Optimal data fusion for the improvement of QRS complex detection in multi-channel ECG recordings

The automatic analysis of the electrocardiogram (ECG) begins, traditionally, with the detection of QRS complexes. Afterwards, useful information can be extracted from it, ranging from the estimation of the instantaneous heart rate to nonlinear heart rate variability analysis. A plethora of works have been published on this topic; consequently, there exist many QRS complex detectors with high-performance values. However, just a few detectors have been conceived that profit from the information contained in several ECG leads to provide a robust QRS complex detection. In this work, we explore the fusion of multi-channel ECG recordings QRS detections as a means to improve the detection performance. This paper presents a decentralized multi-channel QRS complex fusion scheme that optimally combines single-channel detections to produce a single detection signal. Using six different widely used QRS complex detectors on the MIT-BIH Arrhythmia and INCART databases, a reduction in false and missed detections was achieved with the proposed approach compared with the single-channel counterpart. Furthermore, our detection results are comparable with the performance of other multi-channel detectors found in the literature, showing, in turn, various advantages in scalability, adaptability, and simplicity in the system’s implementation

.