Detection of heartbeats based on the Bayesian framework

Wen-Long Chin, Jong Hun Yu, Cheng Lung Tseng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The detection of heartbeat is an important and challenging issue for health care. This work proposes to estimate the QRS complex parameters based on the maximum-likelihood (ML) principle. To this goal, a new signal model and its Bayesian framework are studied. Detectors or estimators based on the Bayesian framework are considered to be optimal in the statistical signal processing point of view. To reduce the complexity of original method, its iterative counterpart is investigated by using the decomposition method. Detailed information of QRS complexes, including the starting point, duration, and period, can be derived by the proposed method for further medical diagnosis. Simulations using the benchmark MIT-BIH Arrhythmia database verify the advantages of the proposed approaches compared to traditional ones.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages612-616
Number of pages5
ISBN (Electronic)9781509023769
DOIs
Publication statusPublished - 2017 Mar 27
Event2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016 - Beijing, China
Duration: 2016 Aug 132016 Aug 15

Publication series

Name2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016

Other

Other2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016
CountryChina
CityBeijing
Period16-08-1316-08-15

All Science Journal Classification (ASJC) codes

  • Signal Processing

Fingerprint Dive into the research topics of 'Detection of heartbeats based on the Bayesian framework'. Together they form a unique fingerprint.

Cite this