Research Article
Body Sensor Network Based Context Aware QRS Detection
@INPROCEEDINGS{10.1109/PCTHEALTH.2006.361683, author={Huaming Li and Jindong Tan}, title={Body Sensor Network Based Context Aware QRS Detection}, proceedings={1st International ICST Conference on Pervasive Computing Technologies for Healthcare}, publisher={IEEE}, proceedings_a={PERVASIVEHEALTH}, year={2007}, month={5}, keywords={Body Sensor Network (BSN) Medium Access Control (MAC) Electrocardiography (ECG) QRS complex detection activity classification.}, doi={10.1109/PCTHEALTH.2006.361683} }- Huaming Li
Jindong Tan
Year: 2007
Body Sensor Network Based Context Aware QRS Detection
PERVASIVEHEALTH
IEEE
DOI: 10.1109/PCTHEALTH.2006.361683
Abstract
In this paper, a body sensor network (BSN) based context aware QRS detection scheme is proposed. The algorithm uses the context information provided by the body sensor network to improve the QRS detection performance by dynamically selecting the leads with best SNR and taking advantage of the best features of two complementary detection algorithms. The accelerometer data from the BSN are used to classify the patients' daily activity and provide the context information. The classification results indicate both the type of the activities and their corresponding intensity, which is related to the signal/noise ratio of the ECG recordings. Activity intensity is first fed to lead selector to eliminate the leads with low SNR, and then is fed to a selector for selecting a proper QRS detector according to the noise level. MIT-BIH noise stress test database is used to evaluate the algorithms
