Medically Relevant Criteria used in EEG Compression for Improved Post-Compression Seizure Detection

TL;DR

This paper evaluates EEG compression schemes that incorporate bio-physical signal models to preserve clinically relevant features, demonstrating improved seizure detection accuracy after compression.

Contribution

It introduces and compares three EEG compression methods based on physiological signal models, highlighting their effectiveness in maintaining seizure detection performance.

Findings

Bio-physically inspired models improve post-compression seizure detection.

Different reliance levels on physiological features affect compression resilience.

Compression schemes can preserve critical clinical information effectively.

Abstract

Biomedical signals aid in the diagnosis of different disorders and abnormalities. When targeting lossy compression of such signals, the medically relevant information that lies within the data should maintain its accuracy and thus its reliability. In fact, signal models that are inspired by the bio-physical properties of the signals at hand allow for a compression that preserves more naturally the clinically significant features of these signals. In this paper, we illustrate this through the example of EEG signals; more specifically, we analyze three specific lossy EEG compression schemes. These schemes are based on signal models that have different degrees of reliance on signal production and physiological characteristics of EEG. The resilience of these schemes is illustrated through the performance of seizure detection post compression.