TY  - JOUR
A1  - Chromik, Jonas
A1  - Pirl, Lukas
A1  - Beilharz, Jossekin Jakob
A1  - Arnrich, Bert
A1  - Polze, Andreas
T1  - Certainty in QRS detection with artificial neural networks
JF  - Biomedical signal processing and control
N2  - Detection of the QRS complex is a long-standing topic in the context of electrocardiography and many algorithms build upon the knowledge of the QRS positions. Although the first solutions to this problem were proposed in the 1970s and 1980s, there is still potential for improvements. Advancements in neural network technology made in recent years also lead to the emergence of enhanced QRS detectors based on artificial neural networks. In this work, we propose a method for assessing the certainty that is in each of the detected QRS complexes, i.e. how confident the QRS detector is that there is, in fact, a QRS complex in the position where it was detected. We further show how this metric can be utilised to distinguish correctly detected QRS complexes from false detections.
KW  - QRS detection
KW  - Electrocardiography
KW  - Artificial neural networks
KW  - Machine
KW  - learning
KW  - Signal-to-noise ratio
Y1  - 2021
U6  - https://doi.org/10.1016/j.bspc.2021.102628
SN  - 1746-8094
SN  - 1746-8108
VL  - 68
PB  - Elsevier
CY  - Oxford
ER  -