title: A real-time cardiac arrhythmia classification system with wearable sensor networks.
abstract: 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.
authors.affiliation: Department of Mechanical, Aerospace and Biomedical Engineering, The University of Tennessee, Knoxville, TN 37996, USA. shengh@mtu.edu
authors.forename: Sheng
authors.initials: S
authors.lastname: Hu
authors.forename: Hongxing
authors.initials: H
authors.lastname: Wei
authors.forename: Youdong
authors.initials: Y
authors.lastname: Chen
authors.forename: Jindong
authors.initials: J
authors.lastname: Tan
mesh_terms: D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000175:diagnosis*; D003199:Computer Systems; D015716:Electrocardiography, Ambulatory / Q000379:methods*; D006801:Humans; D012815:Signal Processing, Computer-Assisted
keywords: cardiac arrhythmia classification; hidden Markov model; wearable ECG