中耳功能异常对畸变产物耳声发射测试的影响

目的 探讨中耳传音功能异常对畸变产物耳声发射（DPOAE）测试结果的影响程度并在临床应用中加以识别和剔除。方法 将年龄2月－11岁56例（107耳）小儿依听力损失类型分为感音性聋组 和传导性聋组,并设置正常听力对照组,应用GSI 60耳声发射仪分别观察其DPOAE的DP检出率、DP幅值、DP与NF幅值差及相同参数条件下DPOAE测试时间。结果 耳蜗功能严重受损和渗出性中耳炎所记录到的DPOAE各测量参数之间的差异无统计学意义（P＞0．05）。结论 中耳积液导致的中耳传音功能障碍对DPOAE测试结果的影响,与耳蜗功能严重损害时所记录到的DPOAE参数变化,在临床检测中难以区分。因此,临床上在应用DPOAE进行听力评估之前,首先要检测受检者的中耳功能,剔除其对DPOAE检测结果的影响,确保结果的可靠性。     

用于诱发电位提取的自适应噪声抵消算法

自适应噪声抵消是提取诱发电位的较好方法，但原算法中噪声参考输入中包含的信号分量造成了输出信号的衰减和畸变，使得诱发电位提取的效果很差，无法使用，根据诱发电位的信号的特点，作者提出了改进的自适应噪声抵消算法，达到了噪声抵消对信号和噪声统计关系的要求，仿真测试和临床实验都证明改进后的算法性能大为改善。     

A novel real-time noise reduction system for the assessment of evoked otoacoustic emissions

A novel noise reduction method and apparatus to be used in connection with the measurement of evoked otoacoustic emissions (EOAE) are presented. The noise reduction method is based on an adaptive noise canceller and requires a noise-only reference microphone placed in the vicinity of the OAE-probe. The method was implemented in real time on a custom built digital signal processing system using an Analog Devices ADSP-2181 digital signal processor. The system interfaces seamlessly with a commercial EOAE acquisition system. Results of a series of experiments show than noise reductions of 7–8 dB can be reached.     

Mammographic Image Denoising and Enhancement Using the Anscombe Transformation, Adaptive Wiener Filtering, and the Modulation Transfer Function

A new restoration methodology is proposed to enhance mammographic images through the improvement of contrast features and the simultaneous suppression of noise. Denoising is performed in the first step using the Anscombe transformation to convert the signal-dependent quantum noise into an approximately signal-independent Gaussian additive noise. In the Anscombe domain, noise is filtered through an adaptive Wiener filter, whose parameters are obtained by considering local image statistics. In the second step, a filter based on the modulation transfer function of the imaging system in the whole radiation field is applied for image enhancement. This methodology can be used as a preprocessing module for computer-aided detection (CAD) systems to improve the performance of breast cancer screening. A preliminary assessment of the restoration algorithm was performed using synthetic images with different levels of quantum noise. Afterward, we evaluated the effect of the preprocessing on the performance of a previously developed CAD system for clustered microcalcification detection in mammographic images. The results from the synthetic images showed an increase of up to 11.5 dB (p = 0.002) in the peak signal-to-noise ratio. Moreover, the mean structural similarity index increased up to 8.3 % (p < 0.001). Regarding CAD performance, the results suggested that the preprocessing increased the detectability of microcalcifications in mammographic images without increasing the false-positive rates. Receiver operating characteristic analysis revealed an average increase of 14.1 % (p = 0.01) in overall CAD performance when restored image sets were used.     

基于ANFIS的自适应噪声消除方法在视觉诱发脑电信号的单次提取中的应用

本研究将基于自适应模糊神经网络(ANFIS)的噪声消除方法应用于视觉诱发脑电信号的单次提取。通过数字仿真和实际临床应用的结果验证了该方法的有效性。经与目前临床通用的相干平均法比较，在波形整体和P100潜伏期的提取上，效果显著。     

一种恢复高刺激率下听觉诱发电位的迭代维纳滤波方法

研究分离高刺激率诱发的听觉诱发电位(AEP)暂态成分的方法。根据前期提出的维纳滤波技术,结合高阶反应的数学模型,提出一种长期记忆的迭代计算方案,在无需信号先验知识的条件下逼近一个稳定的AEP估计。将该方法应用于仿真数据和真实数据进行检验,其效果接近理想条件下维纳滤波的理论解。结果表明该方法可在缺乏信号先验知识的条件下,得到一个接近于理论真实值的AEP估计,为在高刺激率下还原AEP提供了一定的启示。     

基于RLS-ANC自适应滤波的FECG信号提取方法

本文应用RLS-ANC(recursive least squares adaptive noise canceⅡation)自适应滤波方法提取胎儿心电(FECG)信号.该方法采用RLS-ANC自适应滤波消除母亲心电,提取胎儿心电信号.实验结果表明,本方法适应非平稳信号的能力强,收敛速度快,提取效果好于NLMS(normalized least mean squares)算法.     

Adaptive estimation of EEG for subject-specific reactive band identification and improved ERD detection

The event-related desynchronization (ERD) is a magnitude decrease phenomenon which can be found in electroencephalogram (EEG) mu-rhythm in a certain narrow frequency band (reactive band) during different sensorimotor tasks and stimuli. The success of ERD detection depends on proper identification of subject specific reactive band. An adaptive algorithm band limited multiple Fourier linear combiner (BMFLC) is employed in this paper for identification of subject specific reactive band for real-time ERD detection. With the time–frequency mapping obtained with BMFLC, a procedure is formulated for reactive band identification. Improved classification is obtained by applying this method to a standard BCI data set compared to traditional ERD detection methods. Study conducted with 8 subjects drawn from BCI Competition IV data set show a 22% increase in ERD and 10% improvement in classification with the proposed method compared to standard ERD based classification.     

Development of Gradient Descent Adaptive Algorithms to Remove Common Mode Artifact for Improvement of Cardiovascular Signal Quality

Background: Common-mode noise degrades cardiovascular signal quality and diminishes measurement accuracy. Filtering to remove noise components in the frequency domain often distorts the signal. Method: Two adaptive noise canceling (ANC) algorithms were tested to adjust weighted reference signals for optimal subtraction from a primary signal. Update of weight w was based upon the gradient term of the steepest descent equation: , where the error ɛ is the difference between primary and weighted reference signals. ∇ was estimated from Δɛ² and Δw without using a variable Δw in the denominator which can cause instability. The Parallel Comparison (PC) algorithm computed Δɛ² using fixed finite differences ± Δw in parallel during each discrete time k. The ALOPEX algorithm computed Δɛ²· Δw from time k to k + 1 to estimate ∇, with a random number added to account for Δɛ² · Δw→ 0 near the optimal weighting. Results: Using simulated data, both algorithms stably converged to the optimal weighting within 50–2000 discrete sample points k even with a SNR = 1:8 and weights which were initialized far from the optimal. Using a sharply pulsatile cardiac electrogram signal with added noise so that the SNR = 1:5, both algorithms exhibited stable convergence within 100 ms (100 sample points). Fourier spectral analysis revealed minimal distortion when comparing the signal without added noise to the ANC restored signal. Conclusions: ANC algorithms based upon difference calculations can rapidly and stably converge to the optimal weighting in simulated and real cardiovascular data. Signal quality is restored with minimal distortion, increasing the accuracy of biophysical measurement.