Next Article in Journal
3D Face Modeling Using the Multi-Deformable Method
Next Article in Special Issue
On Increasing Network Lifetime in Body Area Networks Using Global Routing with Energy Consumption Balancing
Previous Article in Journal
Restoration of Reflection Spectra in a Serial FBG Sensor Array of a WDM/TDM Measurement System
Previous Article in Special Issue
Unobstructive Body Area Networks (BAN) for Efficient Movement Monitoring
Sensors 2012, 12(9), 12844-12869; doi:10.3390/s120912844

A Real-Time Cardiac Arrhythmia Classification System with Wearable Sensor Networks

1,2,*  and 1
1 Department of Mechanical, Aerospace and Biomedical Engineering, The University of Tennessee, Knoxville, TN 37996, USA 2 School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China
* Author to whom correspondence should be addressed.
Received: 25 July 2012 / Revised: 27 August 2012 / Accepted: 6 September 2012 / Published: 21 September 2012
(This article belongs to the Special Issue Body Sensor Networks for Healthcare and Pervasive Applications)


Long term continuous monitoring of electrocardiogram (ECG) in a free living environment provides valuable information for prevention on the heart attack and other high risk diseases. This paper presents the design of a real-time wearable ECG monitoring system with associated cardiac arrhythmia classification algorithms. One of the striking advantages is that ECG analog front-end and on-node digital processing are designed to remove most of the noise and bias. In addition, the wearable sensor node is able to monitor the patient’s ECG and motion signal in an unobstructive way. To realize the real-time medical analysis, the ECG is digitalized and transmitted to a smart phone via Bluetooth. On the smart phone, the ECG waveform is visualized and a novel layered hidden Markov model is seamlessly integrated to classify multiple cardiac arrhythmias in real time. Experimental results demonstrate that the clean and reliable ECG waveform can be captured in multiple stressed conditions and the real-time classification on cardiac arrhythmia is competent to other workbenches.
Keywords: wearable ECG; cardiac arrhythmia classification; hidden Markov model wearable ECG; cardiac arrhythmia classification; hidden Markov model
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Share & Cite This Article

Further Mendeley | CiteULike
Export to BibTeX |
EndNote |
MDPI and ACS Style

Hu, S.; Wei, H.; Chen, Y.; Tan, J. A Real-Time Cardiac Arrhythmia Classification System with Wearable Sensor Networks. Sensors 2012, 12, 12844-12869.

View more citation formats

Related Articles

Article Metrics

For more information on the journal, click here


[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert