Open AccessResearch on Identification Algorithm Based on PPG Signal and Improved Convolutional Neural Network
Author(s) -
Dechao Wan,
Luqiao Zhang,
Yunxiang Bai,
Haoyuan Deng
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/428/1/012017
Subject(s) - computer science , convolutional neural network , identification (biology) , pattern recognition (psychology) , artificial intelligence , wavelet transform , reliability (semiconductor) , artificial neural network , signal (programming language) , wavelet , power (physics) , botany , physics , quantum mechanics , biology , programming language
In this paper, an improved convolutional neural network model is proposed, which is used for the identification of ECG signals while PPG signals are rarely used. The PPG data from the mimic database is denoised by wavelet transform, and then directly sent to the model for automatic sign extraction and recognition. This method can achieve the highest recognition rate of 99.39% on PPG data in mimic database. This makes the identification based on PPG signal meet the requirements of high reliability. With the rapid development of sensors and wearable devices, this research can also meet the universality of one of the requirements of identification.