Identity information based on human magnetocardiography signals
This provides a potential tool for personalized healthcare management by enabling identity verification based on MCG signals.
The paper tackled individual identification using magnetocardiography (MCG) signals, achieving 97.04% accuracy with a system that processes signals into time-frequency matrices and uses a CNN for classification.
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.