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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.


A P-th order multi-layer perceptron network.
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fig3: A P-th order multi-layer perceptron network.

Mentions: Neural networks possess certain attractive properties such as massive parallelism, robustness, adaptive learning, self-organization, fault tolerance, and generalization which are useful to enhance the performance of a predictor [29]. The purpose of the predictor is to decorrelate the input data thereby reducing the amplitude range of the data and generating a sequence, which is approximately white Gaussian. In this paper, the neural network models considered are: (1) single-layer perceptron (SLP), (2) multi-layer perceptron (MLP), (3) Elman network (EN). The architectures of SLP, MLP, and EN with P-th predictor order are shown in Figures 2, 3, and 4, respectively [27, 30]. The first two networks are feed forward models whereas the third one is a feedback network.


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

Sriraam N - Int J Telemed Appl (2011)

A P-th order multi-layer perceptron network.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig3: A P-th order multi-layer perceptron network.
Mentions: Neural networks possess certain attractive properties such as massive parallelism, robustness, adaptive learning, self-organization, fault tolerance, and generalization which are useful to enhance the performance of a predictor [29]. The purpose of the predictor is to decorrelate the input data thereby reducing the amplitude range of the data and generating a sequence, which is approximately white Gaussian. In this paper, the neural network models considered are: (1) single-layer perceptron (SLP), (2) multi-layer perceptron (MLP), (3) Elman network (EN). The architectures of SLP, MLP, and EN with P-th predictor order are shown in Figures 2, 3, and 4, respectively [27, 30]. The first two networks are feed forward models whereas the third one is a feedback network.

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.