The Design Of Signal Generatoe For Aed Testing

With the development of science and technology, especially the uninterrupted growth in the fields of computer technology, electronic technology, digital signal processing technology, medical electronic equipment is also continuously to be more and more automatic and intelligent. AED is a typical representative of medical electronic equipment. It sets ecg signal acquisition, ecg characteristics identification, electric shock treatment at an organic whole. So the security requirement of the AED is extremely high, it needs signal source simulates a variety of ecg signal of normal and pathology to test and validate multiple technical indexes in the process of research and production, such as ecg acquisition, defibrillation decision, electric shock moment and discharge quantity, etc.In this paper, the ecg signal generator which can produce normal ecg waveform and pathological ecg waveform was designed. The generator was mainly used in the development and testing of the automatic defibrillation apparatus, it can also be used for teaching, scientific research and other fields. STM32F103 was used as the MCU. The paper designed the reset circuit, clock circuit, storage circuit, buttons and display circuit, and power circuit. Signal generating circuit uses the high-speed analog-to-digital conversion chip MCP4728. According to the principle of DDS structures, I set up the system hardware circuit to realize the output of waveform, finally the working condition of ecg signal generator was displayed on the LCD screen. In order to the testing demands of AED, the paper used the ecg database from Physio Bank site to create normal ecg signals, ventricular fibrillation signal waveform, etc. System program was written and completed in the compilation environment of Kiel MDK software with C language, and achieved the function of human-computer interaction of ecg signal generator.This paper collected the waveform signal which generated fromecg signal generator through the BIOPAC MP150 Recor, Then QRS waves of the ecg was positioned using the quadratic B-spline wavelet algorithm in the matlab environment, and the time domain features were compared between the original signal and the collected signal. At last, the paper calculated the wavelet entropy of these two signals by dividing frequency, and analyzed the change of the signal complexity to verify the reliability of the ecg signal generator.