  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.
