Objective To solve the problems of insufficient parameter quantity in the feature extraction stage of pulse signals in one-dimensional time series as well as missing inverse operations and blurry relationship between data and time series when con-verting one-dimensional signals into two-dimensional sequence images.Methods It proposed a one-dimensional pulse signal multi period data classification method that combined Gram and angle field(GASF)based on non segmented aggregation approxi-mation(PAA)and deep learning network models.Firstly,the one-dimensional pulse signal was converted into a two-dimen-sional temporal image through GASF encoding and then input into a neural network(TCPNet)for training and classification.It set 4273 segments of multi cycle pulse signals of the same length as the input dataset.Results This study found that the network accuracy using non segmented aggregation approximation for GASF processing was not less than 89％.The highest accuracy of the model reached 93.61％,with an accuracy of 93.63％,an F1 score of 93.60％and a recall rate of 93.61％.Conclusions The accuracy of the pulse classification model established based on the method of this paper is significantly improved,which forcefully proves the effectiveness of the classification method of this paper,and also provides a new idea and method for the classification problem of pulse signals.
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