The distortion of data compression via compressed sensing in EEG telemonitoring for the epileptic.

Compressed sensing (CS) has been used as an efficient, low-energy data compressor for the telemonitoring of physiological signals such as ECG or EEG. However, the performance of these applications is often assessed by the quality of signal recovery such as Percentage Root-mean square Difference (PRD). In this paper, (1) we show that the metric of signal recovery quality such as PRD is not adequate for CS based compressor, especially when failing to regard the sampling rate of raw signals. (2) we analyze the distortion of CS on EEG telemonitoring for the epileptic, where the performance is evaluated using different sampling rates and the quality is assessed by epileptic seizure classification accuracy. We aim to establish a task-oriented protocol for correctly applying CS as a data compressor on EEG and other physiological signals.