共查询到18条相似文献,搜索用时 140 毫秒
1.
为去除心电信号中的各种噪声,本文以小波变换的多分辨率分析为理论基础,利用自适应阈值调整小波变换系数,用调整后的系数进行心电信号重建.采用MIT-BIH数据库中的心电信号进行仿真、验证,有效地去除了噪声信号.与传统滤波器具去噪相比有明显的优越性. 相似文献
2.
采集了10名受试者在做手部握力动作时桡侧腕屈肌的肌音信号,通过对信号进行滤波、动作分割和特征提取来分析肌肉动态疲劳程度与肌音信号特征值的关系。在信号滤波中,采用了小波包(WP)分解重构和经验模态分解(EMD)两种方法。在动作信号的分割中,提出了基于移动窗内信号方差阈值的自适应不等长分割算法。在特征提取时,提出了利用包含多个动作信号的移动窗对分割好的信号进行再重构,并选用平均功率频率(MPF)和中值频率(MDF)作为窗内信号提取的特征,再分别利用指数函数、二次函数和线性函数对特征值进行拟合。结果表明:去噪方法选用小波包分解重构、特征值选用MPF值、拟合方式选用指数函数进行逼近的分析方法,可以更好地反映肌肉疲劳的变化趋势。 相似文献
3.
语音是人类重的生理信号,生物雷达能够通过检测喉部发音器官振动的微弱信息对语音进行非接触采集,从而提供一种新型的非接触语音获取方法。但生物雷达所接收的含有语音信息的回波中还存在多种噪声和杂波。本文采用离散短时傅立叶变换,在保持语音信号振幅谱不变的条件下通过改变棚位谱来对信号谱进行重构,达到去除雷达语音信号中噪声分量的日的。通过与经典的谱减法和维纳滤波语音增强算法进行对比,此算法能够在低信噪比条件下有效地降低噪声分最,可提高生物雷达语音信号质量。 相似文献
4.
《新乡医学院学报》2016,(2)
目的探讨精神分裂症患者额叶脑电信号中眼电伪迹自动去除的最佳方法。方法利用基于3种小波基(db4、sym4、bior2.4)小波包变换,分别对精神分裂症患者额叶脑电信号进行4层小波包分解,获取多尺度小波包系数,对第4层含有眼电伪迹的小波包系数进行自适应阈值处理,处理后重建脑电信号,去除眼电伪迹,并通过功率谱分析对其进行评价。结果基于3种小波基的处理算法均能很好地去除脑电信号中的眼电伪迹,其中db4小波基处理方法能更好地保留脑电信号成分。结论采用小波包变换自适应阈值方法不仅能有效去除脑电信号中低频段的眼电伪迹干扰,而且能有效保留脑电信号的有用成分,特别是高频段脑电信号。 相似文献
5.
基于小波多分辨率系数模值的并发故障检测方法 总被引:1,自引:0,他引:1
提出了利用小波分析技术进行信号奇异性检测的多重并发故障检测方法。利用小波多尺度分解技术,将信号进行多尺度的小波分解,得到不同尺度下的信号高频分量的小波系数模值,并根据奇变信号和噪声信号小波系数模值的差异,采用软阈值去噪法,对其高频分量小波系数进行去噪处理,获取不同尺度上突变信号的小波系数模值,实现对故障的检测,并可根据不同尺度上小波系数模值的对应关系,实现对多重并发故障的区分。对电网信号分析的仿真结果证实了该方法的正确性和可行性。 相似文献
6.
小波包变换已成为一种重要的信号处理方法.本文为实现小波包的快速分解和重构,采用单片机和 DSP处理器相结合的双 CPU结构,从而解决了实时监测的难点.文章还详细介绍了从心电信号中检测睡眠呼吸暂停的方法,给出了系统电路的设计原理和相应的软件流程. 相似文献
7.
EEG(脑电)信号的4个节律(δ波、θ波、α波、β波)的能量水平与人体不同精神状态直接对应。利用小波包变换对EEG信号进行分解,并计算相应的频带能量比例,可以实现对人体精神状态的量化评估。首先介绍了利用小波包分解与重构技术提取EEG信号4个节律的方法,构造了以“db20”为基小波函数的6层分解,用8个子频带对应组成EEG的4个节律,然后由相应的重构信号计算出4个归一化的频带能量比例。这些频带能量比例值可以作为对人体精神状态进行评价的量化指标。对10组典型EEG信号的分析结果表明了本方法的有效性。 相似文献
8.
9.
10.
目的 设计一种降噪算法改善低血流灌注状态下提取的光电脉搏容积波信号质量.方法 首先对原始信号进行经验模态分解(Empirical Mode Decomposition,EMD),计算各固有模态函数(Intrinsic Mode Function,IMF)的连续均方误差,判断噪声主导的IMF后对其进行小波阈值降噪,最后将... 相似文献
11.
目的:基于经验模态分解(EMD)的多尺度时域滤波特性,抑制生物雷达回波信号噪声。方法将经过预处理的雷达回波信号进行EMD分解,获取固有模态函数(IMF)分量;根据噪声与信号自相关函数的特性差异将IMF区分为有用信号占主导模态与噪声占主导模态两部分;然后用基于小波软阈值的去噪方法提取噪声占主导模态部分中的有用成分,与有用信号占主导模态部分的固有模态函数叠加,获取雷达回波信号中的生命信息。结果基于EMD的噪声抑制方法有效地去除了雷达回波信号中的高频噪声成分,提取出了有用信息。结论基于EMD的噪声抑制方法能为生命体目标的识别提供更多信息。 相似文献
12.
13.
14.
Qi Wei Oung Hariharan Muthusamy Shafriza Nisha Basah Hoileong Lee Vikneswaran Vijean 《Journal of medical systems》2018,42(2):29
Parkinson’s disease (PD) is a type of progressive neurodegenerative disorder that has affected a large part of the population till now. Several symptoms of PD include tremor, rigidity, slowness of movements and vocal impairments. In order to develop an effective diagnostic system, a number of algorithms were proposed mainly to distinguish healthy individuals from the ones with PD. However, most of the previous works were conducted based on a binary classification, with the early PD stage and the advanced ones being treated equally. Therefore, in this work, we propose a multiclass classification with three classes of PD severity level (mild, moderate, severe) and healthy control. The focus is to detect and classify PD using signals from wearable motion and audio sensors based on both empirical wavelet transform (EWT) and empirical wavelet packet transform (EWPT) respectively. The EWT/EWPT was applied to decompose both speech and motion data signals up to five levels. Next, several features are extracted after obtaining the instantaneous amplitudes and frequencies from the coefficients of the decomposed signals by applying the Hilbert transform. The performance of the algorithm was analysed using three classifiers – K-nearest neighbour (KNN), probabilistic neural network (PNN) and extreme learning machine (ELM). Experimental results demonstrated that our proposed approach had the ability to differentiate PD from non-PD subjects, including their severity level – with classification accuracies of more than 90% using EWT/EWPT-ELM based on signals from motion and audio sensors respectively. Additionally, classification accuracy of more than 95% was achieved when EWT/EWPT-ELM is applied to signals from integration of both signal’s information. 相似文献
15.
16.
Objective: To study extracellular multi-neuron activity in the nervous system based on wavelet-fractal technique. Methods: The wavelet transform was employed to decompose the original signal and obtain 4 sub-patterns. The dividing fractal dimensions of these sub-patterns were computed. A knnclassier was used to classify feature vectors. Results: Not all the elements in feature vector DimDC were very powerful for this pattern recognition problem through the empirical study of noise signals. The most effective feature vector was defined as DimDC = (d3; d4) above. Conclusion: Wavelet-fractal algorithm has high accuracy and provides a powerful tool for clinical application. 相似文献
17.
A biotelemetry system based on digital data processing and transmission is implemented. The transmitter digitizes analog physiological signal, converts into serial form, forms a digital data packet by summing data and synchronization information and transmits the packets via an FM transmitter. The receiver side amplifies and pulse-shapes the received data packet, separates the data and synchronization information, converts serial data into 8-bit parallel form and then into analog signal. A special coding method provides high noise immunity and proper synchronization between transmitter and receiver. The system shows a stable and high performance within a distance of 100 m. 相似文献
18.
小波变换和独立分量分析去除脑电信号中的噪声和干扰 总被引:8,自引:0,他引:8
目的:去除脑电信号中的噪声和心电干扰。方法:首先采用小波软门限法去除脑电中的噪声,然后使用扩展独立分量分析算法去除脑电信手中的心电干扰。该算法的优点在于不需要计算信号的高阶统计量,收敛速度快,同时适用于超高斯和亚高斯混合信号的分离。在提取独立分量之前,对观测信号进行白化处理,去除各信号之间的相关性。结果:消除了脑电信号中的噪声和心电干扰。结论:小波门限去噪结合独立分量分析可有效地去除脑电信手中的噪声和心电干扰。 相似文献