BIOSIGNALS EVENTS DETECTION - A Morphological Signal-independent Approach

Abstract

Abstract:This study presents a signal-independent algorithm, which detects significant events in a biosignal, without previous knowledge or specific pre-processing steps. From a morphological analysis, the algorithm computes the instants when the most significant standard deviation discontinuities occur. An iterative optimization step is then applied. This assures that a minimal error is achieved when modeling the signal segments (between the detected instants) with a polynomial regression. The detection scale can be modified by an optional input scale factor. An objective algorithm performance evaluation procedure was designed, and applied on two types of synthetic signals, for which the events instants were previously known. An overall mean error of 20.32 (±16.01) samples between the detected and the real events show the high accuracy of the proposed algorithm. The algorithm was also applied on accelerometry and electromyography raw signals collected in different experimental scenarios. The fact that this approach does not require any previous knowledge and the good level of accuracy represents a relevant contribution in events detection and biosignal analysis.