BM-BPW: A Bidirectional Mamba-based Model for Blood Pressure Waveform Estimation in Arrhythmia

Continuous and non-invasive blood pressure (BP) monitoring is crucial for effective health management in patients with arrhythmia. Cuffless BP measurement methods, utilizing electrocardiogram (ECG) and photoplethysmogram (PPG) signals combined with deep learning, have shown great promise. However, existing deep learning algorithms face challenges in representing physiological data due to the position-sensitivity of such data and the need for global context to accurately interpret it, resulting in limited accuracy. This study introduces BM-BPW, a novel deep learning-based model for continuous BP waveform estimation from ECG and PPG signals for arrhythmia patients. The bidirectional Mamba architecture, combined with position embeddings, is employed in the BM-BPW model to enable global context modeling and locally-aware understanding. A multi-scale convolutional neural network (CNN) feature extractor is also employed to capture the local features. The model was evaluated using a dataset of 36,537 beats collected from 48 arrhythmia patients, with invasive arterial waveforms as the reference. Experimental results indicate that BM-BPW effectively manages BP waveform variability caused by arrhythmia, achieving estimation errors of 0.04 ± 5.03 mmHg and -0.05 3.20 mmHg for systolic BP (SBP), and diastolic BP (DBP),±respectively. These results outperform state-of-the-art methods. This study provides an accurate and efficient solution for cuffless continuous BP waveform monitoring for arrhythmia patients, offering substantial potential for improving clinical management in this population.