Real-time myocardial infarction diagnosis system based on FPGA and convolutional neural network
Gao Xinwei，Liu Wenhan，Xie Wenxin，Huang Qijun
(School of Physics and Technology， Wuhan University， Wuhan 430072， China)
Abstract： Aiming at the demand of miniaturized daily ECG monitoring system, a real-time diagnosis system of myocardial infarction disease based on FPGA and convolutional neural network algorithm is designed. The system consists of morphological filter, least mean square adaptive notch filter and neural network hardware acceleration module. By parallel and accelerated processing in FPGA, real-time monitoring and diagnosis of cardiovascular diseases are realized. After verification on the board, the system achieves 99.91% relative accuracy, the on-chip power consumption is 2.39 W, and the processing time is 3.81 ms, which is suitable for various design requirements.
Key words : myocardial infarction；real-time system；digital filter；neural network accelerator
