Research Of Remote Real-time ECG Monitoring System Based On GPRS

With the development of society, people are increasingly concerned about their health. Elderly patients, chronic diseases patients and emergency patients all require a platform which can continuously monitor important physiological parameters and allow doctor-patient communication anytime and anywhere. With the development of modern communication technology, especially the popularity of mobile communications and Internet technology, remote ECG monitoring has begun to show an increasingly strong attraction. The prevention and diagnosis-based ECG monitoring system has great practical significance as it is family and individual oriented and also allows timely communication and exchange of information with the hospital.In this paper, a GPRS-based remote real-time ECG detection system was studied and designed on the basis of the laboratory project. The system could transmit ECG terminal equipment user information and real-time ECG data collected to the monitoring central workstation through the GPRS network, help doctors monitor and analysis patients’current cardiac functional status through the ECG data collected which was analyzed and managed by care centers. Main work of this paper is organized as follows:First, the paper introduced the research background and significance of the remote ECG monitoring, made a review of current status of remote ECG and presented the overall system architecture idea and composition modules.Second, the paper analyzed the principle of short messaging through GPRS and wireless data transmission protocol, developed ECG data encoding format for wireless transmission, completed wireless transmission of user information and ECG waveform data, and finally realized reliable data communication between user terminal and care center.Third, the paper completed the design and development of the monitoring center workstation. Four basic modules of the care centers were completed:data transmission, user work interface, automatic analysis of ECG and ECG data management.Fourth, the paper performed ECG data denoising and feature extraction through wavelet analysis method. For ECG data denosing, Daubechies wavelet was adopted as denoising wavelet base, soft thresholding was applied to the scale wavelet coefficients in which noise existed through the method of wavelet decomposition and reconstruction, and noise and drift was removed. For ECG waveform feature extraction, different scales of Mexican-Hat wavelet decomposition analysis were used for the R-wave and Q-wave, S-wave, QRS starting position and the P-wave T-wave positioning.Identification and location algorithm was tested with MIT-BIH data and the results showed that the algorithm had good performance and basically met the design requirements.Finally, a systematic summary of the paper work was done and future research directions were pointed out.