A Multiplexing System for Multichannel Signal Averaging of the Visual Evoked Response

Ary, James P.; Fry, Terry L.; Biersdorf, William R.

doi:10.1109/tbme.1979.326460

Cited by 1 publication

(1 citation statement)

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“…In recent years, with the rapid development of digital signal processing technology, the digital medical center, the application of medical image processing and multimedia in medicine and the control of biological information and processing of the neural network biomedical signal are more and more applied in hospitals. Because of the close combination of digital signal processing technology and biomedical engineering, many new algorithms, such as wavelet transform, 7 neural network 8 and averaged evoked response, 9 have been developed for detection, extraction and clinical application of biomedical signal characteristics. As biomedical signals are characterized by strong randomness or strong noise background, it is necessary to separate the useful biomedical signal and useless noise.…”

Section: Digital Signal Processing Engineering Education With Biomedimentioning

confidence: 99%

A MATLAB-based biomedical signal de-noising applied to digital signal processing course for third-year students

Luo

2018

International Journal of Electrical Engineering & Education

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Signal de-noising is one of the major topics of engineering application covered in an undergraduate-level digital signal processing course. Generally speaking, it involves a number of tedious concepts that have intrinsic physical meaning, which is difficult for students to understand . In this paper, an educational method using diaphragmatic electromyographic (EMGdi) as the de-noising object, which runs on the MATLAB software, has been developed for the convenience of learning and understanding for three-years students in digital signal processing course. This method transforms the analog filter to a digital filter by applying bilinear transformation equations, which allows the students explore the various characteristics of digital filter, such as low pass filter, high pass filter, band pass filter and band stop filter. That means Laplace equation transformed by inductance, capacitance and resistance will be replaced by the z equation, which is used for deriving sequence of difference equations. In the case studies, the clinical EMGdi is used to show the features of the developed method. Furthermore, classroom experience in the Nanfang College of Sun Yat-sen University has shown that the developed method helps in consolidating a better understanding of signal de-noising processing in digital signal processing course.

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Section: Digital Signal Processing Engineering Education With Biomedimentioning

confidence: 99%

A MATLAB-based biomedical signal de-noising applied to digital signal processing course for third-year students

Luo

2018

International Journal of Electrical Engineering & Education

View full text Add to dashboard Cite

show abstract

A Multiplexing System for Multichannel Signal Averaging of the Visual Evoked Response

Cited by 1 publication

References 4 publications

A MATLAB-based biomedical signal de-noising applied to digital signal processing course for third-year students

A MATLAB-based biomedical signal de-noising applied to digital signal processing course for third-year students

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