Biosignals events detection. A Morphological Signal independent Approach

Abstract

This study presents a signal-independent algorithm, which detects significant events in a biosignal, withoutprevious knowledge or specific pre-processing steps. From a morphological analysis, the algorithm computesthe instants when the most significant standard deviation discontinuities occur. An iterative optimization stepis then applied. This assures that a minimal error is achieved when modeling the signal segments (betweenthe detected instants) with a polynomial regression. The detection scale can be modified by an optional inputscale factor. An objective algorithm performance evaluation procedure was designed, and applied on twotypes of synthetic signals, for which the events instants were previously known. An overall mean error of20.32 ( 16.01) samples between the detected and the real events show the high accuracy of the proposedalgorithm. The algorithm was also applied on accelerometry and electromyography raw signals collected indifferent experimental scenarios. The fact that this approach does not require any previous knowledge and thegood level of accuracy represents a relevant contribution in events detection and biosignal analysis.