Research And Implementation Of Wearable Remote ECG Monitoring System Based On Compressed Sensing

The sudden onset of cardiovascular disease has led to an increase in cardiovascular mortality,and the use of wearable telemedicine technology to monitor the heart condition of patients is becoming more widespread.Due to the low power requirements of wearable devices and their ability to process signals is limited,the traditional Nyquist sampling theorem is not applicable to signal sampling of wearable devices.It is necessary to sample signals based on the theory of compressed sensing.However,during the sampling process,the signal will be affected by the sampling rate and noise,and the sampled signal is not sparse enough.The existing signal reconstruction algorithm can not meet the requirements of the wearable remote ECG monitoring system to quickly and accurately reconstruct the ECG signal.Therefore,improving the existing signal reconstruction algorithm according to the application requirements of the system and applying it to the wearable remote ECG monitoring system has become an urgent problem to be solved.In response to the above problems,the main work of this thesis is as follows:(1)This thesis proposes an adaptive block sparse Bayesian learning reconstruction algorithm.Based on the Alternating Direction Method of Multipliers of block sparse Bayesian learning algorithm,the algorithm adaptively adjusts the step size in the Alternating Direction Method of Multipliers when the ECG data of different patients changes dynamically,which achieves the goals of monitoring patients with different physiological conditions.And the validity of the proposed adaptive block sparse Bayesian reconstruction algorithm is verified in the publicly collected MIT ECG database and the ECG data collected by the actual system.(2)This thesis designs and implements a wearable remote ECG monitoring system based on compressed sensing.The system includes acquisition end,data forwarding end,background server end and data display end.The acquisition end performs compression sampling based on the compressed sensing theory on the ECG signal,and the data forwarding end receives the sampled and compressed ECG data and completes the forwarding.The background server end embeds the adaptive block sparse Bayesian learning reconstruction algorithm proposed in this paper to reconstruct the ECG signal to realize the patient’s heart condition monitoring and data analysis.The data display end is used by doctors to view the ECG data of different patients and give specific diagnosis results and recommendations.In this thesis,the functional and performance of the wearable remote ECG monitoring system based on compressed sensing is tested.The test results show that the system can implement compression sampling based on compressed sensing theory on the patient’s ECG signal through the wearable device,and quickly and accurately reconstruct the patient’s ECG signal after transmission to the remote server through the network.The overall function of the system is accurate and stable,achieving the goal of remotely monitoring the patient’s physiological condition.