ECG Segmentation in a body sensor network using hidden Markov models
Document Type
Conference Proceeding
Publication Date
9-23-2008
Abstract
A novel approach for segmenting ECG signal in a body sensor network employing Hidden Markov Modeling (HMM) technique is presented. In traditional HMM methods inadequate and slow parameter adaptation is largely responsible for the low positive predictivity rate. To solve the problem, we introduce an active HMM parameter adaptation and ECG segmentation algorithm. Body sensor networks are used to pre-segment the raw ECG data by performing QRS detection. Instead of one single generic HMM, multiple individualized HMMs are used. Each HMM is only responsible for extracting the characteristic waveforms of the ECG signals with similar temporal features from the same group, so that the temporal parameter adaptation can be naturally achieved. ©2008 IEEE.
Publication Title
Proc. 5th Int. Workshop on Wearable and Implantable Body Sensor Networks, BSN2008, in conjunction with the 5th Int. Summer School and Symp. on Medical Devices and Biosensors, ISSS-MDBS 2008
Recommended Citation
Li, H.,
&
Tan, J.
(2008).
ECG Segmentation in a body sensor network using hidden Markov models.
Proc. 5th Int. Workshop on Wearable and Implantable Body Sensor Networks, BSN2008, in conjunction with the 5th Int. Summer School and Symp. on Medical Devices and Biosensors, ISSS-MDBS 2008, 285-288.
http://doi.org/10.1109/ISSMDBS.2008.4575075
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/10691