Dynamic Beat-to-Beat Measurements of Blood Pressure Using Multimodal Physiological Signals and a Hybrid CNN-LSTM Model

Abstract

Wearable cuffless blood pressure (BP) technology is emerging as a critical tool for monitoring hypertension, the leading risk factor of most cardiovascular diseases. However, current cuffless BP methods are not accurate enough for clinical use, because they mainly use single or dual modalities/features as inputs for estimation. To address this challenge, we propose multimodal McBP-Net, built with hybrid CNN-LSTM architecture combing two-layer convolution operations with four-layer LSTMs to capture both local signal features and temporal dependencies for continuous dynamic beat-to-beat BP estimation. The McBP-Net includes photoplethysmographic, electrocardiographic, impedanceplethysmographic (IPG), and skin temperature (ST) signals as inputs. Validated on 23 subjects undergoing cold pressor test to induce large BP variability, the McBP-Net achieves the mean absolute errors of 4.19 and 2.98 mmHg for systolic BP (SBP) and diastolic BP (DBP), respectively, which fall within the accuracy range required by the Grade A of IEEE standard. The integration of four multimodal signals improves performance by 16.20%, 37.37%, and 49.52% over three-, dual-, and single-modality approaches, respectively, with significant contributions from IPG and ST signals. Notably, ST shows a strong nonlinear relationship with BP with high mutual information of 0.9056 for SBP. Furthermore, McBP-Net achieves a reasonable balance between accuracy and computational efficiency, offering inference speed of 36.7% faster and reducing computational demands by 78% compared to transformer-based models tested. Importantly, it maintains robust performance, with only a 0.21 mmHg degradation in dynamic SBP estimation when trained on rest-stage data. McBP-Net demonstrates promising potential in medical-grade wearable cuffless dynamic BP measurements.