By Topic

An Automated System for On-line Monitoring and Detection of ST Changes in ECG Signal

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Mohebbi, M. ; K.N.Toosi Univ. of Technol., Tehran ; Moghadam, H.A. ; Teshnehlab, M.

In this paper, we present n new automated system for on-line monitoring and detection of ST changes in one channel electrocardiograms (ECG). This system consists of a preprocessing step for QRS detection, baseline wandering removal, and noise suppression. In the next step, the system uses a normal beat template as reference and a set of rules defined by cardiologists for detecting ischemic beats based on ST slope/deviation measurements. In the third step, the system uses a window classification for detecting sequences of ischemic beats. In the final step ischemic episodes in ECG signal are detected by merging sequences which are close together. Our system is advantageous with respect to similar algorithms; because it uses a template for rejecting abnormal ECG waveforms and accurate measurement of ST deviations. We have also modified some rules used by previous similar works in order to correspond better to the criteria used by cardiac specialists. A graphical user interface has been also developed for real time monitoring of the ST amplitude/slope along with the ECG signal. The performance of the system was evaluated using a subset of ESC ST-T database including 48 records. This evaluation demonstrated high sensitivity (94.5%) and good positive predictivity (85.03%) of our system.

Published in:

Signal Processing and Communications Applications, 2007. SIU 2007. IEEE 15th

Date of Conference:

11-13 June 2007