Fault Diagnosing ECG in Body Sensor Networks Based on Hidden Markov Model

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.