Continuous Cuffless Blood Pressure Estimation via Effective and Efficient Broad Learning Model

Hypertension is a critical cardiovascular risk factor, underscoring the necessity of accessible blood pressure (BP) monitoring for its prevention, detection, and management. While cuffless BP estimation using wearable cardiovascular signals via deep learning models (DLMs) offers a promising solution, their implementation often entails high computational costs. This study addresses these challenges by proposing an end-to-end broad learning model (BLM) for efficient cuffless BP estimation. Unlike DLMs that prioritize network depth, the BLM increases network width, thereby reducing computational complexity and enhancing training efficiency for continuous BP estimation. An incremental learning mode is also explored to provide high memory efficiency and flexibility. Validation on the University of California Irvine (UCI) database (403.67 hours) demonstrated that the standard BLM (SBLM) achieved a mean absolute error (MAE) of 11.72 mmHg for arterial BP (ABP) waveform estimation, performance comparable to DLMs such as long short-term memory (LSTM) and the one-dimensional convolutional neural network (1D-CNN), while improving training efficiency by 25.20 times. The incremental BLM (IBLM) offered horizontal scalability by expanding through node addition in a single layer, maintaining predictive performance while reducing storage demands through support for incremental learning with streaming or partial datasets. For systolic and diastolic BP prediction, the SBLM achieved MAEs (mean error $\pm$ standard deviation) of 3.04 mmHg (2.85 $\pm$ 4.15 mmHg) and 2.57 mmHg (-2.47 $\pm$ 3.03 mmHg), respectively. This study highlights the potential of BLM for personalized, real-time, continuous cuffless BP monitoring, presenting a practical solution for healthcare applications.