@inproceedings{fd9f3b9e664d4395a8bba5176973799b,
title = "Fault diagnosing ECG in body sensor networks based on hidden markov model",
abstract = "In this paper, we focus on medical body sensor networks collecting physiological signs to monitor the health of patients. We propose a Hidden Markov Model (HMM) based method for fault diagnosis of ECG sensor data. We firstly verify the Markov property of heart rate sequences by medical datasets. Then we use the Baum-Welch algorithm to estimate parameters of HMMs by history training data, and the Viterbi algorithm to determine whether the new sensor reading is fault. Finally, we do experiments on both real and synthetic medical datasets to study the performance of our method. The result shows that the proposed approach possesses a good detection accuracy with a low false alarm rate.",
keywords = "body sensor networks, ECG, fault diagosis, hidden Markov model",
author = "Haibin Zhang and Jiajia Liu",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 10th IEEE International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2014 ; Conference date: 19-12-2014 Through 21-12-2014",
year = "2014",
month = feb,
day = "27",
doi = "10.1109/MSN.2014.23",
language = "英语",
series = "Proceedings - 2014 10th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "123--129",
booktitle = "Proceedings - 2014 10th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2014",
}