Authors:
Jakub Kuzilek
1
and
Lenka Lhotska
2
Affiliations:
1
Dept. of Cybernetics, FEE and CTU in Prague, Czech Republic
;
2
FEE CTU in Prague, Czech Republic
Keyword(s):
QRS Detection, AdaBoost, Combining Classifiers.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
Beat detection is a basic and fundamental step in electrocardiogram (ECG) processing. In many ECG application time is crucial and slow beat detection algorithm may cause serious problems. Beat detection algorithm desired property is to detect sufficiently large number of QRS complexes with small error in shortest time as possible. Our proposed method tries to combine weak and fast QRS detectors such as amplitude threshold based detector in order to obtain better detection result with very low computational increase. We developed a modified version of the well known AdaBoost algorithm for combining weak QRS detectors. Our algorithm has been compared with the performance of our implementation of the Pan-Tompkins’s beat detection algorithm.