A Real-time Wearable Smart Cloth For Myocardial Ischemia Monitoring

ECG monitoring and prevention of cardiovascular disease,cardiovascular disease is an important tool for cardiovascular disease with chronic and sudden characteristics,so that long-term ECG monitoring system has important clinical significance.Current ECG monitoring system is gradually changing from electronic medical to mobile medical and wearable medical.With the rapid development of the field of large data and intelligent hardware,Wearable ECG monitoring equipment has gradually become the focus of the field of smart medical,and wearable smart clothing is the most potential Wearable ECG monitoring equipment.Wearable ECG monitoring equipment is currently experiencing real-time and high power consumption.This paper mainly involves a real-time low-power ECG monitoring of wearable smart clothing,and put forward the real-time low-power algorithm for wearable devices,including the following five parts:(1)A smart wearable sensor is designed,which is composed of a traditional textile fiber and metal fiber.The wearer will not feel uncomfortable in the process of wearing.(2)The hardware system of Wearable ECG monitoring system is built,which not only designs the module circuit(ECG signal processing module,data acquisition module,data storage module,data transmission module,data interface and power module,etc.),but also the circuit is debugged,the size of the circuit is improved,and the size of the final system is only 3.8cm*5.8cm.(3)A dynamic ECG detection algorithm is designed.The algorithm can detect the dynamic ECG signal,and can also operate with low power consumption in the case of high accuracy.(4)The analysis method based on HRV is designed,and the analysis model is proposed,and the recognition rate of the sleep stages with discrimination ability is reduced,the complexity and resource requirements of the network are reduced,and the recognition rate can reach 93.33%.(5)In order to reduce the complexity of the neural network,the PCA is used to design the real-time monitoring of myocardial ischemia.In addition,the structure of the neural network analysis,the final recognition rate reached 91.4%.