Correlations between short-time Fourier- and continuous wavelet transforms in the analysis of localized back and hip muscle fatigue during isometric contractions.

The aims of the current study were to examine the stationarities of surface electromyographic (EMG) signals obtained from eight bilateral back and hip muscles during a modified Biering-Sørensen test, and to investigate whether short-time Fourier (STFT) and continuous wavelet transforms (CWT) provided similar information with regard to EMG spectral parameters in the analysis of localized muscle fatigue. Twenty healthy subjects participated in the study after giving their informed consent. Reverse arrangement tests showed that 91.6% of the EMG signal epochs demonstrated no significant trends (all p > 0.05), meaning 91.6% of the EMG signal epochs could be considered as stationary signals. Pearson correlation coefficients showed that STFT and CWT in general provide similar information with respect to the EMG spectral variables during isometric back extensions, and as a consequence STFT can still be used.     

Feasibility of spectral CT imaging for the detection of liver lesions with gold-based contrast agents – A simulation study

PurposeTo demonstrate the feasibility of gold-specific spectral CT imaging for the detection of liver lesions in humans at low concentrations of gold as targeted contrast agent.MethodsA Monte Carlo simulation study of spectral CT imaging with a photon-counting and energy-resolving detector (with 6 energy bins) was performed in a realistic phantom of the human abdomen. The detector energy thresholds were optimized for the detection of gold. The simulation results were reconstructed with the K-edge imaging algorithm; the reconstructed gold-specific images were filtered and evaluated with respect to signal-to-noise ratio and contrast-to-noise ratio (CNR).ResultsThe simulations demonstrate the feasibility of spectral CT with CNRs of the specific gold signal between 2.7 and 4.8 after bilateral filtering. Using the optimized bin thresholds increases the CNRs of the lesions by up to 23% compared to bin thresholds described in former studies.ConclusionsGold is a promising new CT contrast agent for spectral CT in humans; minimum tissue mass fractions of 0.2 wt% of gold are required for sufficient image contrast.     

肌肉动态收缩期间表面肌电信号的时频分析

介绍了用于肌肉动态收缩期间非平稳表面肌电信号的时频分析方法。用短时傅里叶变换、Wigner-Ville分布及Choi-Williams分布计算了表面肌电信号的时频分布,用于信号频率内容随时间演化的可视化观察。通过计算瞬时频谱参数,对肌肉疲劳的电表现进行量化描述。分析了反复性的膝关节弯曲和伸展运动期间从股外侧肌所记录的表面肌电信号。发现和在静态收缩过程中观察到的平均频率线性下降不同,在动态收缩期间瞬时平均频率的变化过程是非线性的并且更为复杂,且与运动的生物力学条件有关。研究表明将时频分析技术应用于动态收缩期间的表面肌电信号可以增加用传统的频谱分析技术不能得到的信息。     

Stochastic resonance in psychophysics and in animal behavior

 A recent analysis of the energy detector model in sensory psychophysics concluded that stochastic resonance does not occur in a measure of signal detectability (d′), but can occur in a percent-correct measure of performance as an epiphenomenon of nonoptimal criterion placement [Tougaard (2000) Biol Cybern 83: 471–480]. When generalized to signal detection in sensory systems in general, this conclusion is a serious challenge to the idea that stochastic resonance could play a significant role in sensory processing in humans and other animals. It also seems to be inconsistent with recent demonstrations of stochastic resonance in sensory systems of both nonhuman animals and humans using measures of system performance such as signal-to-noise ratio of power spectral densities and percent-correct detections in a two-interval forced-choice paradigm, both closely related to d′. In this paper we address this apparent dilemma by discussing several models of how stochastic resonance can arise in signal detection systems, including especially those that implement a “soft threshold” at the input transform stage. One example involves redefining d′ for energy increments in terms of parameters of the spike-count distribution of FitzHugh–Nagumo neurons. Another involves a Poisson spike generator that receives an exponentially transformed noisy periodic signal. In this case it can be shown that the signal-to-noise ratio of the power spectral density at the signal frequency, which exhibits stochastic resonance, is proportional to d′. Finally, a variant of d′ is shown to exhibit stochastic resonance when calculated directly from the distributions of power spectral densities at the signal frequency resulting from transformation of noise alone and a noisy signal by a sufficiently steep nonlinear response function. All of these examples, and others from the literature, imply that stochastic resonance is more than an epiphenomenon, although significant limitations to the extent to which adding noise can aid detection do exist. Received: 22 January 2001 / Accepted in revised form: 8 March 2002     

Time and frequency domain responses of the mechanomyogram and electromyogram during isometric ramp contractions: a comparison of the short-time Fourier and continuous wavelet transforms.

The purposes of this study were to examine the mechanomyographic (MMG) and electromyographic (EMG) time and frequency domain responses of the vastus lateralis (VL) and rectus femoris (RF) muscles during isometric ramp contractions and compare the time-frequency of the MMG and EMG signals generated by the short-time Fourier transform (STFT) and continuous wavelet transform (CWT). Nineteen healthy subjects (mean+/-SD age=24+/-4 years) performed two isometric maximal voluntary contractions (MVCs) before and after completing 2-3, 6-s isometric ramp contractions from 5% to 100% MVC with the right leg extensors. MMG and surface EMG signals were recorded from the VL and RF muscles. Time domains were represented as root mean squared amplitude values, and time-frequency representations were generated using the STFT and CWT. Polynomial regression analyses indicated cubic increases in MMG amplitude, MMG frequency, and EMG frequency, whereas EMG amplitude increased quadratically. From 5% to 24-28% MVC, MMG amplitude remained stable while MMG frequency increased. From 24-28% to 76-78% MVC, MMG amplitude increased rapidly while MMG frequency plateaued. From 76-78% to 100% MVC, MMG amplitude plateaued (VL) or decreased (RF) while MMG frequency increased. EMG amplitude increased while EMG frequency changed only marginally across the force spectrum with no clear deflection points. Overall, these findings suggested that MMG may offer more unique information regarding the interactions between motor unit recruitment and firing rate that control muscle force production during ramp contractions than traditional surface EMG. In addition, although the STFT frequency patterns were more pronounced than the CWT, both algorithms produced similar time-frequency representations for tracking changes in MMG or EMG frequency.     

An optimum algorithm in pathological voice quality assessment using wavelet-packet-based features,linear discriminant analysis and support vector machine

Voice impairments, attention to increased unhealthy social behavior and voice abuse, have been increasing dramatically. Therefore, diagnosis of voice diseases has an important role in the opportune treatment of pathologic voices. This paper presents an extensive study in identification of different voice disorders which their origin is in the vocal folds. Firstly, a qualitative study is applied based on short-time Fourier transform (STFT) and continuous wavelet transform (CWT) in order to investigate their aptitude in the presentation of discriminative features to identify disordered voices from normal ones. Therefore, wavelet packet transform (WPT) for their ability to analyze scrutinizingly a signal at several levels of resolution is chosen as strong speech signal parameterization method. The ability of energy and entropy features, obtained from the coefficients in the output nodes of the optimum wavelet packet tree, is investigated. Linear discriminant analysis (LDA) and principal component analysis (PCA) are evaluated as feature dimension reduction methods in order to optimize recognition algorithm. The performance of each structure is evaluated in terms of the accuracy, sensitivity, specificity, and area under the receiver operating curve (AUC). Eventually, entropy features in the sixth level of WPT decomposition along with feature dimension reduction by LDA and a support vector machine-based classification method is the most optimum algorithm that leads to the recognition rate of 100% and AUC of 100%. Proposed system clearly outperforms previous works in both respect of accuracy and reduction of residues; which may lead in full accuracy and high speed diagnosis procedure.     

Stochastic resonance and signal detection in an energy detector – implications for biological receptor systems

Stochastic resonance is demonstrated in a simple energy detector model, as a non-monotonic relationship between signal-to-noise ratio and detection of a sinusoid signal in bandpass-limited Gaussian noise. The behaviour of the model detecting signals of various intensities and signal-to-noise ratios was investigated. Significant improvement in detection was obtained by adding noise for mean signal intensities below the detection criterion of the detector. The range of usable noise levels, however, may be too small to be biologically meaningful. It is demonstrated that improving detection in the analysed model by adding noise to an otherwise undetectable signal is only at best as efficient as what can be obtained by adjusting the criterion to the signal-to-noise ratio. Improving detection by means of stochastic resonance is thus a sub-optimal strategy. It is speculated whether a demonstration of stochastic resonance in a biological system indicates any adaptive significance. More than anything, it indicates the presence of a mismatch between receptor sensitivity and the signal-to-noise ratio of the experiment, not the cause of this mismatch. Received: 22 December 1999 / Accepted in revised form: 14 April 2000     

Metrics from nonlinear dynamics adapted for characterizing the behavior of nonequilibrium enzymatic rate functions

Several metrics from nonlinear dynamics and statistical mechanics have been characterized on computer-generated number series with various signal-to-noise ratios, demonstrating their individual reliability as a function of sample size and their relationships to each other. The root mean square (RMS) evaluates amplitude, and the power spectral density (PSD) provides a visual display of the frequency spectrum; both measures have very high reliability even for an N as low as 50. The Fractal Dimension (D) is shown to converge rapidly and also to be reliable when N is as low as 50. These three measures (RMS, PSD, and D) have been applied to the complex kinetics of tyrosine hydroxylase time courses (50-point curves) at various BH4 concentrations (near physiological, but far from equilibrium levels). Recently developed measures of spectral entropy and the Liapunov Exponent, -lambda are also characterized.     

基于独立元分析和联合小波熵检测多导联ECG信号的QRS

QRS波群的准确定位是ECG信号自动分析的基础。为提高QRS检测率,提出一种基于独立元分析（ICA）和联合小波熵（CWS）检测多导联ECG信号QRS的算法。ICA算法从滤波后的多导联ECG信号中分离出对应心室活动的独立元;然后对各独立元进行连续小波变换（CWT）,重构小波系数的相空间,结合相空间中的QRS信息对独立元排序;最后检测排序后独立元的CWS得到QRS信息。实验对St.Petersburg12导联心率失常数据库及64导联犬心外膜数据库测试,比较本文算法与单导联QRS检测算法和双导联QRS检测算法的性能。结果表明,该文算法的性能最好,检测准确率分别为99.98%和100%。     

Time-frequency filtering of MEG signals with matching pursuit.

Time-frequency signal analysis based on various decomposition techniques is widely used in biomedical applications. Matching Pursuit is a new adaptive approach for time-frequency decomposition of such biomedical signals. Its advantage is that it creates a concise signal approximation with the help of a small set of Gabor atoms chosen iteratively from a large and redundant set. In this paper, the usage of Matching Pursuit for time-frequency filtering of biomagnetic signals is proposed. The technique was validated on artificial signals and its performance was tested for varying signal-to-noise ratios using both simulated and real MEG somatic evoked magnetic field data.     

Noise Reduction in Electron Tomographic Reconstructions Using Nonlinear Anisotropic Diffusion

Electron tomography is a powerful technique capable of giving unique insights into the three-dimensional structural organization of pleomorphic biological objects. However, visualization and interpretation of the resulting volumetric data are hampered by an extremely low signal-to-noise ratio, especially when ice-embedded biological specimens are investigated. Usually, isosurface representation or volume rendering of such data is hindered without any further signal enhancement. We propose a novel technique for noise reduction based on nonlinear anisotropic diffusion. The approach combines efficient noise reduction with excellent signal preservation and is clearly superior to conventional methods (e.g., low-pass and median filtering) and invariant wavelet transform filtering. The gain in the signal-to-noise ratio is verified and demonstrated by means of Fourier shell correlation. Improved visualization performance after processing the 3D images is demonstrated with two examples, tomographic reconstructions of chromatin and of a mitochondrion. Parameter settings and discretization stencils are presented in detail.     

Quantifying Neural Oscillatory Synchronization: A Comparison between Spectral Coherence and Phase-Locking Value Approaches

Synchronization or phase-locking between oscillating neuronal groups is considered to be important for coordination of information among cortical networks. Spectral coherence is a commonly used approach to quantify phase locking between neural signals. We systematically explored the validity of spectral coherence measures for quantifying synchronization among neural oscillators. To that aim, we simulated coupled oscillatory signals that exhibited synchronization dynamics using an abstract phase-oscillator model as well as interacting gamma-generating spiking neural networks. We found that, within a large parameter range, the spectral coherence measure deviated substantially from the expected phase-locking. Moreover, spectral coherence did not converge to the expected value with increasing signal-to-noise ratio. We found that spectral coherence particularly failed when oscillators were in the partially (intermittent) synchronized state, which we expect to be the most likely state for neural synchronization. The failure was due to the fast frequency and amplitude changes induced by synchronization forces. We then investigated whether spectral coherence reflected the information flow among networks measured by transfer entropy (TE) of spike trains. We found that spectral coherence failed to robustly reflect changes in synchrony-mediated information flow between neural networks in many instances. As an alternative approach we explored a phase-locking value (PLV) method based on the reconstruction of the instantaneous phase. As one approach for reconstructing instantaneous phase, we used the Hilbert Transform (HT) preceded by Singular Spectrum Decomposition (SSD) of the signal. PLV estimates have broad applicability as they do not rely on stationarity, and, unlike spectral coherence, they enable more accurate estimations of oscillatory synchronization across a wide range of different synchronization regimes, and better tracking of synchronization-mediated information flow among networks.     

Optimal rejection of movement artefacts from myoelectric signals by means of a wavelet filtering procedure.

In this work the problem of rejection of motion artefacts from surface myoelectric signals, recorded during dynamic contractions, is studied. In fact, the extraction of frequency parameters and the detection of muscular activation patterns can be detrimentally affected by artefacts due to the movement of the surface electrodes, particularly stressed by the dynamic conditions of the exercise performed during measurement. In order to overcome this difficulty, four different filtering procedures have been tested and compared: a high-pass filtering procedure, a moving average procedure, a moving median procedure and a new adaptive wavelet based procedure, expressly designed for this work. Orthogonal Meyer wavelets are used with the aim of obtaining both a good reconstruction and a decomposition of the signal into non-overlapping bands. The four procedures have been tested with a set of different proofs utilising both synthetic and experimentally recorded myoelectric signals. The results show that the wavelet procedure performs better than the other methods both in information preservation and in time-detection. Moreover, the features of user-independence and adaptivity to the noise level suggest a wider range of applications of the proposed algorithm.     

Removing ECG contamination from EMG recordings: a comparison of ICA-based and other filtering procedures

Trunk muscle electromyography (EMG) is often contaminated by the electrocardiogram (ECG), which hampers data analysis and potentially yields misinterpretations. We propose the use of independent component analysis (ICA) for removing ECG contamination and compared it with other procedures previously developed to decontaminate EMG. To mimic realistic contamination while having uncontaminated reference signals, we employed EMG recordings from peripheral muscles with different activation patterns and superimposed distinct ECG signals that were recorded during rest at conventional locations for trunk muscle EMG. ICA decomposition was performed with and without a separately collected ECG signal as part of the data set and contaminated ICA modes representing ECG were identified automatically. Root mean squared relative errors and correlations between the linear envelopes of uncontaminated and contaminated EMG were calculated to assess filtering effects on EMG amplitude. Changes in spectral content were quantified via mean power frequencies. ICA-based filtering largely preserved the EMG's spectral content. Performance on amplitude measures was especially successful when a separate ECG recording was included. That is, the ICA-based filtering can produce excellent results when EMG and ECG are indeed statistically independent and when mode selection is flexibly adjusted to the data set under study.     

Acoustic detection and acoustic habitat characterisation of the critically endangered white-bellied heron (Ardea insignis) in Bhutan

1. Growing developmental activities, such as hydropower construction, farm roads, and other human activities, are affecting the critically endangered white-bellied heron (WBH). Out of a known global population of 60, 28 individuals inhabit the river basin area and freshwater lakes and ponds of Bhutan. Several constraints impede continuous monitoring of endangered species, such as the isolated and cryptic nature of the species and the remoteness of its habitat; to date, there are no long-term reference data or techniques implemented for continuous monitoring of this species.
2. In this study, we designed acoustic detection and habitat characterisation methods using long-duration recordings from three habitat areas in Bhutan. Acoustic indices were extracted and used to implement a species-specific call detector and to generate habitat soundscape representations. Using WBH calls annotated in month-long recordings from a known site, a novel indices-based detector was implemented and tested. A total of 960 hr of continuous audio recordings from three habitats in Bhutan were analysed.
3. We found that a species call detector implemented using a combination of acoustic indices (that includes measures of spectral and temporal entropy and different angles of spectral ridges) has a correct detection rate of 81%. Additionally, visual inspection of the species’ acoustic habitat using long-duration false-colour spectrograms enabled qualitative assessment of acoustic habitat structure and other dominant acoustic events.
4. This study proposes a combined approach of species acoustic detection and habitat soundscape analysis for holistic acoustic monitoring of endangered species. As a direct outcome of this work, we documented acoustic reference data on the critically endangered WBH from multiple habitat areas and have analysed its temporal vocalisation patterns across sites.

     

A compact fiber‐optic probe‐based singlet oxygen luminescence detection system

This paper presents a novel compact fiberoptic based singlet oxygen near‐infrared luminescence probe coupled to an InGaAs/InP single photon avalanche diode (SPAD) detector. Patterned time gating of the single‐photon detector is used to limit unwanted dark counts and eliminate the strong photosensitizer luminescence background. Singlet oxygen luminescence detection at 1270 nm is confirmed through spectral filtering and lifetime fitting for Rose Bengal in water, and Photofrin in methanol as model photosensitizers. The overall performance, measured by the signal‐to‐noise ratio, improves by a factor of 50 over a previous system that used a fiberoptic‐coupled superconducting nanowire single‐photon detector. The effect of adding light scattering to the photosensitizer is also examined as a first step towards applications in tissue in vivo.

     

基于Renyi熵滤波的光声图像重建算法设计与实现

针对光声图像重建过程中存在的原始光声信号信噪比差、重建图像对比度低、分辨率不足等问题,提出了基于Renyi熵的光声图像重建滤波算法.该算法首先根据原始光声信号的Renyi熵分布情况,确定分割阈值,并滤除杂波信号;再利用滤波后的光声数据进行延时叠加光声图像重建.利用该滤波算法分别处理铅笔芯横截面(零维)、头发丝(一维)以及小鼠大脑皮层血管(二维)等不同维度样本的光声信号,实验结果表明:相比Renyi熵处理之前,重建图像对比度平均增强了32.45%,分辨率平均提高了30.78%,信噪比提高了47.66%,均方误差降低了35.01%;相比典型的滤波处理算法(模极大值法和阈值去噪法),本研究中图像的对比度、分辨率和信噪比分别提高了25.94%/10.60%、27.90%/19.48%、35.21%/10.60%,均方误差减小了28.57%/16.66%.因此,选择利用Renyi熵滤波算法处理光声信号,从而使光声图像重建质量得到大幅改善.     

On the detection of signals in non-white noise

We have studied the detection, by human observers, of suprathreshold bandlimited signals embedded at various locations in non-white, Gaussian filtered noise. Detection models based upon the direct cross-correlation between the signal and the noise image (matched filtering) cannot account for the results of our experiments. Our findings point instead at a detection process occurring at the level of signal decomposition, and jointly determined by: (a) the differential outputs of discrete, bandlimited spatial analyzers selectively responsive to different components of the signal; and (b) variable detection rules adaptively related to such outputs and to the type of signal information available to the observer.     

Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching

MOTIVATION: A major problem for current peak detection algorithms is that noise in mass spectrometry (MS) spectra gives rise to a high rate of false positives. The false positive rate is especially problematic in detecting peaks with low amplitudes. Usually, various baseline correction algorithms and smoothing methods are applied before attempting peak detection. This approach is very sensitive to the amount of smoothing and aggressiveness of the baseline correction, which contribute to making peak detection results inconsistent between runs, instrumentation and analysis methods. RESULTS: Most peak detection algorithms simply identify peaks based on amplitude, ignoring the additional information present in the shape of the peaks in a spectrum. In our experience, 'true' peaks have characteristic shapes, and providing a shape-matching function that provides a 'goodness of fit' coefficient should provide a more robust peak identification method. Based on these observations, a continuous wavelet transform (CWT)-based peak detection algorithm has been devised that identifies peaks with different scales and amplitudes. By transforming the spectrum into wavelet space, the pattern-matching problem is simplified and in addition provides a powerful technique for identifying and separating the signal from the spike noise and colored noise. This transformation, with the additional information provided by the 2D CWT coefficients can greatly enhance the effective signal-to-noise ratio. Furthermore, with this technique no baseline removal or peak smoothing preprocessing steps are required before peak detection, and this improves the robustness of peak detection under a variety of conditions. The algorithm was evaluated with SELDI-TOF spectra with known polypeptide positions. Comparisons with two other popular algorithms were performed. The results show the CWT-based algorithm can identify both strong and weak peaks while keeping false positive rate low. AVAILABILITY: The algorithm is implemented in R and will be included as an open source module in the Bioconductor project.