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.

我们基于磁心电图（MCG）信号开发了一个个体识别系统，利用光泵磁强计（OPMs）捕获的信号。我们的系统使用模式识别分析不同身体位置上获取的信号，通过扫描由 MCG 信号构成的矩阵，并使用 2*2 窗口。为了利用 MCG 信号的空间信息，我们将相邻小区域的信号转换为数据集的四个通道。我们进一步利用小波变换将数据转换为时间 - 频率矩阵，并采用卷积神经网络（CNN）进行分类。结果表明，我们的系统在个体识别方面的准确率达到 97.04％。这一发现表明了 MCG 信号在个体识别系统中具有潜力，为个性化医疗管理提供了有价值的工具。