Deep learning-based beat-to-beat arterial blood pressure estimation using distant radar signals

Maintaining constant vigilance over arterial blood pressure (ABP) is crucial for diagnosing hypertension and other critical cardiovascular diseases. While traditional cuff-based approaches are non-invasive, they have limitations in providing continuous blood pressure monitoring. In contrast, complex ABP monitoring systems, while accurate, are primarily suitable for clinical settings due to their intrusive nature. This study introduces a groundbreaking method for generating arterial blood pressure (ABP) waveforms using remote radar signals and deep learning (DL) techniques. This approach eliminates the need for invasive procedures, wearable biosensors, and costly equipment typically associated with ABP recording. We introduce MultiResLinkNet, a segmentation model based on a one-dimensional convolutional neural network (1D CNN), specifically designed to synthesize arterial blood pressure (ABP) directly from raw radar waveforms. We trained and evaluated the end-to-end DL framework using a publicly available benchmark radar dataset containing raw radar data and corresponding physiological signals from 30 subjects across various scenarios, including Resting, Valsalva, Apnea, Tilt-up, and Tilt-down. The proposed MultiResLinkNet excelled in ABP segmentation, outperforming state-of-the-art networks in combined and individual scenarios, and produced the best average temporal and spectral correlations as well as the lowest temporal and spectral errors in nearly all scenarios’ data. Furthermore, qualitative evaluation demonstrated a strong resemblance between the synthesized and ground truth ABP waveforms. Our novel approach enables remote monitoring of critical patients continuously, especially those undergoing surgery, by predicting ABP waveforms from non-contact radar signals. This breakthrough offers significant advantages, facilitating continuous ABP monitoring without the need for invasive procedures or cumbersome wearable sensors.