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

Yuzhong Liu, Hualiang Li, Jianmin Wang, Haochuan Zhang, X. Zheng
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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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基于深度神经网络的异常脉冲信号分类研究
人体脉搏受心脏和血液的影响,携带着反映人体状态的重要信息。深度学习在复杂信号的特征提取方面取得了突破。本文的目的是建立一个深度神经网络,对长时间工作前后采集的脉冲数据进行分类和识别。首先将原始脉冲数据的异常值替换为正常范围内的值,然后将脉冲长度序列30分割为75个连续采样点的短序列。最后,建立深度神经网络模型,输入短脉冲序列,输出相应的生理状态。经过训练,高级神经网络在3200个训练集上的最终分类准确率为0.79,在800个测试集上的最终分类准确率为0.78。对长时间工作前后的脉冲数据进行了有效分类。
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