广义时频分析方法在耳声发射研究中的应用 Ⅱ.短声与短纯音诱发耳声发射信号的时频分布

为了进一步探讨耳声发射的产生机制,需要研究不同类型刺激诱发的耳声发射之间的相互关系,主要研究短声与短纯音诱发的耳声发射,用广义时频分析方法中的锥形核分布分别计算了它们的时频分布,从其时频分布分析了它们之间的相互关系。结果表明：具有不同中心频率的短纯音刺激诱发耳声发射的时频分布的迭加与短声刺激诱发的耳声发射的时频分布具有相似性,两者时频分布中的主要频率成分数目、潜伏期和持续时间完全相同,它们高度的相     

广义时频分析方法在耳声发射研究中的应用 Ⅰ.仿真瞬态诱发耳声发射信号的时频分布

为了研究瞬态诱发耳声发射的时频分布, 寻找其时频分布的最佳计算方法, 首先系统地介绍了广义时频分析方法,并描述了其中二次时频表示方法的特点,然后用Ｗｉｇｎｅｒ 分布及其改进型分布计算了仿真的瞬态诱发耳声发射信号的时频分布,通过对不同分布计算结果的比较,得出了锥形核分布最适合用于描述瞬态诱发耳声发射的时频分布。     

广义时频分析方法在耳声发射研究中的应用Ⅱ.短声与短纯音诱发耳声发射信号的时频分布

为了进一步探讨耳声发射的产生机制,需要研究不同类型刺激诱发的耳声发射之间的相互关系.主要研究短声与短纯音诱发的耳声发射,用广义时频分析方法中的锥形核分布分别计算了它们的时频分布,从其时频分布分析了它们之间的相互关系.结果表明:具有不同中心频率的短纯音刺激诱发耳声发射的时频分布的迭加与短声刺激诱发的耳声发射的时频分布具有相似性,两者时频分布中的主要频率成分数目、潜伏期和持续时间完全相同,它们高度的相关性支持了短声与短纯音诱发的耳声发射具有共同的产生器的观点.     

广义时频分析方法在耳声发射研究中的应用 Ⅰ.仿真瞬态诱发耳声发射信号的时频分布

为了研究瞬态诱发耳声发射的时频分布,寻找其时频分布的最佳计算方法,首先筚计介绍了广时频分析方法,并描述了其中二次时频表示方法的特特点,然后用Ｗｉｇｎｅｒ分布及其改进型分布计算了仿真的瞬态诱发耳声发射信号的时频分布,通过对不同分布计算结果的比较,得出了锥形核分布最适合用于描述瞬态诱发耳声发射的时频分布。     

小波神经网络在脑电信号数据压缩与棘波识别中的应用

介绍了一种新的神经网络模型———小波神经网络,利用它并适当调节网络、小波基参数,实现了对脑电信号的压缩表达,较好的恢复了原有信号。另外,在其算法研究的基础上,提出了适应于非稳态和非线性信号处理的时频分析新方法。在脑电信号的时频谱等高线图上,得到了易于自动识别的棘波和棘慢复合波特征,与传统的短时傅立叶变换（ＳＴＦＴ）和Ｗｉｇｎｅｒ分布相比,此方法有更高的分辨率和自适应性,而且其时频能量分布没有交叉项干扰。     

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。     

心音、心杂音时域、频域分析若干方法学问题初探

心音的时间序列分析和功率谱估计已有文献报道(1,2),但人们对心杂音的时域、频域特征则很少了解。关于心音的阻尼系数、残差等其它时域参数以及能量在频域内的分布情况亦未见报道。心音、心杂音是心脏、大血管振动时所产生。研究心音、心杂音,实际上是研究心脏和大血管的振动过程,其理论价值远远超出入耳心脏听诊和普通心音图记录对心音、心杂音的解释。但由于存在一些方法上的问题,分析结果不太令人满意,因而未被广泛应用于临床。本文从临床需要出发,根据心音、心杂音的形成机制,对其在时域、频域分析中的若干方法学问题和可     

短纯音诱发耳声发射的指数方法时频分析

目的在于使用指数分布方法计算来自于正常人耳短纯音诱发耳声发射（Ｔｏｎｅ－ｂｕｒｓｔＥｖｏｋｅｄＯｔｏａｃｏｕｓｔｉｃＥｍｉｓｓｉｏｎｓ,ＴＢＯＡＥｓ）的时频分布。对耳声发射的定量分析依赖于谱方法,而ＴＢＯＡＥｓ是非平稳信号,因此传统的谱分析方法已不能满足要求,指数分布能很好地给出ＴＢＯＡＥｓ的时频表示。我们根据对仿真信号及实测ＴＢＯＡＥｓ的计算结果,分析了其时频分布的特点,并对不同的频率成份与潜伏期的关系进行了描述。     

基于时频分析检测EEG中癫痫样棘/尖波的方法

提出了一种基于Ｃｈｏｉ－Ｗｉｌｌｉａｍｓ分布检测ＥＥＧ中癫痫样棘波／尖波的方法。该方法通过计算ＥＥＧ信号的时频分布,得到一段信号在各个时刻上沿频率方向上的能量分布。这种能量分布相当于一种瞬时频谱,反映了ＥＥＧ信号在局部时间范围里的波形特征。以一段ＥＥＧ信号在各个时刻的瞬时频谱的平均作为这段脑电的背景信号频谱,通过计算每一时刻的瞬时频谱与背景信号频谱之间的频谱差,检测这段信号中的棘波／尖波。对临床Ｅ     

躯体和内脏传入冲动在大鼠束旁核内的会聚

在麻痹的大鼠上,分别刺激迷走神经、内脏大神经、坐骨神经、腓肠神经、睾丸和副睾,在对侧丘脑束旁核记录到了45个细胞的单位放电。根据诱发反应的潜伏期、时程和放电频谱分布的不同,可将他们分为五种类型,并且认为这些类型和刺激引起的感觉性质有关。在观察到的45单位中,29个的反应具有痛放电的特性,而且对躯体及内脏的传入冲动呈聚合性反应。其中2个单位只对内脏传入冲动产生反应。这项研究的结果表明,束旁核不仅是接受内脏传入的丘脑结构,而且也是一个整合内脏与躯体传入信息的中枢。     

Heart energy signature spectrogram for cardiovascular diagnosis

A new method and application is proposed to characterize intensity and pitch of human heart sounds and murmurs. Using recorded heart sounds from the library of one of the authors, a visual map of heart sound energy was established. Both normal and abnormal heart sound recordings were studied. Representation is based on Wigner-Ville joint time-frequency transformations. The proposed methodology separates acoustic contributions of cardiac events simultaneously in pitch, time and energy. The resolution accuracy is superior to any other existing spectrogram method. The characteristic energy signature of the innocent heart murmur in a child with the S3 sound is presented. It allows clear detection of S1, S2 and S3 sounds, S2 split, systolic murmur, and intensity of these components. The original signal, heart sound power change with time, time-averaged frequency, energy density spectra and instantaneous variations of power and frequency/pitch with time, are presented. These data allow full quantitative characterization of heart sounds and murmurs. High accuracy in both time and pitch resolution is demonstrated. Resulting visual images have self-referencing quality, whereby individual features and their changes become immediately obvious.     

From wavelets to adaptive approximations: time-frequency parametrization of EEG

This paper presents a summary of time-frequency analysis of the electrical activity of the brain (EEG). It covers in details two major steps: introduction of wavelets and adaptive approximations. Presented studies include time-frequency solutions to several standard research and clinical problems, encountered in analysis of evoked potentials, sleep EEG, epileptic activities, ERD/ERS and pharmaco-EEG. Based upon these results we conclude that the matching pursuit algorithm provides a unified parametrization of EEG, applicable in a variety of experimental and clinical setups. This conclusion is followed by a brief discussion of the current state of the mathematical and algorithmical aspects of adaptive time-frequency approximations of signals.     

Intensity analysis in time-frequency space of surface myoelectric signals by wavelets of specified resolution

Surface myoelectric signals often appear to carry more information than what is resolved in root mean square analysis of the progress curves or in its power spectrum. Time-frequency analysis of myoelectric signals has not yet led to satisfactory results in respect of separating simultaneous events in time and frequency. In this study a time-frequency analysis of the intensities in time series was developed. This intensity analysis uses a filter bank of non-linearly scaled wavelets with specified time-resolution to extract time-frequency aspects of the signal. Special procedures were developed to calculate intensity in such a way as to approximate the power of the signal in time. Applied to an EMG signal the intensity analysis was called a functional EMG analysis. The method resolves events within the EMG signal. The time when the events occur and their intensity and frequency distribution are well resolved in the intensity patterns extracted from the EMG signal. Averaging intensity patterns from multiple experiments resolve repeatable functional aspects of muscle activation. Various properties of the functional EMG analysis were shown and discussed using model EMG data and real EMG data.     

Sound representation methods for spectro-temporal receptive field estimation

The spectro-temporal receptive field (STRF) of an auditory neuron describes the linear relationship between the sound stimulus in a time-frequency representation and the neural response. Time-frequency representations of a sound in turn require a nonlinear operation on the sound pressure waveform and many different forms for this non-linear transformation are possible. Here, we systematically investigated the effects of four factors in the non-linear step in the STRF model: the choice of logarithmic or linear filter frequency spacing, the time-frequency scale, stimulus amplitude compression and adaptive gain control. We quantified the goodness of fit of these different STRF models on data obtained from auditory neurons in the songbird midbrain and forebrain. We found that adaptive gain control and the correct stimulus amplitude compression scheme are paramount to correctly modelling neurons. The time-frequency scale and frequency spacing also affected the goodness of fit of the model but to a lesser extent and the optimal values were stimulus dependant. Action Editor: Israel Nelken     

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.     

Time-frequency analysis of phonocardiogram signals using wavelet transform: a comparative study

Analysis of phonocardiogram (PCG) signals provides a non-invasive means to determine the abnormalities caused by cardiovascular system pathology. In general, time-frequency representation (TFR) methods are used to study the PCG signal because it is one of the non-stationary bio-signals. The continuous wavelet transform (CWT) is especially suitable for the analysis of non-stationary signals and to obtain the TFR, due to its high resolution, both in time and in frequency and has recently become a favourite tool. It decomposes a signal in terms of elementary contributions called wavelets, which are shifted and dilated copies of a fixed mother wavelet function, and yields a joint TFR. Although the basic characteristics of the wavelets are similar, each type of the wavelets produces a different TFR. In this study, eight real types of the most known wavelets are examined on typical PCG signals indicating heart abnormalities in order to determine the best wavelet to obtain a reliable TFR. For this purpose, the wavelet energy and frequency spectrum estimations based on the CWT and the spectra of the chosen wavelets were compared with the energy distribution and the autoregressive frequency spectra in order to determine the most suitable wavelet. The results show that Morlet wavelet is the most reliable wavelet for the time-frequency analysis of PCG signals.     

Pseudo-sound generation at atherosclerotic constrictions in arteries

Sounds and murmurs have long been employed to qualitatively diagnose cardiovascular disease. However, quantitative diagnosis has been hindered by the lack of understanding of the sound generation and transmission mechanisms. Clinical phonoangiographic studies have shown that simple assumptions about low frequency sound transmission through tissue surrounding an artery are inadequate for obtaining meaningful quantitative diagnosis. Therefore, a theory is developed which relates internal turbulent flow in constricted peripheral arteries to the sound observed at the surface of the skin by means of assumptions of similarity and local axial homogeneity of the internal turbulence. It is found that the spectrum of pressure at the wall of the artery is related to the spectrum of the pressure at the surface of the skin by a filtering factor approximately proportional to ω^-2. This arises not because of frequency dependent volumetric absorption in the surrounding medium, as with ultrasound, but because of the manner in which stochastic signals add when observed.     

The Application of Auto-Disturbance Rejection Control Optimized by Least Squares Support Vector Machines Method and Time-Frequency Representation in Voltage Source Converter-High Voltage Direct Current System

In order to improve the performance of voltage source converter-high voltage direct current (VSC-HVDC) system, we propose an improved auto-disturbance rejection control (ADRC) method based on least squares support vector machines (LSSVM) in the rectifier side. Firstly, we deduce the high frequency transient mathematical model of VSC-HVDC system. Then we investigate the ADRC and LSSVM principles. We ignore the tracking differentiator in the ADRC controller aiming to improve the system dynamic response speed. On this basis, we derive the mathematical model of ADRC controller optimized by LSSVM for direct current voltage loop. Finally we carry out simulations to verify the feasibility and effectiveness of our proposed control method. In addition, we employ the time-frequency representation methods, i.e., Wigner-Ville distribution (WVD) and adaptive optimal kernel (AOK) time-frequency representation, to demonstrate our proposed method performs better than the traditional method from the perspective of energy distribution in time and frequency plane.