Research And Application Of Machine Learning In ECG Data Analysis

Nowadays,with the wide popularization of national fitness,the topic of sports safety has been widely mentioned.Both professional athletes and the general public need to have a comprehensive understanding of their own physical conditions after exercise to ensure that there will be no physical injury caused by excessive exercise.Similarly,monitoring the physical conditions can make appropriate training plans for themselves and improve the quality of exercise.Electrocardiogram is a very important part of various physiological indicators in human body.Because abnormal ECG data is likely to lead to sudden death and other types of heart diseases,it is particularly important to monitor ECG data and analyze the types of abnormalities.In this paper,the technical and theoretical knowledge of ecg signal anomaly type detection is deeply studied,and the relevant steps in the process of anomaly detection are optimized.The automatic detection of ecg signal anomaly type is realized by combining machine learning and signal analysis.Based on this algorithm,a management system for monitoring and analyzing athletes' physical condition in marathon race is implemented.The main content and innovation points of this paper are as follows:Firstly,in this paper,the wavelet decomposition method is used to de-noising the signals.Since the noise signals are distributed in different wavelet decomposition layers,the combined threshold method is used to de-noising the wavelet coefficients of different layers to improve the de-noising effect.Secondly,in the waveform detection of ecg data,the difference threshold method has low robustness and is prone to noise interference.Therefore,this paper proposes the difference method based on dynamic time window and dynamic threshold to segment the beat to reduce the false detection rate and the missed detection rate.Thirdly,because the frequency domain characteristics of ecg signals are difficult to express their non-linear characteristics,a method based on local mean value decomposition and sample entropy is proposed to extract the frequency domain characteristics of ECG signals.Forthly,in this paper,SVM algorithm was used to classify the processed ecg signals,and swarm algornthm was used to optimize the parameters.Based on this algorithm,a marathon management system is developed.