The message of the genes [medical signal processing]

2007 ◽  
Vol 2 (5) ◽  
pp. 28-32
Author(s):  
C. Evans-Pughe
Keyword(s):  
Signal Processing ◽  
Medical Signal ◽  
Medical Signal Processing

Compressive sensing in medical signal processing and imaging systems

2019 ◽  
pp. 69-92 ◽  
Author(s):  
Thales Wulfert Cabral ◽  
Mahdi Khosravy ◽  
Felipe Meneguitti Dias ◽  
Henrique Luis Moreira Monteiro ◽  
Marcelo Antônio Alves Lima ◽  
...  
Keyword(s):  
Signal Processing ◽  
Compressive Sensing ◽  
Imaging Systems ◽  
Medical Signal ◽  
Medical Signal Processing

scholarly journals Detection and Separation of Eeg Artifacts Using Wavelet Transform

2018 ◽  
Vol 7 (3) ◽  
pp. 149
Author(s):  
R. Suresh Kumar ◽  
P. Manimegalai
Keyword(s):  
Signal Processing ◽  
Wavelet Transform ◽  
Output Signal ◽  
Computation Time ◽  
Basic Structure ◽  
Wide Sense ◽  
Linear Estimation ◽  
Stationary Signals ◽  
Medical Signal ◽  
Medical Signal Processing

Bio-medical signal processing is one of the most important techniques of multichannel sensor network and it has a substantial concentration in medical application. However, the real-time and recorded signals in multisensory instruments contains different and huge amount of noise, and great work has been completed in developing most favorable structures for estimating the signal source from the noisy signal in multichannel observations. Methods have been developed to obtain the optimal linear estimation of the output signal through the Wide-Sense-Stationary (WSS) process with the help of time-invariant filters. In this process, the input signal and the noise signal are assumed to achieve the linear output signal. During the process, the non-stationary signals arise in the bio-medical signal processing in addition to it there is no effective structure to deal with them. Wavelets transform has been proved to be the efficient tool for handling the non-stationary signals, but wavelet provide any possible way to approach multichannel signal processing. Based on the basic structure of linear estimation of non-stationary multichannel data and statistical models of spatial signal coherence acquire through the wavelet transform in multichannel estimation. The above methods can be used for Electroencephalography (EEG) signal denoising through the original signal and then implement the noise reduction technique to evaluate their performance such as SNR, MSE and computation time.

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