eprintid: 3784
rev_number: 8
eprint_status: archive
userid: 331
dir: disk0/00/00/37/84
datestamp: 2019-12-20 04:39:30
lastmod: 2019-12-20 04:40:07
status_changed: 2019-12-20 04:39:30
type: article
metadata_visibility: show
creators_name: Hoang, Van Manh
creators_name: Pham, Manh Thang
creators_id: manhhv87@vnu.edu.vn
creators_id: thangpm@vnu.edu.vn
title: Automatic Detection of Myocardial Infarction Based on 
High-Frequency QRS Analysis
ispublished: pub
subjects: ElectronicsandComputerEngineering
divisions: fac_fema
abstract: In this paper, we present an algorithm for automatic detection of myocardial infarction using high-frequency components of the ECG signal. Firstly, the QRS complexes and their boundaries are identified. Then, the correlation matrix between the detected QRS complexes in each lead is determined to eliminate noises and ectopic oscillations. The dominant QRS complexes are finally determined using cluster analysis. These resulting values are averaged to have a unique representative QRS complex in a given lead. This averaged signal is then passed through a band-pass filter to obtain high-frequency components of the QRS complex. Finally, the High-Frequency Morphological Index (HFMI) for each lead is calculated and diagnosed with myocardial infarction based on decision rules. The performance of the proposed algorithm is evaluated on signals from the PTB database. The obtained results show that the proposed method reached satisfactory performance compared with the results from clinical studies.
date: 2019-12
date_type: published
publisher: Vietnam National University Hanoi
official_url: https://js.vnu.edu.vn/NST/index
full_text_status: public
publication: VNU Journal of Science: Natural Sciences and Technology
volume: 35
number: 4
refereed: TRUE
issn: 1859-3585
citation:   Hoang, Van Manh and Pham, Manh Thang  (2019) Automatic Detection of Myocardial Infarction Based on High-Frequency QRS Analysis.  VNU Journal of Science: Natural Sciences and Technology, 35  (4).    ISSN 1859-3585     
document_url: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3784/1/manhhv.pdf