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Study on the Classification of Abnormal pulse signals Based on Deep Neural Network

Published: 14 October 2022 Publication History

Abstract

The pulse of human body is affected by heart and blood, and carries important information reflecting the state of human body. Deep learning has made a breakthrough in features extracting for complex signals. The purpose of this paper is to establish a deep neural network to classify and identify the collected pulse data before and after long-time work. Firstly, the abnormal value of the original pulse data is replaced with the value within the normal range, and then the pulse length sequence 30 is divided into a short sequence of 75 consecutive sampling points. Finally, a deep neural network model is established to input short pulse sequence and output the corresponding physiological state. After training, the final classification accuracy of advanced neural network is 0.79 on 3200 training sets and 0.78 on 800 test sets. The pulse data before and after long-time work were effectively classified.

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ICCIR '22: Proceedings of the 2022 2nd International Conference on Control and Intelligent Robotics
June 2022
905 pages
ISBN:9781450397179
DOI:10.1145/3548608
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 14 October 2022

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