基于机器学习的心律失常信号分类算法研究.

Based on the data files provided by the MIT-BIH, this paper extracted the characteristic information of ECG signals by wavelet transform, and studied the classification and recognition of common signals. This paper mainly designed and implemented three classification algorithms based on softmax regression and neural network. Simulation experiments show that the training speed of a suitable neural ne twork algorithm is faster. With fewer iterations, the accuracy rate of classification recognition is more than 90%. [ABSTRACT FROM AUTHOR]