高维蛋白质波谱癌症数据特征提取

高维蛋白质波谱癌症数据分析,一直面临着高维数据的困扰。针对高维蛋白质波谱癌症数据在降维过程中的问题,提出基于小波分析技术和主成分分析技术的高维蛋白质波谱癌症数据特征提取的方法,并在特征提取之后,使用支持向量机进行分类。对8-7-02数据集进行2层小波分解时,分别使用db1、db3、db4、db6、db8、db10、haar小波基,并使用支持向量机进行分类,正确率分别达到98.18%、98.35%、98.04%、98.36%、97.89%、97.96%、98.20%。在进一步提高分类识别正确率的同时,提高了时间率。     

基于小波高频系数基因芯片数据的特征提取

结合小波分析理论与支持向量机理论,构造分类器模型,将前列腺癌基因芯片数据分成癌症和正常两种。本文着重研究小波高频系数基因芯片数据的特征提取,并通过实验对比小波高频系数和低频系数特征提取对分类器性能的影响。其中haar小波3层分解提取高频系数,送入分类器分类后,得到的正确分类率为93.31%。db1小波4层分解提取低频系数,送入分类器分类后,得到的正确分类率为93.53%。小波低频系数特征提取分类效果总体上好于高频系数,分类器性能稳定。     

基于小波低频系数基因芯片数据的特征提取

特征提取和分类是模式识别中的关键问题。结合小波分析理论和支持向量机理论,构造分类器模型,将前列腺癌基因芯片数据分成癌症和正常两种。提取小波低频系数表征原始数据并送入支持向量机分类器分类,实验证明:提取db1小波4层分解下的低频系数,送入分类器分类后正确分类率达到93.53%。Haar小波的正确率是92.94%。可见提取不同小波低频系数,得到的分类效果相差不大。     

基于多特征融合的多分类运动想象脑电信号识别研究

针对目前多分类运动想象脑电识别存在特征提取单一、分类准确率低等问题,提出一种多特征融合的四分类运动想象脑电识别方法来提高识别率。对预处理后的脑电信号分别使用希尔伯特-黄变换、一对多共空间模式、近似熵、模糊熵、样本熵提取结合时频—空域—非线性动力学的初始特征向量,用主成分分析降维,最后使用粒子群优化支持向量机分类。该算法通过对国际标准数据集BCI2005 Data set IIIa中的k3b受试者数据经MATLAB仿真处理后获得93.30%的识别率,均高于单一特征和其它组合特征下的识别率。分别对四名实验者实验采集运动想象脑电数据,使用本研究提出的方法处理获得了72.96%的平均识别率。结果表明多特征融合的特征提取方法能更好的表征运动想象脑电信号,使用粒子群支持向量机可取得较高的识别准确率,为人脑的认知活动提供了一种新的识别方法。     

基因表达数据小波降噪与肿瘤识别

建立了基于小波降噪和支持向量机的结肠癌基因表达数据肿瘤识别模型.对试验数据进行小波分解,并利用交叉验证的方法计算试验样本的平均分类准确率,确定小波函数与小波分解层数;引入能量阈值方法对小波分解系数进行阈值处理,达到降噪的目的;提出了基因分类贡献率与主成分分析结合的方法,提取结肠癌样本数据特征;利用支持向量机强大的非线性映射能力,实现对结肠癌样本数据的非线性分类.为了减弱样本集的划分对分类准确率的影响,本文采取Jackknife检验方法对支持向量分类器的分类器检验,其分类准确率为96.77%.试验结果证明了该方法的有效性,该方法对结肠癌的识别具有一定的参考价值.     

主元余像集主成分分析在蛋白质质谱数据中的应用

癌蛋白质谱数据中包含了大量未知的内部结构和变量。针对癌蛋白质谱数据这些特点,在总结主元余像集主成分分析（二次主成分分析）应用的基础上,提出了用t-验证方法进行特征子集选取,然后用主元余像集主成分分析提取特征,以线性判别分析进行分类的新方法。通过对典型癌蛋白质谱数据的分类实验,证明该方法不但识别率高,而且需要选取的特征子集小,分类速度快,提高了方法的准确性与分类速度。     

基于小波变换的混合二维ECG数据压缩方法

提出了一种新的基于小波变换的混合二维心电(electrocardiogram,ECG)数据压缩方法。基于ECG数据的两种相关性,该方法首先将一维ECG信号转化为二维信号序列。然后对二维序列进行了小波变换,并利用改进的编码方法对变换后的系数进行了压缩编码:即先根据不同系数子带的各自特点和系数子带之间的相似性,改进了等级树集合分裂(setpartitioninghierarchicaltrees,SPIHT)算法和矢量量化(vectorquantization,VQ)算法;再利用改进后的SPIHT与VQ相混合的算法对小波变换后的系数进行了编码。利用所提算法与已有具有代表性的基于小波变换的压缩算法和其他二维ECG信号的压缩算法,对MIT/BIH数据库中的心律不齐数据进行了对比压缩实验。结果表明:所提算法适用于各种波形特征的ECG信号,并且在保证压缩质量的前提下,可以获得较大的压缩比。     

一种滤除医学影像噪声的混合滤波算法

目的:医学影像在获取、存储、传输过程中会不同程度地受到噪声污染,这极大影像了其在临床诊疗中的应用。为了有效地滤除医学影像噪声,提出了一种混合滤波算法。方法:该算法首先将含有高斯和椒盐噪声的图像进行形态学开运算,然后对开运算后的图像进行二维小波分解,得到高频和低频小波分解系数。保留低频系数不变,将高频系数经过维纳滤波器进行滤波,最后进行小波系数重构。结果:采用该混合滤波算法、小波阈值去噪、中值滤波、维纳滤波分别对含有混合噪声的医学影像分别进行滤除噪声处理,该滤波算法去噪后影像的PSNR值明显高于其他三种方法。结论:该混合滤波算法是一种较为有效的医学影像噪声滤除方法。     

一种滤除医学影像噪声的混合滤波算法

目的：医学影像在获取、存储、传输过程中会不同程度地受到噪声污染,这极大影像了其在临床诊疗中的应用。为了有效地滤除医学影像噪声,提出了一种混合滤波算法。方法：该算法首先将含有高斯和椒盐噪声的图像进行形态学开运算,然后对开运算后的图像进行二维小波分解,得到高频和低频小波分解系数。保留低频系数不变,将高频系数经过维纳滤波器进行滤波,最后进行小波系数重构。结果：采用该混合滤波算法、小波阚值去噪、中值滤波、维纳滤波分别对含有混合噪声的医学影像分别进行滤除噪声处理,该滤波算法去噪后影像的PSNR值明显高于其他三种方法。结论：该混合滤波算法是一种较为有效的医学影像噪声滤除方法。     

基于深度特征融合的鸟鸣识别方法及其可解释性分析

鸟鸣识别是生态监测的重要手段,为进一步提升鸟鸣识别的准确性和鲁棒性,本文提出了1种新的基于深度特征融合的鸟鸣识别方法。该方法首先利用深度特征提取网络对鸟鸣的对数梅尔谱图和补充特征集的深度特征进行提取,再将两种深度特征进行融合,最后使用轻量级梯度提升机(light gradient boosting machine,light GBM)分类器进行分类。本文充分利用深度神经网络的特征提取能力以及light GBM的分类性能,将特征提取和特征分类过程进行分离,从而实现了高准确率的鸟鸣识别。实验结果显示,本文提出的方法在北京百鸟数据集中取得了目前已知的最佳结果,模型的平均准确率达到了98.70%,平均F₁分数达到了98.84%。相比传统方法,深度融合特征在鸟鸣识别任务上准确率提升了5.62%以上。同时,引入的light GBM分类器使分类准确率提升了3.02%。此外,在CLO-43SD和Bird CLEF2022比赛的数据集中,本文方法也展现出卓越的性能,分别取得了98.32%和91.12%的平均准确率。本文还引入了类激活图对不同类型鸟鸣的识别结果进行可解释性分析,揭示了...     

Feature selection based on a fuzzy complementary criterion: application to gait recognition using ground reaction forces

An efficient wavelet-based feature selection (FS) method is proposed in this paper for subject recognition using ground reaction force measurements. Our approach relies on a local fuzzy evaluation measure with respect to patterns that reveal the adequacy of data coverage for each feature. Furthermore, FS is driven by a fuzzy complementary criterion (FuzCoC) which assures that those features are iteratively introduced, providing the maximum additional contribution with regard to the information content given by the previously selected features. On the basis of the principles of FuzCoC, we develop two novel techniques. At Stage 1, wavelet packet (WP) decomposition of gaits is accomplished to obtain a set of discriminating frequency sub-bands. A computationally simple FS method is then applied at Stage 2, providing a compact set of powerful and complementary features, from WP coefficients. The quality of our approach is validated via comparative analysis against existing methods on gait recognition.     

Unsupervised feature selection by kernel density estimation in wavelet-based spike sorting

Wavelet transform has been widely applied in extracting characteristic information in spike sorting. As the wavelet coefficients used to distinguish various spike shapes are often disorganized, they still lack in effective unsupervised methods still lacks to select the most discriminative features. In this paper, we propose an unsupervised feature selection method, employing kernel density estimation to select those wavelet coefficients with bimodal or multimodal distributions. This method is tested on a simulated spike data set, and the average misclassification rate after fuzzy C-means clustering has been greatly reduced, which proves this kernel density estimation-based feature selection approach is effective.     

飞行状态下四种菊头蝠回声定位声波的小波包识别方法

研究了飞行状态下的四种菊头蝠回声定位声波的识别方法.通过小波包分解得到各个频带能量作为识一别特征向量,用主成分分析法优化特征空间.提取少数几个主成分,这些主成分彼此不相关,符合特征优化的要求,以主成分向量作为BP神经网络的输入对蝙蝠的种类进行识别.个体识别正确率达到了80%以上,表明基于小渡包分解和神经网络识别的方法对蝙蝠回声定位声波进行识别是可行的.     

Data reduction using a discrete wavelet transform in discriminant analysis of very high dimensionality data

We present a method of data reduction using a wavelet transform in discriminant analysis when the number of variables is much greater than the number of observations. The method is illustrated with a prostate cancer study, where the sample size is 248, and the number of variables is 48,538 (generated using the ProteinChip technology). Using a discrete wavelet transform, the 48,538 data points are represented by 1271 wavelet coefficients. Information criteria identified 11 of the 1271 wavelet coefficients with the highest discriminatory power. The linear classifier with the 11 wavelet coefficients detected prostate cancer in a separate test set with a sensitivity of 97% and specificity of 100%.     

Feature extraction and recognition of epileptiform activity in EEG by combining PCA with ApEn

This paper proposes a new method for feature extraction and recognition of epileptiform activity in EEG signals. The method improves feature extraction speed of epileptiform activity without reducing recognition rate. Firstly, Principal component analysis (PCA) is applied to the original EEG for dimension reduction and to the decorrelation of epileptic EEG and normal EEG. Then discrete wavelet transform (DWT) combined with approximate entropy (ApEn) is performed on epileptic EEG and normal EEG, respectively. At last, Neyman–Pearson criteria are applied to classify epileptic EEG and normal ones. The main procedure is that the principle component of EEG after PCA is decomposed into several sub-band signals using DWT, and ApEn algorithm is applied to the sub-band signals at different wavelet scales. Distinct difference is found between the ApEn values of epileptic and normal EEG. The method allows recognition of epileptiform activities and discriminates them from the normal EEG. The algorithm performs well at epileptiform activity recognition in the clinic EEG data and offers a flexible tool that is intended to be generalized to the simultaneous recognition of many waveforms in EEG.     

基于AR模型的基因芯片数据识别

将自回归模型(AR)模型引入基因芯片数据识别领域,提出了基于自回归模型的时间序列特征提取方法．利用动态时轴弯曲(DTW)作为分类器,在标准的肿瘤基因芯片数据的识别结果表明,本方法能够达到100％的识别率,可以应用于基因芯片数据的识别、分类和基因疾病推断。     

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.