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.

scholarly journals Continuous Blood Pressure Estimation Using Exclusively Photopletysmography by LSTM-Based Signal-to-Signal Translation

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2952
Author(s):  
Latifa Nabila Harfiya ◽  
Ching-Chun Chang ◽  
Yung-Hui Li
Keyword(s):  
Blood Pressure ◽  
Absolute Error ◽  
Estimation Methods ◽  
British Hypertension Society ◽  
Arterial Blood ◽  
Proposed Model ◽  
Pressure Estimation ◽  
Effective Signal ◽  
Blood Pressure Estimation ◽  
Continuous Blood Pressure

Monitoring continuous BP signal is an important issue, because blood pressure (BP) varies over days, minutes, or even seconds for short-term cases. Most of photoplethysmography (PPG)-based BP estimation methods are susceptible to noise and only provides systolic blood pressure (SBP) and diastolic blood pressure (DBP) prediction. Here, instead of estimating a discrete value, we focus on different perspectives to estimate the whole waveform of BP. We propose a novel deep learning model to learn how to perform signal-to-signal translation from PPG to arterial blood pressure (ABP). Furthermore, using a raw PPG signal only as the input, the output of the proposed model is a continuous ABP signal. Based on the translated ABP signal, we extract the SBP and DBP values accordingly to ease the comparative evaluation. Our prediction results achieve average absolute error under 5 mmHg, with 70% confidence for SBP and 95% confidence for DBP without complex feature engineering. These results fulfill the standard from Association for the Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS) with grade A. From the results, we believe that our model is applicable and potentially boosts the accuracy of an effective signal-to-signal continuous blood pressure estimation.

scholarly journals Combined deep CNN–LSTM network-based multitasking learning architecture for noninvasive continuous blood pressure estimation using difference in ECG-PPG features

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Da Un Jeong ◽  
Ki Moo Lim
Keyword(s):  
Blood Pressure ◽  
Blood Pressure Measurement ◽  
Patient Specific ◽  
Peak Time ◽  
Simultaneous Prediction ◽  
Deep Cnn ◽  
Pressure Estimation ◽  
The Difference ◽  
Blood Pressure Estimation ◽  
Continuous Blood Pressure

AbstractThe pulse arrival time (PAT), the difference between the R-peak time of electrocardiogram (ECG) signal and the systolic peak of photoplethysmography (PPG) signal, is an indicator that enables noninvasive and continuous blood pressure estimation. However, it is difficult to accurately measure PAT from ECG and PPG signals because they have inconsistent shapes owing to patient-specific physical characteristics, pathological conditions, and movements. Accordingly, complex preprocessing is required to estimate blood pressure based on PAT. In this paper, as an alternative solution, we propose a noninvasive continuous algorithm using the difference between ECG and PPG as a new feature that can include PAT information. The proposed algorithm is a deep CNN–LSTM-based multitasking machine learning model that outputs simultaneous prediction results of systolic (SBP) and diastolic blood pressures (DBP). We used a total of 48 patients on the PhysioNet website by splitting them into 38 patients for training and 10 patients for testing. The prediction accuracies of SBP and DBP were 0.0 ± 1.6 mmHg and 0.2 ± 1.3 mmHg, respectively. Even though the proposed model was assessed with only 10 patients, this result was satisfied with three guidelines, which are the BHS, AAMI, and IEEE standards for blood pressure measurement devices.

scholarly journals Combined Deep CNN–LSTM Network-based Multitasking Learning Architecture for Noninvasive Continuous Blood Pressure Estimation using Difference in ECG-PPG Features

2021 ◽  
Author(s):  
Da Un Jeong ◽  
Ki Moo Lim
Keyword(s):  
Blood Pressure ◽  
Pulse Transit Time ◽  
Patient Specific ◽  
Peak Time ◽  
Simultaneous Prediction ◽  
Deep Cnn ◽  
Pressure Estimation ◽  
The Difference ◽  
Blood Pressure Estimation ◽  
Continuous Blood Pressure

Abstract The pulse transit time (PTT), which is the difference between the R-peak time of the electrocardiogram (ECG) signal and the systolic peak of the photoplethysmography (PPG) signal, is an indicator that enables noninvasive and continuous blood pressure estimation. However, it is difficult to accurately measure the PTT from the ECG and PPG signals because they have inconsistent shapes owing to patient-specific physical characteristics, pathological conditions, and movements. Accordingly, complex preprocessing is required to estimate blood pressure based on PTT. In this paper, as an alternative solution, we propose a noninvasive continuous algorithm using the difference between the ECG and PPG as a new feature that can include PTT information. The proposed algorithm is a deep CNN–LSTM-based multitasking machine learning model that outputs simultaneous prediction results of systolic (SBP) and diastolic blood pressures (DBP). The prediction accuracies of SBP and DBP using the proposed model were 0.017±1.624 mmHg and 0.164±1.297 mmHg, respectively. This result corresponded to Grade A according to the BHS and AAMI standards, which are the validation standards for blood pressure measuring devices.

New photoplethysmogram indicators for improving cuffless and continuous blood pressure estimation accuracy

2018 ◽  
Vol 39 (2) ◽  
pp. 025005 ◽  
Author(s):  
Wan-Hua Lin ◽  
Hui Wang ◽  
Oluwarotimi Williams Samuel ◽  
Gengxing Liu ◽  
Zhen Huang ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Estimation Accuracy ◽  
Pressure Estimation ◽  
Blood Pressure Estimation ◽  
Continuous Blood Pressure
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