基于神经网络的心电智能监测系统设计_毕业论文.pdf
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中北大学学位论文
Design of ECG Intelligent Monitoring System Based on Neural
Network
Abstract
With the accelerated pace of people's lives and increased pressure, coupled with the
growing trend of population aging, the incidence of heart disease and other diseases is
increasing, seriously threatening people's lives and health safety, and people are gradually
paying attention to their own health, and the demand for medical devices for active monitoring
is increasing. Most of the existing ECG monitoring systems are limited to the transmission,
reception and display of ECG signals, and without the help of medical professionals, the
subjects cannot understand their own heart health status. In this paper, we combine deep
learning with ECG monitoring system and design a neural network-based ECG intelligent
monitoring system. In addition to the basic acquisition, transmission and display, we add the
intelligent diagnosis function of ECG data, which can make preliminary diagnosis of ECG data
and facilitate the subjects to know their heart health status in time. The main contents of this
paper are as follows:
Firstly, the paper analyzes the characteristics of ECG signals as well as the application
scenarios and needs of the intelligent monitoring system, and introduces the general scheme of
the ECG intelligent monitoring system. Then, the design of the ECG acquisition device and the
ZigBee transmission module are introduced. The processing circuit of the ECG signal uses the
ADS1292R chip, the master control module uses Arduino, and the ZigBee module uses the
CC2530 chip.
Then, the noise and pre-processing methods of the ECG signal are introduced, and the
Finite Impulse Response (FIR) filter used is described. After that, the paper describes in detail
the algorithm to perform ECG intelligent recognition, and the CNN model is finally selected
by designing, testing and studying the models of Long-Short Term Memory (LSTM) network,
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