Human ECG signal is weak, the signal-to-noise ratio is small, the collected ECG signal amplification and filtering are often accompanied by interference, the interference from the body's own interference such as EMG interference, but also from outside interference such as 50Hz power frequency interference[2]. These disturbances degrade the signal-to-noise ratio of the system, and can even overwhelm the useful weak ECG signal, especially 50Hz power frequency interference. If not to eliminate to

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... very small extent, it will affect the subsequent signal processing, diagnosis, recognition accuracy. In addition the ECG signal also includes the following two kinds of interference: First, baseline drift [3]. Usually caused by human respiration and excitement of the heart and legs, the body's movement will lead to a certain amount of baseline drift, the frequency is lower than 1Hz, the performance of the slow changes in the curve; Second, EMG interference, which is caused by human body tremor, its frequency range is very wide. Usually between 5 ~ 2kHz[1], the performance of the rapid changes in irregular waveforms. In addition, due to the test sex, age and skin conductivity, so there are differences and the impact of equipment, system acquisition of different people's ECG signal is also very different. Therefore, in order to eliminate these interference and obtain an effective ECG signal, this paper presents a genetic neural network based on human heart ECG parameters acquisition system. By combining the nonlinear approximation ability of wavelet function with the self-learning characteristic of neural network, the output accuracy of sensor can be effectively improved, the effect of non-target parameter on detection result can be eliminated, and the detection accuracy of human physiological parameters can be improved. Through the experimental test, the ECG parameters of the device are accurate and reliable data has a good practical value.