An Efficient Near-lossless Compression Algorithm for Multichannel EEG signals

In many biomedical measurement procedures, it is important to record a huge amount of data, to monitor the state of health of a subject. In such a context, electroencephalograph (EEG) data are one of the most demanding in terms of size and signal behavior. In this paper, we propose a near-lossless c...

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Veröffentlicht in:2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA) S. 1 - 6
Hauptverfasser: Campobello, Giuseppe, Quercia, Angelica, Gugliandolo, Giovanni, Segreto, Antonino, Tatti, Elisa, Ghilardi, Maria Felice, Crupi, Giovanni, Quartarone, Angelo, Donato, Nicola
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 23.06.2021
Schlagworte:
biomedical signal processing
Distortion
Distortion measurement
Electroencephalograph (EEG)
Electroencephalography
Memory management
near-lossless compression
Noise reduction
Signal processing algorithms
Singular Value Decomposition (SVD)
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Zusammenfassung:In many biomedical measurement procedures, it is important to record a huge amount of data, to monitor the state of health of a subject. In such a context, electroencephalograph (EEG) data are one of the most demanding in terms of size and signal behavior. In this paper, we propose a near-lossless compression algorithm for EEG signals able to achieve a compression ratio in the order of 10 with a root-mean-square distortion less than 0.01%. The proposed algorithm exploits the fact that Principal Component Analysis is usually performed on EEG signals for denoising and removing unwanted artifacts. In this particular context, we can consider this algorithm as a good tool to ensure the best information of the signal beside an efficient compression ratio, reducing the amount of memory necessary to record data.
DOI:10.1109/MeMeA52024.2021.9478756