Combining algorithms in automatic detection of QRS complexes in ECG signals.

QRS complex and specifically R-Peak detection is the crucial first step in every automatic electrocardiogram analysis. Much work has been carried out in this field, using various methods ranging from filtering and threshold methods, through wavelet methods, to neural networks and others. Performance is generally good, but each method has situations where it fails. In this paper, we suggest an approach to automatically combine different QRS complex detection algorithms, here the Pan-Tompkins and wavelet algorithms, to benefit from the strengths of both methods. In particular, we introduce parameters allowing to balance the contribution of the individual algorithms; these parameters are estimated in a data-driven way. Experimental results and analysis are provided on the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia Database. We show that our combination approach outperforms both individual algorithms.     

On the detection of QRS variations in the ECG

Detection of subtle beat-to-beat variations in the morphology of the ECG is complicated by the effects of alignment errors and respiration. A method of directly estimating the alignment error (trigger jitter) from an ECG is derived by relating the variance to the squared slope of the averaged QRS complex. Results based on recordings obtained from 12 normal subjects and alignment performed by the cross-correlation method showed that the alignment errors were dependent upon the choice of the alignment channel, with the best distribution of the errors occurring when alignment was based on the vector magnitude of the three orthogonal leads. The estimated average alignment errors ranged from 0.33-0.42 ms, which were near the optimal value of 0.29 ms based on the sampling rate of 1000 samples/s. It was shown that the effects of respiration could be reduced by normalizing the amplitude of the QRS complexes. It was also estimated that a significant proportion of the variation (0.54-0.67) in the normalized ECG's could be attributed to alignment errors and noise     

非线性功放对数字调制信号的影响研究

功放的非线性会带来调制信号的性能恶化,基于simulink建模,对比仿真了恒包络调制MSK和非恒包络调制BPSK,在功放线性和非线性区域时的误码性能,以及在带宽受限条件下,相邻信道干扰对信号误码性能影响.仿真结果表明,功放饱和对非恒包络调制信号影响更大,尤其是信道间隔较小的情况下,非恒包络调制信号的误码性能恶化严重.该研究为数字通信系统的工程设计提供了一种可借鉴分析方法.     

Fast number-theoretic transforms for digital filtering

A special number-theoretic transform that can be computed, using a high-radix fast Fourier transform, is defined on primes of the form (2n ? 1) 2n +1. Methods for finding these primes and the primitive dth roots of unity in a field modulo such primes are also included.     

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...     

Speed and error analysis of digital convolution using rectangular transforms

This paper discusses the development of a FORTRAN program to efficiently calculate digital convolution by using the rectangular transform of Agarwal and Cooley. Both a theoretical analysis and experimental measurements were made to compare the running time and the numerical quantization error of the rectangular transform with an efficient DFT approach.     

Spectral analysis of variable-length coded digital signals

Spectral analysis is performed for a digital message produced by a variable-length encoder driven by a stationary memoryless source. In particular, closed form expressions are derived for both the continuous and the discrete part of the spectral density. The continuous part turns out to be a rational function ofz=exp (j2pi fT),whereTis the symbol period, whereas the discrete part exhibits in general spectral lines at multiple integers of(lambda_{0}T)^{-1}, wherelambda_{0}is the greatest common divisor of the codeword lengths. As an application of the theory explicit formulas are derived for the spectra of BnZS and HDBncodes.     

Comparison of discrete wavelet and Fourier transforms for ECG beatclassification

Two feature extraction methods, Fourier and wavelet analyses for ECG beat classification, are comparatively investigated. ECG features are searched by dynamic programming according to the divergence values. 10 types of ECG beat from an MTT-BIH database are classified with a success of 97% using a restricted Coulomb energy neural network trained by genetic algorithms     

Computing linear transforms of symbolic signals

Signals that represent information may be classified into two forms: numeric and symbolic. Symbolic signals are discrete-time sequences that at, any particular index, have a value that is a member of a finite set of symbols. Set membership defines the only mathematical structure that symbolic sequences satisfy. Consequently, symbolic signals cannot be directly processed with existing signal processing algorithms designed for signals having values that are elements of a field (numeric signals) or a group. Generalizing an approach due to Stoffer (see Biometrika, vol.85, p.201-213, 1998), we extend time-frequency and time-scale analysis techniques to symbolic signals and describe a general linear approach to developing processing algorithms for symbolic signals. We illustrate our techniques by considering spectral and wavelet analyses of DNA sequences     

Convergence analysis of finite alphabet beamformers for digital cochannel signals

In this paper, we examine the convergence behavior of finite alphabet (FA) beamformers. The two most popular implementations of FA beamformers for digital communication signals are the iterative least-squares with projection (ILSP) and the minimum mean-square error (MMSE) beamformer. To facilitate the analysis, for the binary communications case, we derive closed-form expressions for the mean weight vector, signal-to-noise ratio, and signal-to-interference ratio for both the ILSP and MMSE beamformers in terms of the bit-error rate (BER) performance at each iteration. Next, we generalize the analysis for the M-ary pulse-amplitude modulation and M-ary phase-shift keying cases. We show that both ILSP and MMSE beamformers have previously unreported bias terms in the array response vectors which are functions of the BER for each iteration. Furthermore, as the BER becomes arbitrarily small, we show that our solutions converge to the well-known asymptotic expressions widely published in the literature. Next, we provide a geometric interpretation of the effects of the noise bias vector in terms of angles between subspaces. Based on our analysis, we were able to develop necessary and sufficient conditions for convergence in the mean. We conclude with Monte-Carlo simulations to validate our analysis.     

Nonlinear processing and analysis of angular signals

Physical quantities referring to angles, like vector direction, color hue, etc., exhibit an inherently periodic nature. Due to this periodicity, digital filters and edge operators proposed for data on the line cannot be applied on such data. We introduce filters for angular signals (circular mean, circular median, circular a-trimmed mean, circular modified trimmed mean). Particular emphasis is given to the circular median filter, for which some interesting properties are derived. We also use estimators of circular dispersion to introduce edge detectors for angular signals. Three variations for the extension of quasirange to circular data are proposed, and expressions for their output PDF are derived. These “circular” quasiranges have good and user-controlled properties as edge detectors in noisy angular signals. The performance of the proposed edge operators is evaluated on angular edges, using certain quantitative criteria. Finally, a series of experiments featuring one-dimensional (1-D) angular signals and hue images is used to illustrate the operation of the new filters and edge detectors     

Fractional Fourier transforms of hypercomplex signals

An overview is given to a new approach for obtaining generalized Fourier transforms in the context of hypercomplex analysis (or Clifford analysis). These transforms are applicable to higher-dimensional signals with several components and are different from the classical Fourier transform in that they mix the components of the signal. Subsequently, attention is focused on the special case of the so-called Clifford-Fourier transform where recently a lot of progress has been made. A fractional version of this transform is introduced and a series expansion for its integral kernel is obtained. For the case of dimension 2, also an explicit expression for the kernel is given.     

Detection of ECG characteristic points using wavelet transforms

An algorithm based on wavelet transforms (WT's) has been developed for detecting ECG characteristic points. With the multiscale feature of WT's, the QRS complex can be distinguished from high P or T waves, noise, baseline drift, and artifacts. The relation between the characteristic points of ECG signal and those of modulus maximum pairs of its WT's is illustrated. By using this method, the detection rate of QRS complexes is above 99.8% for the MIT/BIH database and the P and T waves can also be detected, even with serious base line drift and noise     

一种心电信号QRS复杂形态自动分析算法

QRS形态分析是心电信号自动分析中的关键步骤之一.现有QRS形态分析算法大多是基于时频变换分析或基于基元线段句法识别的.其中,基于时频变换分析的方法难以处理复杂波形和异常波形;而基于基元线段句法识别的算法则易丢失波形细节.针对这一问题,本文提出了一种基于QRS波群关键点和有限自动机的QRS形态分析算法.首先,采用二次多项式曲线对输入的心电信号进行分段最小二乘法逼近.然后,通过分析各段曲线的单调性、陡峭程度以及曲线方向等特征,实现QRS波群中峰点、谷点、边界点等关键点的检测,同时提取各关键点的幅度和时间信息,并判断出关键点的类型.最后,构造了一个有限自动机,以带属性的QRS波群关键点序列作为输入,实现了QRS形态的识别.经MIT-BIH心律失常数据库验证,本文算法可处理含有任意多个子波的QRS波群,正确识别出其各种复杂形态,准确分析出如切迹、顿挫等形态细节.     

Nonlinear wavelet transforms for image coding via lifting

We investigate central issues such as invertibility, stability, synchronization, and frequency characteristics for nonlinear wavelet transforms built using the lifting framework. The nonlinearity comes from adaptively choosing between a class of linear predictors within the lifting framework. We also describe how earlier families of nonlinear filter banks can be extended through the use of prediction functions operating on a causal neighborhood of pixels. Preliminary compression results for model and real-world images demonstrate the promise of our techniques.     

Recursive algorithm for efficient representation of ECG signals

A method is presented for representing signals made up of discrete component waves separated by iso-electric regions such as electrocardiogram (EGG), respiratory and blood pressure signals. The computational complexity is minimised by treating the discrete cosine transform of a group of component waves to be the sum of a finite number of biphasic rational functions     

Integrated approach for fetal QRS detection

Fetal magnetocardiography provides reliable signals of the fetal heart dynamics with high temporal resolution that can be used in a clinical setting. We present a robust Hilbert transform method for extraction of the fetal heart rate. Our method may be applied to signals derived from a single channel or an array of channels. In the case of multichannel data, the channels can be combined to improve signal-to-noise ratio for the extraction of fetal heart data. The method is inherently insensitive to fetal position or movement and, in addition, can be automated. We demonstrate that the determination of R-wave timing is relatively insensitive to waveform morphology. The method can also be applied if the data were preprocessed by independent component analysis (ICA). We compared the Hilbert method, ICA, ICA $+$ Hilbert, and raw signals and found that the Hilbert method gave the best overall performance. We demonstrated that there were approximately 171 errors in 46 789 fetal heart beats.      

适用于低频率生理信号片上谱分析的低功耗数字芯片设计

本文针对人体传感网络的应用定制设计了一款基于片上处理技术的低功耗的数字芯片。该数字芯片主要包括一个用于生物医学信号谱分析的可扩展FFT处理模块,一个授权的ARM7TDMI内核和其他外围IP模块。该FFT采用了一种新的混合结构,存储器优化,减少乘法器,高效率的地址产生技术来降低功耗。芯片经过流片,并实际测试后表明,性能方面能满足生物医学信号处理要求,功耗方面,在1.8v输入下,功耗仅0.69mW @ 1MHz,该芯片计算256点FFT的能量是基于ARM7嵌入式平台计算所耗功耗的0.9%,同时,与业界主流ASIC实现的FFT相比,也具有低功耗优势。