BP-STFNet: A Hybrid Time-Frequency Domain Neural Network for Blood Pressure Estimation from Multi-channel BCG Signals

Abstract

Continuous blood pressure (BP) monitoring is crucial for cardiovascular care but is often constrained by financial concerns. Continuous BP estimation based on ballistocardiogram (BCG) signals offers a non-invasive and cost-effective alternative. Traditional approaches to BP estimation from BCG signals have been hampered by single-channel noise interference, high costs of multi-signal acquisition, and insufficient feature capture. This paper introduces BP-STFNet, a novel deep learning framework that transcends these limitations by leveraging a unique fusion of time-frequency domain information. BP-STFNet enhances input signal quality and ensures robustness against physical movement artifacts. Our custom-designed parallel gated dilated convolution architecture, along with Squeeze-and-Excitation ResNet and Bidirectional Gated Recurrent Unit (Bi-GRU) module, extract detailed features and capture dynamic temporal patterns. The framework achieves a Mean Absolute Error (MAE) of 4.08 mmHg for systolic and 2.12 mmHg for diastolic pressure measurements, demonstrating highly competitive results for systolic pressure estimation and state-of-the-art performance for diastolic pressure prediction. The promising results achieved with BP-STFNet suggest its potential as a viable solution for both continuous and real-time BP monitoring, paving the way for improved cardiovascular health management.