Energy-Efficient ECG Signal Compression Method in Wearable Body Sensor Networks by Leveraging Empirical Mode Decomposition

Abstract

Wireless wearable body sensor networks are widely used in the continuous daily monitoring of vital parameters such as electrocardiography (ECG). The wireless wearable technology is a key enabling technology for out-hospital patient-centric healthcare which enables the improvement for the ability of prevention, diagnosis and patient life quality. However, as wireless transmission is power-demanding and the power supply from battery is also limited, the energy efficiency issue impose a stringent constraint on the continuous vitals monitoring in wearable body sensor networks (BSN). Signal compression for biosignals provides a good solution to reduce the power consumption of data transmission by decreasing the transmitted data size of the biosignals. In this study, we develop the first scheme of applying empirical mode decomposition (EMD) on ECG signal for feature extraction and compression and further propose a new ECG signal compression framework based on EMD constructed feature dictionary for energy-efficient ECG sensing in wearable body sensor networks. Our method is validated with the ECG data from MIT-BIH arrhythmia database and compared with existing methods. Based on the simulations on the ECG signals from MIT-BIH, the results show that our method achieves the compression ratio (CR) up to 133 with RMSE of 5.58% and the average CR of 96.08 with RMSE 6.69% which is more than twice of the highest CR among other existing methods with similar recovering error rate of around 6%. For diagnostic distortion perspective, our method achieves high QRS detection performance with the sensitivity (SE) of 99.8% and the specificity (SP) of 99.6%, which shows that our ECG compression method can preserve almost all the QRS features and have no impact on the diagnosis process. In addition, the energy consumption is only 30% of the energy consumption achieved by the other methods considering the ECG signals from MIT-BIH database. The excellent compressing performance and low energy consumption make it outperform the prior studies in ECG data compressing for wearable body sensor networks.