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 those leads with the 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 daily activities of patients and provide context information. The classification results indicate the types of activities that were engaged in. They also indicate their corresponding intensity, which is related to the signal-to-noise ratio (SNR) of the ECG recordings. Activity intensity is first fed to the lead selector to eliminate those leads with low SNR, and then is fed to a selector to select a proper QRS detector according to the noise level. An MIT-BIH noise stress test database is used to evaluate the algorithms.
body sensor network (bsn) medium access control (mac) electrocardiography (ecg) qrs complex detection activity classification