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

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Abstract

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

N QRS complex detectors simultaneously monitor N ECG channels. Once a detection occurs in a given channel, a 150 ms long window is opened to look for detections in other channels. Within this window, yn = + 1 if a QRS complex is detected and yn = − 1 otherwise. A coefficient α n, obtained during a training period and related to the detection performance in channel n, multiplies the detection signal yn, so that greater weights are assigned to ECG channels where single-channel detectors performed better. Finally, the binary detection decision (f ) is obtained from the comparison of the weighted sum of single-channel detections (z) with a fixed threshold (β)

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Authors and Affiliations

  1. Department of Mechanical Engineering, University College London, London, UK

    Carlos A. Ledezma

  2. Faculty of Electrical and Electronic Engineering, Pontifical Bolivarian University, Bucaramanga, Colombia

    Miguel Altuve

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  1. Carlos A. Ledezma
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  2. Miguel Altuve
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Correspondence to Miguel Altuve.

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Ledezma, C.A., Altuve, M. Optimal data fusion for the improvement of QRS complex detection in multi-channel ECG recordings. Med Biol Eng Comput 57, 1673–1681 (2019). https://doi.org/10.1007/s11517-019-01990-3

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  • DOI: https://doi.org/10.1007/s11517-019-01990-3

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