Research And Implementation Of ECG Signals Processing For Intelligent Terminal

With the rapid development of the economy, people become more and more concerned about people's living and health problems. The progress of technology and the social development will promote the reform of the medical field. Compared with traditional medical equipment, portable mobile medical equipment is more favored and attended by people. Portable mobile medical equipment has characteristics with intelligence, miniaturization, simplly operation and so on, which can not only provide timely, appropriate, borderless services, but also effectively ease the current limited medical resources. What's more, it can effectively alleviate the lack of medical resources. With the usage on the remote dynamic ECG terminals, features for the algorithm of high accuracy, high real-time performance and easy realization is needed. This thesis focuses on the de-noising algorithm and R wave detection of ECG signals, and putting forward a new de-noising and R wave detection algorithm. This thesis mainly completes the following work:1. Analyzing the standard ECG database, and this thesis determines to use the MIT-BIH database.2. Based on the traditional ECG signal de-noising algorithm of hard threshold, soft threshold and compromise threshold, this thesis puts forward a new nonlinear power threshold de-noising method. Choosing No.100 and No.103 different ECG signals from the MIT-BIH database to analyze the algorithm performance. Under the Matlab software, the experiments show the new nonlinear power threshold de-noising method is obviously superior to the traditional. It has lower computation complexity and easy to be implemented, which is suitable for the analysis of ECG signal intelligent terminal.3. Based on the traditional R wave differential threshold detection algorithm, the improved algorithm is proposed, which is improved on the starting point position and time window function. Through the Matlab simulation, the results show that the 15 different ECG signals are chosen from the MIT-BIH database in 30 minutes, R wave recognition can reach more than 99.69% and 5 minutes prediction accuracy of the observed values can be 100%.4. According to the above algorithms and analytical methods, this thesis completes the design on the software and the intelligent terminal. The software can achieve the real-time data collecting, and the database system of software is designed, which can save all kinds of the information and data. Large test results show that under the Matlab simulation and intelligent terminal system(Android) environment, the R wave detection almost have same location. It is proved that the improved algorithm is effective and feasible. Through a large number of real-time ECG signal acquisition and verification, showing that the software has a perfect performance.