Embedded real-time feature extraction for electrode inversion detection in telemedicine electrocardiograms

Abstract

Early detection of technical errors in medical examinations, especially in remote locations, is of utmost importance in order to avoid invalid measurements that would require costly and time consuming repetitions. This paper proposes a highly efficient method for the identification of an erroneous inversion of the measuring electrodes during a multichannel electrocardiogram. Therefore, a widely applied approach for heart beat detection is modified and approximated feature extraction techniques are employed. In contrast to existing works, the improved heart beat identification requires no removal of baseline wandering and no amplitude related thresholds. Furthermore, a piecewise linear approximation of the baseline and basic calculations are sufficient for extracting the cardiac axis, which allows the construction of a classifier capable of quickly detecting electrode reversals. Our implementation indicates that the proposed method has minimal hardware costs and is able to operate in real-time on a simple micro-controller.