Body sensor network based context aware QRS detection.
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.
Li
Huaming
H
Dept. of Electr. & Comput. Sci., Michigan Technol. Univ., Houghton, MI 49931, USA.
Tan
Jindong
J
D000465:Algorithms; D001698:Biomedical Engineering; D015374:Biosensing Techniques; D003936:Diagnosis, Computer-Assisted; D004562:Electrocardiography / Q000706:statistics & numerical data; D006331:Heart Diseases / Q000175:diagnosis; D006801:Humans; D018670:Monitoring, Ambulatory / Q000379:methods* / Q000706:statistics & numerical data; D013686:Telemetry / Q000379:methods* / Q000706:statistics & numerical data