1) respiration EMG
呼吸肌电信号
1.
Research of time-frequency analysis method of respiration EMG;
呼吸肌电信号时频分析方法的研究
2) Respiration Signal
呼吸信号
1.
In this paper , it is analyzed and designed how to condition , sample and process biologic signal, such as electrocardiosignal, respiration signal.
本文对生物信号——心电和呼吸信号的调理、采集和处理进行了分析和设计。
2.
In this paper,it analyzed and designed how to condition,sample and process biologic signal,such as electrocardiosignal,respiration signal.
该文对生物信号——心电和呼吸信号的调理、采集和处理进行了分析和设计。
3) respiratory signal
呼吸信号
1.
Characteristic analysis of respiratory signals in four species of cockroaches;
四种蜚蠊的呼吸信号特征比较
2.
Method The ECG sampled at 200 Hz was decomposed into multi-scale components by DWT with db4 wavelet, then compared with respiratory signal frequency components.
目的 研究用小波变换去除心电图信号中呼吸信号的方法。
3.
But ECG signals are so sensitive to many factors such as the respiratory signal.
目前虽然有多种去除心电信号中呼吸信号的方法,但效果并不十分理想,有必要进一步寻求更可靠的解决方案。
4) EMG
肌电信号
1.
RQA-BASED ANALYSIS OF SURFACE EMG SIGNALS;
基于定量分析方法的动作表面肌电信号分析
2.
Method of mathematics treatment in the EMG analysis;
表面肌电信号分析中的数学处理方法
3.
Prosthetic Hand Control Based on EMG Signals;
基于肌电信号的仿人型假手控制
5) electromyography
[英][i,lektrəumai'ɔgrəfi] [美][ɪ,lɛktromaɪ'ɑgrəfɪ]
肌电信号
1.
Surface electromyography processing based on recurrence quantification analysis;
基于定量递归分析(RQA)方法的肌电信号处理
2.
We used an algorithm based on single value decomposition to decomposed the signal into tow time- orthogonal subspaces: one contains ECG signal, the other contains artificial noise such as baseline wander and electromyography.
使用一种基于奇异值分解 (SVD)的算法 ,将信号分解为两个时正交的子空间即一个包含了 ECG信号 ,另一个则包含了人工噪声 ,诸如基线漂移 (BW)和肌电信号 (EMG) ,这种方法利用了存在于 1 2导联心电图中的冗余。
3.
The motion-pattern recognition arithmetic based on s u pport vector machine is proposed to enhance the discrimination rate and the real-time of multi-movement pattern recognition of surface electromyography.
为了提高肌电信号多运动模式识别的准确性和实时性,提出了一种基于支持向量机的动作模式分类算法。
6) surface EMG
肌电信号
1.
Application of improved BP algorithm to surface EMG signal classification;
改进的BP算法在表面肌电信号识别中的应用
2.
Surface EMG Signal Classification Using Wavelet Transform;
小波变换在表面肌电信号分类中的应用
3.
Four types of movement of forearm,hand grasp,hand extension,wrist pronation and wrist supination can be identified from surface EMG s.
针对肌电信号的非平稳特性 ,采用短时傅里叶变换方法对表面肌电信号进行分析 ,并通过奇异值分解有效地提取特征矢量进行模式识别 ,能够成功地从掌长肌和肱桡肌采集的两道表面肌电信号中识别展拳、握拳、腕内旋、腕外旋四种运动模式。
补充资料：呼吸肌
呼吸肌
见"呼吸运动"。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条