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Quality-on-Demand Compression of EEG Signals for Telemedicine Applications Using Neural Network Predictors.

Sriraam N - Int J Telemed Appl (2011)

Bottom Line: The objective is to obtain a greater compression gains at a low bit rate while preserving the clinical information content.Three neural network models, single-layer and multi-layer perceptrons and Elman network are used and the results are compared with linear predictors such as FIR filters and AR modeling.It is found from the results that the quality of the reconstructed signal is preserved at a low PRD thereby yielding better compression results compared to results obtained using lossless scheme.

View Article: PubMed Central - PubMed

Affiliation: Center for Biomedical Informatics and Signal Processing, Department of Biomedical Engineering, SSN College of Engineering, Chennai 603110, India.

ABSTRACT
A telemedicine system using communication and information technology to deliver medical signals such as ECG, EEG for long distance medical services has become reality. In either the urgent treatment or ordinary healthcare, it is necessary to compress these signals for the efficient use of bandwidth. This paper discusses a quality on demand compression of EEG signals using neural network predictors for telemedicine applications. The objective is to obtain a greater compression gains at a low bit rate while preserving the clinical information content. A two-stage compression scheme with a predictor and an entropy encoder is used. The residue signals obtained after prediction is first thresholded using various levels of thresholds and are further quantized and then encoded using an arithmetic encoder. Three neural network models, single-layer and multi-layer perceptrons and Elman network are used and the results are compared with linear predictors such as FIR filters and AR modeling. The fidelity of the reconstructed EEG signal is assessed quantitatively using parameters such as PRD, SNR, cross correlation and power spectral density. It is found from the results that the quality of the reconstructed signal is preserved at a low PRD thereby yielding better compression results compared to results obtained using lossless scheme.

No MeSH data available.


BPS versus PRD and BPS versus SNR characteristics using DS2.
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Related In: Results  -  Collection


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fig7: BPS versus PRD and BPS versus SNR characteristics using DS2.

Mentions: Figures 7 and 8 show the variations of PRD and SNR with BPS for DS2 and DS3, respectively, using the SLP.


Quality-on-Demand Compression of EEG Signals for Telemedicine Applications Using Neural Network Predictors.

Sriraam N - Int J Telemed Appl (2011)

BPS versus PRD and BPS versus SNR characteristics using DS2.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3139903&req=5

fig7: BPS versus PRD and BPS versus SNR characteristics using DS2.
Mentions: Figures 7 and 8 show the variations of PRD and SNR with BPS for DS2 and DS3, respectively, using the SLP.

Bottom Line: The objective is to obtain a greater compression gains at a low bit rate while preserving the clinical information content.Three neural network models, single-layer and multi-layer perceptrons and Elman network are used and the results are compared with linear predictors such as FIR filters and AR modeling.It is found from the results that the quality of the reconstructed signal is preserved at a low PRD thereby yielding better compression results compared to results obtained using lossless scheme.

View Article: PubMed Central - PubMed

Affiliation: Center for Biomedical Informatics and Signal Processing, Department of Biomedical Engineering, SSN College of Engineering, Chennai 603110, India.

ABSTRACT
A telemedicine system using communication and information technology to deliver medical signals such as ECG, EEG for long distance medical services has become reality. In either the urgent treatment or ordinary healthcare, it is necessary to compress these signals for the efficient use of bandwidth. This paper discusses a quality on demand compression of EEG signals using neural network predictors for telemedicine applications. The objective is to obtain a greater compression gains at a low bit rate while preserving the clinical information content. A two-stage compression scheme with a predictor and an entropy encoder is used. The residue signals obtained after prediction is first thresholded using various levels of thresholds and are further quantized and then encoded using an arithmetic encoder. Three neural network models, single-layer and multi-layer perceptrons and Elman network are used and the results are compared with linear predictors such as FIR filters and AR modeling. The fidelity of the reconstructed EEG signal is assessed quantitatively using parameters such as PRD, SNR, cross correlation and power spectral density. It is found from the results that the quality of the reconstructed signal is preserved at a low PRD thereby yielding better compression results compared to results obtained using lossless scheme.

No MeSH data available.