Dataset Information
Intelligent classification of heartbeats for automated real-time ECG monitoring.
Ontology highlight
ABSTRACT: Background
The automatic interpretation of electrocardiography (ECG) data can provide continuous analysis of heart activity, allowing the effective use of wireless devices such as the Holter monitor.Materials and methods
We propose an intelligent heartbeat monitoring system to detect the possibility of arrhythmia in real time. We detected heartbeats and extracted features such as the QRS complex and P wave from ECG signals using the Pan-Tompkins algorithm, and the heartbeats were then classified into 16 types using a decision tree.Results
We tested the sensitivity, specificity, and accuracy of our system against data from the MIT-BIH Arrhythmia Database. Our system achieved an average accuracy of 97% in heartbeat detection and an average heartbeat classification accuracy of above 96%, which is comparable with the best competing schemes.Conclusions
This work provides a guide to the systematic design of an intelligent classification system for decision support in Holter ECG monitoring.
SUBMITTER: Park J
PROVIDER: S-EPMC4270110 | biostudies-literature | 2014 Dec
REPOSITORIES: biostudies-literature
Publications
Intelligent classification of heartbeats for automated real-time ECG monitoring.
Telemedicine journal and e-health : the official journal of the American Telemedicine Association 20141201 12
<h4>Background</h4>The automatic interpretation of electrocardiography (ECG) data can provide continuous analysis of heart activity, allowing the effective use of wireless devices such as the Holter monitor.<h4>Materials and methods</h4>We propose an intelligent heartbeat monitoring system to detect the possibility of arrhythmia in real time. We detected heartbeats and extracted features such as the QRS complex and P wave from ECG signals using the Pan-Tompkins algorithm, and the heartbeats were ...[more]
