Scheduled System Maintenance on May 29th, 2015:
IEEE Xplore will be upgraded between 11:00 AM and 10:00 PM EDT. During this time there may be intermittent impact on performance. For technical support, please contact us at We apologize for any inconvenience.
By Topic

Searching for non-sense: identification of pacemaker non-sense and non-capture failures using machine learning techniques

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

2 Author(s)
Malinowski, M.R.B. ; Marquette Univ., Milwaukee, WI, USA ; Povinelli, R.J.

Abnormal or unexpected function of pacemakers due to mechanical failure of the implantation, electrical failures of the battery and electrodes, or physiological failures to respond to the stimulus may cause harm to a patient. A novel Bayesian decision tree algorithm is proposed to detect two types of pacemaker failures, non-sense and non-capture, without a priori knowledge of pacemaker type, model, or programming. A variety of pacemaker devices and modes were studied, including devices with single and dual chamber pacing; single and dual chamber sensing; and fixed rate and rate adaptive pacing. 12-lead ECG signals were acquired from 34 pacemaker patients at rest. These signals were annotated by a team of experts. A 10-fold cross-validation was performed on the data set to test the algorithm. Out-of-sample sensitivity and specificity of 87.8% and 98.7%, respectively, were achieved. This work shows that non-sense and non-captures pacemaker failures can be detected with high sensitivity and specificity without prior knowledge of the pacemaker type, model or programming, making this algorithm clinically relevant in emergency room environments where such pacemaker information may be unavailable.

Published in:

Computers in Cardiology, 2003

Date of Conference:

21-24 Sept. 2003