Research On Arrhythmia Analysis Algorithm Of Portable ECG Detection Equipment

Cardiovascular disease is a major threat to human health.Traditional large-scale ECG detection devices have been unable to meet people's needs because of their large size,high price,and complex detection procedures.In recent years,as people pay more attention to health,more and more people have begun to use portable ECG detection equipment to care for their heart health.However,most portable ECG detectors(such as wristbands and metal electrode ECG detectors)can only detect heart rate,cannot perform waveform analysis,disease diagnosis,etc.,affecting the user experience.Combined with the development of portable ECG detection equipment,this paper studies an arrhythmia analysis algorithm that is suitable for use in portable ECG detection equipment.It only needs to locate the five feature points of the ECG waveform to analyze several common arrhythmia diseases.This article has further developed an APP that can manage the user's ECG information.It can receive the ECG signal transmitted by the portable ECG detector via Bluetooth in real time,and display the results of ECG signal analysis,and then save the ECG signal.After experimental verification can initially meet the needs of home users.This paper main work is as follows.(1)From the aspects of ECG preprocessing,QRS detection,and arrhythmia analysis,the domestic and international research status of ECG automatic analysis technology in recent years has been summarized,and research status of portable ECG detection equipment has been studied.Research status of portable ECG detection equipment and architecture and advantages of Android platform and technology are studied.(2)Algorithm:For ECG signal preprocessing,this paper compares commonly used ECG preprocessing algorithms,and ultimately selects Butterworth low-pass filter to filter out EMG signals,50 Hz notch filter to suppress power frequency interference,and correcting of baseline drift with FIR high-pass filter.In the aspect of feature point localization,the commonly used R-wave detection method based on differential threshold and R-wave detection algorithm based on wavelet transform are studied.Considering the real-time requirement of the detection signal of portable ECG equipment,the improved threshold method is used to detect QRS wave.In addition,a modular maxima pair detection algorithm based on the window search method is designed to detect P-waves and T-waves.This paper studies the existing ECG detection algorithms and proposes a SVM classification algorithm based on the combination of logic branch discrimination method and quadratic polynomial kernel function.The algorithm has the advantages of small calculation,fast operation,etc.It is suitable for the application of portable ECG detection equipment.(3)Software design: The software design and writing of the portable ECG detection device under the Android platform is realized.Real-time display of heart rate and ECG analysis results can be realized,and SQLite database is designed to save user information and test data.After experimental verification by the human body and simulator group,this software can realize real-time heart rate,arrhythmia analysis,data storage and management functions,and can initially meet the needs of home users.