scholarly journals Blood pressure estimation from photoplethysmogram and electrocardiogram signals using machine learning

2018 ◽  
Author(s):  
Sen Yang ◽  
W.S.W. Zaki ◽  
S.P. Morgan ◽  
Siu-Yeung Cho ◽  
R. Correia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Electrocardiogram Signals ◽  
Pressure Estimation ◽  
Blood Pressure Estimation

scholarly journals Enhancement of blood pressure estimation method via machine learning

2021 ◽  
Vol 60 (6) ◽  
pp. 5779-5796
Author(s):  
Nashat Maher ◽  
G.A. Elsheikh ◽  
W.R. Anis ◽  
Tamer Emara
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Estimation Method ◽  
Pressure Estimation ◽  
Blood Pressure Estimation

scholarly journals Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques

Sensors ◽  
2018 ◽  
Vol 18 (4) ◽  
pp. 1160 ◽  
Author(s):  
Monika Simjanoska ◽  
Martin Gjoreski ◽  
Matjaž Gams ◽  
Ana Madevska Bogdanova
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Machine Learning Techniques ◽  
Invasive Blood Pressure ◽  
Non Invasive ◽  
Learning Techniques ◽  
Pressure Estimation ◽  
Blood Pressure Estimation

scholarly journals An Adaptive Weight Learning-Based Multitask Deep Network for Continuous Blood Pressure Estimation Using Electrocardiogram Signals

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1595
Author(s):  
Xiaomao Fan ◽  
Hailiang Wang ◽  
Yang Zhao ◽  
Ye Li ◽  
Kwok Leung Tsui
Keyword(s):  
Blood Pressure ◽  
Short Term Memory ◽  
Blood Pressure Monitoring ◽  
British Hypertension Society ◽  
Electrocardiogram Signals ◽  
Adaptive Weight ◽  
Pressure Estimation ◽  
Blood Pressure Estimation ◽  
Weight Learning ◽  
Continuous Blood Pressure

Estimating blood pressure via combination analysis with electrocardiogram and photoplethysmography signals has attracted growing interest in continuous monitoring patients’ health conditions. However, most wearable/portal monitoring devices generally acquire only one kind of physiological signals due to the consideration of energy cost, device weight and size, etc. In this study, a novel adaptive weight learning-based multitask deep learning framework based on single lead electrocardiogram signals is proposed for continuous blood pressure estimation. Specifically, the proposed method utilizes a 2-layer bidirectional long short-term memory network as the sharing layer, followed by three identical architectures of 2-layer fully connected networks for task-specific blood pressure estimation. To learn the importance of task-specific losses automatically, an adaptive weight learning scheme based on the trend of validation loss is proposed. Extensive experiment results on Physionet Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) II waveform database demonstrate that the proposed method using electrocardiogram signals obtains estimating performance of 0.12±10.83 mmHg, 0.13±5.90 mmHg, and 0.08±6.47 mmHg for systolic blood pressure, diastolic blood pressure, and mean arterial pressure, respectively. It can meet the requirements of the British Hypertension Society standard and US Association of Advancement of Medical Instrumentation standard with a considerable margin. Combined with a wearable/portal electrocardiogram device, the proposed model can be deployed to a healthcare system to provide a long-term continuous blood pressure monitoring service, which would help to reduce the incidence of malignant complications to hypertension.

A novel blood pressure estimation method based on the classification of oscillometric waveforms using machine-learning methods

2020 ◽  
Vol 164 ◽  
pp. 107279 ◽  
Author(s):  
Ahmed S. Alghamdi ◽  
Kemal Polat ◽  
Abdullah Alghoson ◽  
Abdulrahman A. Alshdadi ◽  
Ahmed A. Abd El-Latif
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Estimation Method ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Pressure Estimation ◽  
Blood Pressure Estimation
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