Identity information based on human magnetocardiography signals
Pengju Zhang Chenxi Sun Jianwei Zhang Hong Guo
We have developed an individual identification system based on
magnetocardiography (MCG) signals captured using optically pumped magnetometers
(OPMs). Our system utilizes pattern recognition to analyze the signals obtained
at different positions on the body, by scanning the matrices composed of MCG
signals with a 2*2 window. In order to make use of the spatial information of
MCG signals, we transform the signals from adjacent small areas into four
channels of a dataset. We further transform the data into time-frequency
matrices using wavelet transforms and employ a convolutional neural network
(CNN) for classification. As a result, our system achieves an accuracy rate of
97.04% in identifying individuals. This finding indicates that the MCG signal
holds potential for use in individual identification systems, offering a
valuable tool for personalized healthcare management.