Pulse wave signal identification and classification method based on frequency domain double-feature fusion

Abstract

The invention relates to a pulse wave signal identification and classification method based on frequency domain double-feature fusion. The method comprises the following steps: acquiring original pulse wave data of a patient, and firstly carrying out time domain pulse data preprocessing; converting the preprocessed time-domain pulse data into frequency-domain pulse data and then respectively converting the frequency-domain pulse data into a one-dimensional cepstrum coefficient and a two-dimensional recurrence plot; extracting features of the recurrence plot through a Densenet model to obtain two-dimensional image features; extracting features of the cepstrum coefficient through a CNN model to obtain one-dimensional data features; sending the one-dimensional data features and the two-dimensional image features to a feature fusion module for fusion; and sending the fusion features to a classification module for identification and classification. The high-dimensional nonlinear characteristic and the non-periodic characteristic in the pulse signal are fully considered, so that relatively deep information in the pulse characteristic can be obtained; the problem that the classification accuracy of some diseases is low due to the fact that the high-dimensional nonlinear characteristic and the non-periodic characteristic of the pulse signals are not considered in an existing pulse wave signal classification and identification method is solved.

Description

technical field [0001] The invention relates to a recognition technology, in particular to a pulse wave signal recognition and classification method based on frequency-domain dual-feature fusion. Background technique [0002] Pulse diagnosis in traditional Chinese medicine has a history of more than two thousand years. From the perspective of pulse diagnosis in traditional Chinese medicine, and from the analysis of ECG electrocardiogram signals in western medicine, it can be found that the pulse waves of patients with cardiovascular diseases contain rich physiological and pathological information. It can be seen that the analysis of TCM pulse diagnosis can provide practical help for the clinical diagnosis of cardiovascular diseases. [0003] At present, in the pulse signal classification methods, there are many single-feature recognition methods, generally using convolutional neural network, recurrent neural network, etc., have not considered the fusion method of one-dimensi...

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Problems solved by technology

[0003] At present, in the pulse signal classification methods, there are many single-feature recognition methods, generally using convolutional neural network, recurrent neural network, etc., have not considered the fusion method of one-dimensional data and two-dimensional image features to obtain more aperiodic pulse waves Non-linear features, unable to fully mine the deep features of the pulse wave, reducing the accuracy of some cardiovascular disease classification predictions

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