Non-invasive cuff-less blood pressure estimation using a hybrid deep learning model

Yang, Sen; Zhang, Yaping; Cho, Siu-Yeung; Correia, Ricardo; Morgan, Stephen P.

Abstract

Conventional blood pressure (BP) measurement methods have different drawbacks such as being invasive, cuff-based or requiring manual operations. There is significant interest in the development of non-invasive, cuff-less and continual BP measurement based on physiological measurement. However, in these methods, extracting features from signals is challenging in the presence of noise or signal distortion. When using machine learning, errors in feature extraction result in errors in BP estimation, therefore, this study explores the use of raw signals as a direct input to a deep learning model. To enable comparison with the traditional machine learning models which use features from the photoplethysmogram and electrocardiogram, a hybrid deep learning model that utilises both raw signals and physical characteristics (age, height, weight and gender) is developed. This hybrid model performs best in terms of both diastolic BP (DBP) and systolic BP (SBP) with the mean absolute error being 3.23 ± 4.75 mmHg and 4.43 ± 6.09 mmHg respectively. DBP and SBP meet the Grade A and Grade B performance requirements of the British Hypertension Society respectively.

Citation

Yang, S., Zhang, Y., Cho, S.-Y., Correia, R., & Morgan, S. P. (2021). Non-invasive cuff-less blood pressure estimation using a hybrid deep learning model. Optical and Quantum Electronics, 53(2), Article 93. https://doi.org/10.1007/s11082-020-02667-0

• Journal Article Type: Article
• Acceptance Date: Dec 14, 2020
• Online Publication Date: Jan 25, 2021
• Publication Date: 2021-02
• Deposit Date: Jan 7, 2021
• Publicly Available Date: Jan 25, 2021
• Journal: Optical and Quantum Electronics
• Print ISSN: 0306-8919
• Electronic ISSN: 1572-817X
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 53
• Issue: 2
• Article Number: 93
• DOI: https://doi.org/10.1007/s11082-020-02667-0
• Keywords: blood pressure (BP); cuff-less; photoplethysmogram (PPG); electrocardiogram (ECG); deep learning
• Public URL: https://nottingham-repository.worktribe.com/output/5204091
• Publisher URL: https://link.springer.com/article/10.1007/s11082-020-02667-0