stress test, the surface ECG recorded during transesophageal atrial pacing is contaminated with large pacing artifacts which confound beat identification by standard computer software. We report the development of a robust signal processing algorithm for interpretation of the surface ECG during transesophageal atrial pacing stress. The algorithm employs novel schemes using both linear and nonlinear transformations to detect and differentiate between the pacing artifact and QRS complex even in difficult situations where the pacing artifact is in proximity to or superimposed on the QRS complex. The algorithm uses sophisticated logic for automatic recognition of sustained capture. It subsequently calculates beat-by-beat and average (over five beats) ST segment amplitude and slope. The algorithm also reports the instantaneous heart rate, RR interval, pace-to-R interval, R-wave amplitude, and estimated sinus node recovery time upon loss of sustained capture. The limitations of present exercise ECG computer methods in processing the ECG during transesophageal atrial pacing stress are evaluated and significantly improved performance by our algorithm is demonstrated.     

Image detection under low-level illumination

Image detection under low-light-level conditions is treated as a hypothesis-testing problem in which the observations are modeled as a shot-noise process. Since computing the likelihood ratio for shot-noise processes is not feasible, the use of a one-dimensional test statistic obtained by filtering and sampling the observations is proposed. The filter is chosen to maximize a generalized signal-to-noise ratio. The likelihood ratio for the one-dimensional test statistic is evaluated numerically by inverting the corresponding characteristic function under each hypothesis.     

频点自跟踪自适应频率估计器性能研究

基于二阶自适应陷波滤波器的频率估计器是工程上常用的单频信号频率估计器,其前提是参考信号相对输入信号频偏较小.随着频偏的增大,自适应频率估计的偏差和方差也随之增大.本文分析了自适应频率估计器的性能及频偏的影响,得出了自适应频率估计方差下限的公式及达到此下限的条件.提出了频点自跟踪自适应频率估计器.仿真与理论结果均表明相同条件下该估计器较自适应频率估计器明显减小了估计偏差和方差,在中等信噪比下估计方差可减小到自适应频率估计方差的百分之一.只要信噪比不甚低,频点自跟踪频率估计是无偏的且估计方差接近理论下限.     

Quasi-Optimum Digital Phase-Locked Loops

Quasi-optimum digital phase-locked loops (DPLL) are derived utilizing nonlinear estimation theory. Nonlinear approximations are employed to yield realizable loop structures. Baseband equivalent loop gains are derived, which, under high signal-to-noise ratio conditions may be calculated off line. Additional simplifications are made that permit the application of the Kalman filter algorithms to determine the minimum mean-square error (MSE) loop filter. Consideration is given to sampling rate and implementation requirements. Performance is evaluated by a theoretical analysis and by simulation. Theoretical and simulated results are discussed and a comparison to analog results is made.     

Using wavelet transform and fuzzy neural network for VPC detection from the Holter ECG

A novel method for detecting ventricular premature contraction (VPC) from the Holter system is proposed using wavelet transform (WT) and fuzzy neural network (FNN). The basic ideal and major advantage of this method is to reuse information that is used during QRS detection, a necessary step for most ECG classification algorithm, for VPC detection. To reduce the influence of different artifacts, the filter bank property of quadratic spline WT is explored. The QRS duration in scale three and the area under the QRS complex in scale four are selected as the characteristic features. It is found that the R wave amplitude has a marked influence on the computation of proposed characteristic features. Thus, it is necessary to normalize these features. This normalization process can reduce the effect of alternating R wave amplitude and achieve reliable VPC detection. After normalization and excluding the left bundle branch block beats, the accuracies for VPC classification using FNN is 99.79%. Features that are extracted using quadratic spline wavelet were used successfully by previous investigators for QRS detection. In this study, using the same wavelet, it is demonstrated that the proposed feature extraction method from different WT scales can effectively eliminate the influence of high and low-frequency noise and achieve reliable VPC classification. The two primary advantages of using same wavelet for QRS detection and VPC classification are less computation and less complexity during actual implementation.     

Reliability of Human Visual Signal Detection in the Presence of Noise

This report is concerned with the reliability of human detection of certain classes of signals of simple shape embedded in low-pass filtered white gaussian noise recorded on paper. Results are compared with performance of an optimum matched filter detector which operated on the same set of data. The signal-to-noise ratio obtained at the output of such a filter can be used to predict the reliability of detection and the false alarm rate in subjects' performance. For signal-to-noise ratio greater than one, subjects make fewer errors (i.e., misses plus false alarms) than the optimum filter, and the difference is a monotonically decreasing function of the signal-to-noise ratio. Factors concerned with these two observations are discussed.     

Fast-converging minimum frequency error RLS lattice filter for narrowband interference with discrete frequency steps

This paper discusses the fundamental convergence and frequency tracking properties of the recursive-least-squares (RLS) lattice filter in the presence of narrowband interference (NBI) whose frequency varies in discrete steps. It is shown for filters of this type, that the residual forward energy (RFE) after a frequency transition is a function of the input signal-to-noise ratio (SNR), separation of the sequential frequencies and the filter time constant and is exponentially decaying in nature. Reducing the RFE is important in removing unwanted transient artefacts from the desired signal. The convergence behaviour of the RLS algorithm based on a posteriori estimation errors is analysed under a number of conditions by varying the SNR and frequency step size. In order to limit the impact of the RFE while maintaining a minimum frequency tracking error in steady conditions, a fast-converging minimum frequency error (FCMFE) RLS lattice filter is suggested. For comparison, a least-mean-square (LMS) based gradient-adaptive lattice (GAL) filter is also analysed for this class of narrowband interference.     

基于高阶累积量和多相滤波器联合处理的PRBC-LFM参数提取

针对传统算法在进行伪码-线性调频信号参数提取时存在的条件限制和在低信噪比下提取精度不高的问题,文章提出了一种利用多相滤波器组和高阶累积量相结合进行参数的提取方法,该方法能够完成伪码-线性调频信号（PRBC-LFM）参数在高斯噪声下的提取。首先通过多相滤波器组能快速完成信号在频域上的均匀划分,接着对子带信号进行三阶累积量的短时估计,从而达到了抑制高斯噪声和实现在低信噪比下进行信号参数的提取的目的。测试结果表明,在低信噪比下使用该方法进行参数估计时精度很高。     

Chip-delay locked matched filter for DS-CDMA systems using longsequence spreading

This paper considers an improved single-user detection technique for asynchronous direct-sequence code-division multiple-access (DS-CDMA) systems using long sequence spreading (random-CDMA) Most of the known detection schemes for DS-CDMA suffer from either poor performance under power-imbalance (near-far like) conditions, excessive complexity, or incompatibility with systems employing long sequence spreading. To address these problems, this paper considers a signal-to-noise ratio maximizing linear time-invariant filter for one-shot bit symbol detection exploiting some information about the interferers. This filter, referred to as the chip-delay locked matched filter (CLMF), exploits the cyclostationarity in multiple-access interference, and it can offer good near-far resistance while remaining suitable for systems with long sequence spreading. The CLMF requires knowledge of interferers chip delays and signal powers; however, knowledge of their pseudonoise sequences is unnecessary. This paper also demonstrates the improvement in performances offered by the CLMF over other single-user receivers such as the conventional matched filter and noise-whitening matched filter performance is evaluated in terms of probability of outage for single-rate and dual-rate DS-CDMA systems using bandwidth-efficient chip pulses, over a single-path additive white Gaussian noise channel. Errors in the interferer chip delay estimates degrade the CLMF performance. However, if the root-mean-square value of these errors is less than 5% of the chip interval, then this degradation is small     

A nonlinear filter for estimating a sinusoidal signal and itsparameters in white noise: on the case of a single sinusoid

A nonlinear filter is proposed for estimating a complex sinusoidal signal and its parameters (frequency, amplitude, and phase) from measurements corrupted by white noise. This filter is derived by applying an extended complex Kalman filter (ECKF) to a nonlinear stochastic system whose state variables are a function of its frequency and a sample of an original signal, and then, proof of the stability is given in the case of a single complex sinusoid. Simulations demonstrate that the proposed nonlinear filter is effective as a method for estimating a single complex sinusoid and its frequency under a low signal-to-noise ratio (SNR). In addition, the effect of the initial condition in the filter on frequency estimation is also discussed     

四象限精跟踪光轴检测模型与误差分析

研究了四象限精跟踪光轴检测模型,推导出大气环境下系统固有分辨精度与细分精度的关系,在此基础上指出提升检测系统固有分辨率的方法,有效降低光强闪烁影响;同时,讨论了大气湍流误差、背景光学误差、电子干扰误差产生的原因以及与系统信噪比之间的关系,针对不同误差给出相应补偿办法并进行仿真。仿真结果表明,背景光误差与信标光信噪比、光轴偏差量直接相关;减小系统滤波器带宽,可抑制电子干扰误差;弱大气湍流条件下,增大接收光学口径,能有效抑制信号衰落。     

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     

Performance analysis of LMS adaptive prediction filters

The conditions required to implement real-time adaptive prediction filters that provide nearly optimal performance in realistic input conditions are delineated. The effects of signal bandwidth, input signal-to-noise ratio (SNR), noise correlation, and noise nonstationarity are explicitly considered. Analytical modeling, Monte Carlo simulations and experimental results obtained using a hardware implementation are utilized to provide performance bounds for specified input conditions. It is shown that there is a nonlinear degradation in the signal processing gain as a function of the input SNR that results from the statistical properties of the adaptive filter weights. The stochastic properties of the filter weights ensure that the performance of the adaptive filter is bounded by that of the optimal matched filter for known stationary input conditions     

On an aspect of matched filters in continuous and discrete time

In this paper we present an approach for improving the output signal-to-noise power ratio of a discrete-time matched filter by employing a continuous-time filter, even though the continuous-time signal input is imperfectly known. It is shown that is it possible to design a continuous-time filter which is robust to inexact knowledge of the signal input; moreover, the robustness property does not severely compromise performance. In fact, it is shown that the resulting filter possesses an output signal-to-noise power ratio which upper bounds that of the discrete-time filter.     

Matched Filters in Nerve Conduction Velocity Estimation

The evoked response signal-to-noise ratio in peripheral sensory nerves is of the order of one or less. To reduce noise induced errors in the nerve conduction velocity measurement, signal averaging is employed. The number of responses required depends upon the signal-to-noise ratio and the acceptable error. It is proposed to use maximum likelihood estimators to improve the signal-to-noise ratio and hence, reduce the number of responses required. The matched filter and generalized cross correlator are studied and noise performance equations obtained. It is shown that the matched filter offers a 6.8 dB improvement over the conventional 5 kHz band-limiting filter. It is further shown that the matched filter as a prefilter in the generalized cross correlator gives the optimum correlator configuration for the low signal-to-noise ratio case. These results are verified with experiments.