Related Experiment Video
Updated: Jan 16, 2026
Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task
Published on: December 5, 2025
BPINet: Synchronous blood pressure estimation and user authentication based on ECG and PPG signal with multi-task
Xianliang Jiang1, Dingxin Yu2, Guang Jin2
1College of Intelligence and Computing, Tianjin University, Tianjin, 300072, China; Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, 315211, China.
Insights
BPINet uses ECG/PPG signals for accurate blood pressure estimation and user identification. This novel approach overcomes limitations of cuff-based monitors, enabling personalized health monitoring for individuals, especially the elderly.
Area of Science:
- Biomedical Engineering
- Artificial Intelligence in Healthcare
Background:
- Accurate blood pressure monitoring is crucial for managing chronic illnesses, particularly in elderly individuals living alone.
- Cuff-based home blood pressure monitors often suffer from inaccuracies due to improper use and lack user identification capabilities, hindering personalized care.
Purpose of the Study:
- To develop a novel, non-invasive system for simultaneous blood pressure estimation and user authentication using electrocardiogram (ECG) and photoplethysmogram (PPG) signals.
- To address the limitations of existing home blood pressure monitoring devices by enabling personalized health tracking.
Main Methods:
- A multi-task learning model, BPINet, employing a Convolutional Neural Network-Long Short-Term Memory (CNN-BiLSTM) architecture within a Multi-gate Mixture-of-Experts (MMoE) framework was developed.
- ECG and PPG signals were utilized for feature extraction to perform simultaneous blood pressure estimation and user identity recognition.
- A custom dataset of ECG/PPG signals and blood pressure measurements was created and combined with the University of Queensland Vital Signs Dataset (UQVS) for evaluation.
Main Results:
- BPINet achieved high accuracy in user identity recognition, reaching 97.54% on the UQVS dataset and 94.30% on the custom dataset.
- Systolic blood pressure (SBP) estimation yielded mean absolute errors (MAE) of 3.317 ± 5.771 mmHg (UQVS) and 2.940 ± 4.753 mmHg (custom dataset).
- Diastolic blood pressure (DBP) estimation resulted in MAE of 2.444 ± 4.147 mmHg (UQVS), meeting stringent British Hypertension Society (BHS) Grade A and Association for the Advancement of Medical Instrumentation (AAMI) standards.
Conclusions:
- BPINet effectively estimates blood pressure and authenticates users simultaneously, offering a significant advancement over traditional cuff-based methods.
- The system's ability to recognize individual users facilitates the creation of personalized, long-term health monitoring reports.
- The demonstrated clinical feasibility and effectiveness support the adoption of BPINet for improved remote patient monitoring and hypertension management.
Abstract:
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.
More Related Videos
Related Concept Videos
01:19Assessing Blood pressure using a doppler ultrasound
Pre-Procedural Guidelines for Doppler Ultrasound Blood Pressure Assessment:
Preparation of Equipment:
01:18Neural Regulation of Blood Pressure
Baroreceptor Reflex
Baroreceptors, located in the carotid sinuses and aortic arch, detect changes in blood pressure. When blood pressure rises, these stretch-sensitive receptors...
01:30Pulse rhythm
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
01:17Measurement of Blood Pressure
01:25Correlation between ECG and Cardiac Cycle
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...
01:30Equipments Used To Measure Blood Pressure
This invasive approach involves cannulating a peripheral artery. During each cardiac contraction, pressure generates mechanical motion within the catheter, transmitted through rigid, fluid-filled tubing to a transducer. This transducer converts mechanical motion into electrical signals displayed as waveforms on a monitor. An automatic flushing system prevents blood backflow. Due to the potential risk of unexpected arterial blood loss, this method is primarily used in intensive...