BPINet: Synchronous blood pressure estimation and user authentication based on ECG and PPG signal with multi-task learning

As a vital indicator of health, blood pressure is particularly important for elderly individuals with chronic illnesses who live alone. Daily monitoring is essential to prevent hypertension and related complications. Nevertheless, most current home blood pressure monitors depend on cuff-based techniques, which can produce inaccurate results through improper use or cuff placement. Moreover, these devices generally cannot identify the specific user being measured, which hinders the development of personalized long-term health monitoring reports. In this paper, we propose BPINet, a multi-task model based on the Multi-gate Mixture-of-Experts (MMoE) framework that utilizes CNN-BiLSTM to extract features from ECG/PPG signals for simultaneous blood pressure estimation and user authentication (identity recognition). We also compile a dataset of ECG/PPG signals from multiple families, along with their blood pressure measurements, and incorporate it with the University of Queensland Vital Signs Dataset (UQVS) to evaluate the performance of BPINet. On the UQVS dataset, BPINet achieves a 97.54% user identity recognition accuracy. For systolic blood pressure (SBP) estimation, BPINet yields an MAE ± STD of 3.317 ± 5.771 mmHg. For diastolic blood pressure (DBP) estimation, the corresponding values are 2.444 ± 4.147 mmHg. On our customized dataset, BPINet achieves a 94.30% user identity recognition accuracy. For SBP estimation, it yields an MAE ± STD of 2.940 ± 4.753 mmHg. These results meet both the British Hypertension Society (BHS) Grade A standard and the Association for the Advancement of Medical Instrumentation (AAMI) standard. BPINet not only performs blood pressure estimation effectively but also enables simultaneous user identity recognition, facilitating the creation of personalized health records. The experimental results demonstrate the clinical feasibility and effectiveness of our proposed scheme.