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.

An efficient near-lossless compression algorithm for multichannel EEG signals

Campobello G.
Primo
;
Quercia A.
Secondo
;
Gugliandolo G.;Segreto A.;Crupi G.;Quartarone A.
Penultimo
;
Donato N.
Ultimo
2021-01-01

Abstract

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.
2021
978-1-6654-1914-7
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11570/3209275
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 8
  • ???jsp.display-item.citation.isi??? 2
social impact