Non-contact ECG reconstruction algorithm based on millimeter wave radar

Abstract

With the wide application of millimeter-wave radar signals in medical monitoring, accurately mapping these signals to ECG signals has become a key challenge in meeting the needs for daily continuous non-contact ECG monitoring. The signal processing flow of millimeter-wave radar was introduced in detail, the fine-grained mapping relationship between radar signals and ECG signals was explored, and the nonlinear transformation from radar signals to electrocardiograms was achieved through the introduction of the CAE-BiLSTM deep learning network, which was a hybrid of a convolutional autoencoder (CAE) and bi-directional long short-term memory (BiLSTM), incorporating the convolutional block attention module (CBAM).The results show that the median morphological accuracy of the proposed method is 0.92, and the feature peak prediction error is less than 50 ms. The proposed approach significantly enhances the mapping relationship between radar and ECG signals and offers a new idea for generating non-contact ECG signals.

Keywords

• millimeter wave radar
• non-contact
• ECG
• vital sign monitoring