Denoising ECG Signals Using Transform Domain Adaptive Filtering Technique

2009 ◽  
Author(s):  
Mohammad Zia Ur Rahman ◽  
Rafi Ahamed Shaik ◽  
D. V. Rama Koti Reddy
Keyword(s):  
Adaptive Filtering ◽  
Transform Domain ◽  
Ecg Signals ◽  
Filtering Technique

scholarly journals Two De-Noising Methods Based on Gabor Transform

2011 ◽  
Vol 2-3 ◽  
pp. 176-181
Author(s):  
Yong Jun Shen ◽  
Guang Ming Zhang ◽  
Shao Pu Yang ◽  
Hai Jun Xing
Keyword(s):  
Wavelet Transform ◽  
Adaptive Filtering ◽  
Time Domain ◽  
Background Noise ◽  
Averaging Method ◽  
Vibration Signal ◽  
Gabor Transform ◽  
Transform Domain ◽  
Computation Scheme ◽  
Source Signals

Two de-noising methods, named as the averaging method in Gabor transform domain (AMGTD) and the adaptive filtering method in Gabor transform domain (AFMGTD), are presented in this paper. These two methods are established based on the correlativity of the source signals and the background noise in time domain and Gabor transform domain, that is to say, the uncorrelated source signals and background noise in time domain would still be uncorrelated in Gabor transform domain. The construction and computation scheme of these two methods are investigated. The de-noising performances are illustrated by some simulation signals, and the wavelet transform is used to compare with these two new de-noising methods. The results show that these two methods have better de-noising performance than the wavelet transform, and could reduce the background noise in the vibration signal more effectively.

scholarly journals Active proportional electromyogram controlled functional electrical stimulation system

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Bethel A. C. Osuagwu ◽  
Emily Whicher ◽  
Rebecca Shirley
Keyword(s):  
Electrical Stimulation ◽  
Adaptive Filtering ◽  
Functional Electrical Stimulation ◽  
Comb Filter ◽  
Online Application ◽  
Present Technique ◽  
Filtering Technique ◽  
Stimulation System ◽  
Filter Approach ◽  
Intentional Movement

AbstractNeurophysiological theories and past studies suggest that intention driven functional electrical stimulation (FES) could be effective in motor neurorehabilitation. Proportional control of FES using voluntary EMG may be used for this purpose. Electrical artefact contamination of voluntary electromyogram (EMG) during FES application makes the technique difficult to implement. Previous attempts to date either poorly extract the voluntary EMG from the artefacts, require a special hardware or are unsuitable for online application. Here we show an implementation of an entirely software-based solution that resolves the current problems in real-time using an adaptive filtering technique with an optional comb filter to extract voluntary EMG from muscles under FES. We demonstrated that unlike the classic comb filter approach, the signal extracted with the present technique was coherent with its noise-free version. Active FES, the resulting EMG-FES system was validated in a typical use case among fifteen patients with tetraplegia. Results showed that FES intensity modulated by the Active FES system was proportional to intentional movement. The Active FES system may inspire further research in neurorehabilitation and assistive technology.

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