Multimodal information fusion for robust heart beat detection

Author

Quan Ding ; Yong Bai ; Erol, Yusuf Bugra ; Salas-Boni, Rebeca ; Xiaorong Zhang ; Lei Li ; Xiao Hu

Author_Institution

Univ. of California, San Francisco, San Francisco, CA, USA

fYear

2014

Firstpage

261

Lastpage

264

Abstract

QRS detection based on ECG signal is the most straightforward method for heart beat detection. However, existing QRS detection methods do not work well when ECG signal is contaminated or missing. Other physiological signals also contain information about cardiac activity and ECG. Their information can be explored for robust heart beat detection. As part of the PhysioNet/Computing in Cardiology Challenge 2014, this study proposed a multimodal information fusion framework for robust heart beat detection. The framework consisted of three steps: 1) QRS detection. 2) Remove spurious QRS detection using pulsatile signal if it is available. 3) Refine the remaining beat detection and interpolate missed beats. Results show that the algorithm can sufficiently reduce spurious QRS detection and accurately fill in missed beats.

Keywords

bioelectric potentials; cardiovascular system; electrocardiography; medical signal detection; medical signal processing; QRS detection based ECG signal; cardiac activity; heart beat detection; multimodal information fusion framework; physiological signals; pulsatile signal; Abstracts; Electrocardiography; Electroencephalography; Glass; Open source software; Physiology;

fLanguage

English

Publisher

ieee

Conference_Titel

Computing in Cardiology Conference (CinC), 2014

ISSN

2325-8861

Print_ISBN

978-1-4799-4346-3

Type

conf

Filename

7043029