e-journal

Multichannel EEG Compression: Wavelet-Based Image and Volumetric Coding Approach

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Abstract—In this paper, lossless and near-lossless compression algorithms for multichannel electroencephalogram (EEG) signals arepresentedbasedonimageandvolumetriccoding.Multichannel EEG signals have significant correlation among spatially adjacent channels; moreover, EEG signals are also correlated across time. Suitable representations are proposed to utilize those correlations effectively. In particular, multichannel EEG is represented either in the form of image (matrix) or volumetric data (tensor), next a wavelet transform is applied to those EEG representations. The compression algorithms are designed following the principle of “lossy plus residual coding,” consisting of a wavelet-based lossy coding layer followed by arithmetic coding on the residual. Such approach guarantees a specifiable maximum error between original and reconstructed signals. The compression algorithms are applied to three different EEG datasets, each with different samplingrateandresolution.Theproposedmultichannelcompression algorithms achieve attractive compression ratios compared to algorithms that compress individual channels separately.

Index Terms—Arithmetic coding, compression, electroencephalogram (EEG), multichannel EEG, set partitioning coding.

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Informasi Detail

Judul Seri

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No. Panggil

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Penerbit

: IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS., 2013

Deskripsi Fisik

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, VOL. 17, NO. 1, JANUARY 2013 p. 113-120

Bahasa

English

ISBN/ISSN

1089-7771

Klasifikasi

NONE

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

KESEHATAN
KEDOKTERAN-ALAT DAN PERLENGKAPAN

Info Detail Spesifik

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Pernyataan Tanggungjawab

K. Srinivasan, Justin Dauwels, and M. Ramasubba Reddy

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