Storage analysis and compression of signals with application in medicine

Volodymyr Ponomaryov; Leonardo Badillo; Cristina Juarez; Jose L. Sanchez; Luis Igartua

doi:10.1117/12.476303

Storage analysis and compression of signals with application in medicine

Volodymyr Ponomaryov, Leonardo Badillo, Cristina Juarez, Jose L. Sanchez, Luis Igartua

Escuela Superior de Ingeniería Mecánica y Eléctrica (ESIME), Unidad Culhuacán

Research output: Contribution to journal › Conference article › peer-review

1 Scopus citations

Abstract

This paper presents the use of Wavelet function technique to compress and storage the electroencephalographic (EEG) signal into a multichannel EEG system. The system consists of such components: multichannel bio-amplifier, analog filters, ADC, microprocessor, DSP, PCMCIA memory, etc. The algorithms to compress EEG signal have been implemented using language C/C++. The proposed digital FIR filter to compress the signal has own coefficients chosen as the coefficients of Daubechies Wavelets. The results of the experiments with implemented procedures have shown the compression ratio and SNR values for EEG signal in the case of real time compression. Values of real time compressing and storing parameters are presented when DSP and AMD586 processor used. The Backpropagation Neural Network was used to carry out the identification of EEG Patterns in the case of epilepsy illness.

Original language	English
Pages (from-to)	429-437
Number of pages	9
Journal	Proceedings of SPIE - The International Society for Optical Engineering
Volume	5021
DOIs	https://doi.org/10.1117/12.476303
State	Published - 2003
Event	Storage and Retrieval for Media Databases 2003 - Santa Clara, CA, United States Duration: 22 Jan 2003 → 23 Jan 2003

Keywords

Artificial neutral networks
DSP
Data storage and compression
Multi-channel EEG
Wavelets

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10.1117/12.476303

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title = "Storage analysis and compression of signals with application in medicine",

abstract = "This paper presents the use of Wavelet function technique to compress and storage the electroencephalographic (EEG) signal into a multichannel EEG system. The system consists of such components: multichannel bio-amplifier, analog filters, ADC, microprocessor, DSP, PCMCIA memory, etc. The algorithms to compress EEG signal have been implemented using language C/C++. The proposed digital FIR filter to compress the signal has own coefficients chosen as the coefficients of Daubechies Wavelets. The results of the experiments with implemented procedures have shown the compression ratio and SNR values for EEG signal in the case of real time compression. Values of real time compressing and storing parameters are presented when DSP and AMD586 processor used. The Backpropagation Neural Network was used to carry out the identification of EEG Patterns in the case of epilepsy illness.",

keywords = "Artificial neutral networks, DSP, Data storage and compression, Multi-channel EEG, Wavelets",

author = "Volodymyr Ponomaryov and Leonardo Badillo and Cristina Juarez and Sanchez, {Jose L.} and Luis Igartua",

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T1 - Storage analysis and compression of signals with application in medicine

AU - Ponomaryov, Volodymyr

AU - Badillo, Leonardo

AU - Juarez, Cristina

AU - Sanchez, Jose L.

AU - Igartua, Luis

PY - 2003

Y1 - 2003

N2 - This paper presents the use of Wavelet function technique to compress and storage the electroencephalographic (EEG) signal into a multichannel EEG system. The system consists of such components: multichannel bio-amplifier, analog filters, ADC, microprocessor, DSP, PCMCIA memory, etc. The algorithms to compress EEG signal have been implemented using language C/C++. The proposed digital FIR filter to compress the signal has own coefficients chosen as the coefficients of Daubechies Wavelets. The results of the experiments with implemented procedures have shown the compression ratio and SNR values for EEG signal in the case of real time compression. Values of real time compressing and storing parameters are presented when DSP and AMD586 processor used. The Backpropagation Neural Network was used to carry out the identification of EEG Patterns in the case of epilepsy illness.

AB - This paper presents the use of Wavelet function technique to compress and storage the electroencephalographic (EEG) signal into a multichannel EEG system. The system consists of such components: multichannel bio-amplifier, analog filters, ADC, microprocessor, DSP, PCMCIA memory, etc. The algorithms to compress EEG signal have been implemented using language C/C++. The proposed digital FIR filter to compress the signal has own coefficients chosen as the coefficients of Daubechies Wavelets. The results of the experiments with implemented procedures have shown the compression ratio and SNR values for EEG signal in the case of real time compression. Values of real time compressing and storing parameters are presented when DSP and AMD586 processor used. The Backpropagation Neural Network was used to carry out the identification of EEG Patterns in the case of epilepsy illness.

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KW - Wavelets

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Y2 - 22 January 2003 through 23 January 2003

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Storage analysis and compression of signals with application in medicine

Abstract

Keywords

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