Search

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 biophysical 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.

A novel technique for Electroencephalogram (EEG) compression is proposed in this article. This technique makes use of the inter-channel redundancy present between different EEG channels of the same recording and the intra-channel redundancy between the different samples of a specific channel. It uses Discrete Wavelet Transform (DWT) and Set partitioning in hierarchical trees (SPIHT) in 2-D to code the EEG channels. Smoothness transforms are added in order to guarantee good performance of SPIHT in 2-D. Experimental results show that this technique is able to provide low distortion values for high compression ratios (CRs). In addition, performance results of this method do not vary a lot between different patients which proves the stability of the method when used with recordings of different characteristics.