Analysis of the ECG data based on a Gaussian mixture model
DOI:
https://doi.org/10.54097/hset.v53i.9712Keywords:
GMM model, entropy weight method, Topsis, wavelet packet transformation, bivariate threshold function.Abstract
With the development of The Times, heart disease has become the first killer of human health, and the initial manifestation of heart disease usually is arrhythmia, so it is particularly important to conduct real-time monitoring and analysis of heart rhythm. In this paper, the data analysis of electrocardiogram. First, we preprocessed the heart rate data to improve the accuracy of the subsequent analysis results. Then, the features of the data values were quantified and clustered using a Gaussian mixture model to determine the plausibility of the model. Next, the various characteristic values of the arrhythmia were compared with the normal data, and the arrhythmia was classified. Finally, the entropy weight method is used to empower the risk degree of different indicators, and to evaluate the model to determine the risk level.
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