Research On Computer Automatic Analysis Algorithm And System Software Of ECG Signal

With the continuous development of China's economy and the increasing of people's living standards,more and more people are facing cardiovascular diseases.Timely and accurate detection of arrhythmia and other disease signals is of great significance in preventing heart disease and sudden cardiac death.ECG signals can reflect the heart's active function.Correct analysis of ECG signals is of great significance for the diagnosis and prevention of diseases.Although most disease signals are obvious,artificial identification is not difficult,too many cases can lead to too much tedious and repetitive work for doctors.With the development of computer technology,the automatic analysis of ECG signals by computer can reduce the work intensity of doctors and improve the detection rate of heart diseases.ECG signals are generally very weak,usually at millivolt or even microvolt level,and are easily affected and interfered by the surrounding environent.In order to maintain and extract useful information of ECG signal and filter out noise,many researchers are devoted to designing optimal data preprocessing methods.Among many heart diseases,atrial fibrillation(AF)is a relatively common persistent arrhythmia disease,and long-term atrial fibrillation has a very serious impact on human health.Studies have shown that long-term atrial fibrillation has a significant correlation with coronary heart disease,hypertension and heart failure.Early diagnosis and timely treatment of atrial fibrillation are of great significance to human health.As a persistent heart disease,atrial fibrillation can be detected by electrocardiogram.Atrial fibrillation recognition based on the classical theory development of atrial fibrillation in ecg signal automatic identification algorithm is proposed,and based on the problems existing in the identification results of the algorithm was improved,through the recognition of 55 cases of ecg signal,the original ecg types for af ecg signal is identified as atrial fibrillation increased from 8 cases of ecg signals to the 23 cases,accuracy from 24.2% to 69.7%.It can be seen from the above data that,through the improvement of af identification algorithm,relatively accurate af identification is achieved,which has certain reference value for the identification of ecg in clinical atrial fibrillation.In this study,C++ language has been used for programming to realize the processing,analysis and feature extraction of ECG signals.By loading disease signal features,disease analysis can be realized,providing reference for clinical,community hospital and family heart disease diagnosis.