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Neurophysiology Signal Codecs for the DICOM® Standard: Preliminary Results.

Filippo Battaglia1, Mattia Galanti2, Giovanni Gugliandolo1

  • 1Department of Engineering, University of Messina Messina, Italy.

... IEEE International Symposium on Medical Measurements and Applications : Proceedings. IEEE International Symposium on Medical Measurements and Applications
|June 20, 2025
PubMed
Summary

Compression of electroencephalography (EEG) signals can tolerate up to 5% distortion, according to neurologists. Surprisingly, audio codecs offer performance comparable to specialized algorithms for EEG data compression.

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Area of Science:

  • Medical Imaging and Data Compression
  • Neuroscience and Biomedical Engineering

Background:

  • Working Group 32 (WG-32) of Digital Imaging and Communications in Medicine (DICOM) is standardizing clinical neurophysiology data exchange.
  • Existing compression techniques for neurophysiology signals are being evaluated for effectiveness.

Purpose of the Study:

  • To investigate and compare compression techniques for neurophysiology signals, including specialized algorithms and audio codecs.
  • To determine the maximum tolerable distortion for electroencephalography (EEG) signals in clinical settings.

Main Methods:

  • Experimental comparison of various compression techniques on real-world neurophysiology datasets.
  • Utilization of a web-based application (EEGnet) for viewing and annotating EEG data.
  • Expert evaluation by eight neurologists to identify clinically significant signal distortions.

Main Results:

  • A Percentage Root Mean Square Difference (PRD) of 5% was found to be acceptable for EEG signals by clinical experts.
  • Distortion exceeding a PRD of 15% was deemed unacceptable by all experts.
  • Audio codecs demonstrated performance comparable to state-of-the-art EEG compression algorithms in some cases.

Conclusions:

  • The study provides crucial data on acceptable distortion levels for EEG signal compression, aiding DICOM standardization efforts.
  • Audio codecs present a viable and potentially efficient alternative for neurophysiology signal compression.
  • Findings support the development of effective data compression strategies for clinical neurophysiology, enhancing data exchange and storage.