Study On Dynamic Electrocardiogram Monitoring System And Electrocardiosignal Processing Method

The incidence of heart disease is increasing annually and there is a lowering age trend with the quickened circadian rhythm and intensifying competition. The heart disease is seriously imperiling human health and life. The dynamic electrocardiogram monitoring system can make continuous records of the 24-hour electrocardiosignals and provide real-time monitoring. And, patients can be timely diagnosed and treated based on the heart abnormalities that are automatically analyzed. This system is playing very important roles, especially in diagnoses of the sudden, transient arrhythmia, ischemia myocardial and so on.This paper represented a perfect dynamic electrocardiogram monitoring system capable of electrocardiosignal record, data storage, dynamic display, automatic analysis, and telemonitoring. It adopted the low-power-consumption, high-performance microcontroller STM32F103ZE based on the Cortex-M3 ARM core. Ample peripherals of the chip helped to reduce hardware costs. During the data acquisition, electrocardiosignals were collected by 10 electrodes fixed on body surface and 12 combined leads, and then were converted into digital signals by the analog-to-digital converter of STM32F103ZE after two-stage amplification. The 8G-byte NAND flash memory K9NCG08U5M served to the storage of 24-hour electrocardiogram data. Afterwards, electrocardiogram data were transferred to the upper monitor by the USB high-speed data transmission. Then, the waves were dynamically display, and the diagnostic information was acquired. The analyses of electrocardiogram data and diagnoses of abnormities were automatically completed. The wireless communication module based on GPRS realized the telemonitoring so that the user could be supplied with real-time monitoring, routine examination, daily health care and other services. At the same time, the user could be timely rescued in emergencies.This paper detailed on the electrocardiosignal processing method around two aspects:the electrocardiosignal preprocessing and waveform detection. The improved Levkov was chosen to filter out the 50Hz power-line interference. An algorithm based on PID adjustment for dynamic electrocardiosignal baseline drift removal was put forward, and had played an excellent part in practice. As for the electrocardiosignal waveform detection, it adopted the improved differential threshold method for localization of R-waves, and the improved Tompkins differential method for detection of QRS complex. The MATLAB simulation results using records from MIT-BIH arrhythmia database account for the simplicity, practicability and high accuracy of both above methods so that requirements of the real-time analysis of electrocardiosignal could be met.Several model machines with leads that reserved for our special use had been developed. Simulation and clinical trial results indicated the dynamic electrocardiogram monitoring system had high reliability and stability. And the analysis and diagnose of abnormities could be automatically completed according to the accurate electrocardiosignal diagnostic information.