Embedded real-time feature extraction for electrode inversion detection in telemedicine electrocardiograms.
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Date
2020
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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 repeti-
tions. 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 con-
trast 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 clas-
sifier 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.
Description
Keywords
Electrocardiography, Detection algorithms
Citation
TORRES, V. A. M. F. et al. Embedded real-time feature extraction for electrode inversion detection in telemedicine electrocardiograms. Biomedical Signal Processing and Control, v. 60, 2020. Disponível em: <https://www.sciencedirect.com/science/article/pii/S1746809420301026>. Acesso em: 29 abr. 2022.