Intelligent classification of heartbeats for automated real-time ECG monitoring.

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 acc

SUBMITTER: Park J 

PROVIDER: S-EPMC4270110 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature