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Energy-efficient Blood Pressure Monitoring based on Single-site Photoplethysmogram on Wearable Devices
Summary
This study presents an accurate, energy-efficient cuffless blood pressure monitoring (BPM) system using a single Photoplethysmographic (PPG) sensor and Artificial Neural Network (ANN) on edge devices. The novel approach enhances accuracy by optimizing signal preprocessing for continuous BPM readings.
Area of Science:
- Biomedical Engineering
- Artificial Intelligence in Healthcare
- Wearable Technology
Background:
- Traditional blood pressure monitoring (BPM) often relies on invasive cuff-based methods.
- There is a growing need for continuous, cuffless, and energy-efficient BPM solutions for wearable devices.
- Existing cuffless BPM methods using Photoplethysmography (PPG) face challenges in accuracy and real-time edge implementation.
Purpose of the Study:
- To develop an accurate and energy-efficient cuffless blood pressure monitoring (BPM) system.
- To leverage Artificial Neural Networks (ANN) for processing single-site PPG signals on edge wearable devices.
- To improve BPM accuracy through advanced signal preprocessing techniques.
Main Methods:
- Utilized a single-site Photoplethysmographic (PPG) sensor to capture continuous physiological data.
- Implemented an Artificial Neural Network (ANN) model for analyzing preprocessed PPG signals.
- Employed outlier removal and whole-signal based inputs for enhanced accuracy compared to parameter-based methods.
Main Results:
- Achieved competitive accuracy with Mean Absolute Error (MAE) ± Root Mean Square Deviation (RMSD) of 3.42 ± 5.42 mmHg for systolic BP (SBP), 1.92 ± 3.29 mmHg for diastolic BP (DBP), and 2.21 ± 3.50 mmHg for mean arterial BP (MAP).
- Demonstrated an energy-efficient edge computing solution with an average energy consumption of 2.1 mJ per reading.
- Real-time hardware evaluation showed processing time of 42.2 ms and memory usage of 18.2 KB RAM per reading.
Conclusions:
- The proposed single-site PPG-based BPM system offers a highly accurate and energy-efficient solution for cuffless monitoring on edge devices.
- This represents a novel advancement in edge computing artificial intelligence for continuous blood pressure assessment.
- The method's performance and resource efficiency make it suitable for integration into next-generation wearable health trackers.
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