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

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.

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A Continuous Blood Pressure Estimation Method Using Photoplethysmography by GRNN-Based Model

Sensors ◽

10.3390/s21217207 ◽

2021 ◽

Vol 21 (21) ◽

pp. 7207

Author(s):

Zheming Li ◽

Wei He

Keyword(s):

Blood Pressure ◽

Estimation Method ◽

Absolute Error ◽

Disease Diagnosis ◽

Generalized Regression Neural Network ◽

British Hypertension Society ◽

Proposed Model ◽

The Mean ◽

Blood Pressure Estimation ◽

The Time Domain

Compared with diastolic blood pressure (DBP) and systolic blood pressure (SBP), the blood pressure (BP) waveform contains richer physiological information that can be used for disease diagnosis. However, most models based on photoplethysmogram (PPG) signals can only estimate SBP and DBP and are susceptible to noise signals. We focus on estimating the BP waveform rather than discrete BP values. We propose a model based on a generalized regression neural network to estimate the BP waveform, SBP and DBP. This model takes the raw PPG signal as input and BP waveform as output. The SBP and DBP are extracted from the estimated BP waveform. In addition, the model contains encoders and decoders, and their role is to be responsible for the conversion between the time domain and frequency domain of the waveform. The prediction results of our model show that the mean absolute error is 3.96 ± 5.36 mmHg for SBP and 2.39 ± 3.28 mmHg for DBP, the root mean square error is 5.54 for SBP and 3.45 for DBP. These results fulfill the Association for the Advancement of Medical Instrumentation (AAMI) standard and obtain grade A according to the British Hypertension Society (BHS) standard. The results show that the proposed model can effectively estimate the BP waveform only using the raw PPG signal.

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

Sensors ◽

10.3390/s21051595 ◽

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.

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

Scientific Reports ◽

10.1038/s41598-021-92997-0 ◽

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.

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

Optical and Quantum Electronics ◽

10.1007/s11082-020-02667-0 ◽

2021 ◽

Vol 53 (2) ◽

Author(s):

Sen Yang ◽

Yaping Zhang ◽

Siu-Yeung Cho ◽

Ricardo Correia ◽

Stephen P. Morgan

Keyword(s):

Machine Learning ◽

Blood Pressure ◽

Deep Learning ◽

Absolute Error ◽

Learning Model ◽

Physiological Measurement ◽

British Hypertension Society ◽

Non Invasive ◽

Blood Pressure Estimation ◽

Deep Learning Model

AbstractConventional 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.

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An Estimation Method of Continuous Non-Invasive Arterial Blood Pressure Waveform Using Photoplethysmography: A U-Net Architecture-Based Approach

Sensors ◽

10.3390/s21051867 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1867

Author(s):

Tasbiraha Athaya ◽

Sunwoong Choi

Keyword(s):

Blood Pressure ◽

Cardiovascular Health ◽

Estimation Method ◽

Absolute Error ◽

British Hypertension Society ◽

Non Invasive ◽

Arterial Blood ◽

Blood Pressure Waveform ◽

The Mean ◽

Mimic Iii

Blood pressure (BP) monitoring has significant importance in the treatment of hypertension and different cardiovascular health diseases. As photoplethysmogram (PPG) signals can be recorded non-invasively, research has been highly conducted to measure BP using PPG recently. In this paper, we propose a U-net deep learning architecture that uses fingertip PPG signal as input to estimate arterial BP (ABP) waveform non-invasively. From this waveform, we have also measured systolic BP (SBP), diastolic BP (DBP), and mean arterial pressure (MAP). The proposed method was evaluated on a subset of 100 subjects from two publicly available databases: MIMIC and MIMIC-III. The predicted ABP waveforms correlated highly with the reference waveforms and we have obtained an average Pearson’s correlation coefficient of 0.993. The mean absolute error is 3.68 ± 4.42 mmHg for SBP, 1.97 ± 2.92 mmHg for DBP, and 2.17 ± 3.06 mmHg for MAP which satisfy the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) standard and obtain grade A according to the British Hypertension Society (BHS) standard. The results show that the proposed method is an efficient process to estimate ABP waveform directly using fingertip PPG.

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Cuffless blood pressure estimation methods: physiological model parameters vs. machine-learned features

Physiological Measurement ◽

10.1088/1361-6579/abeae8 ◽

2021 ◽

Author(s):

Jamal Esmaelpoor ◽

Mohammad Hassan Moradi ◽

Abdolrahim Kadkhodamohammadi

Keyword(s):

Blood Pressure ◽

Physiological Model ◽

Estimation Methods ◽

Model Parameters ◽

Pressure Estimation ◽

Blood Pressure Estimation ◽

Cuffless Blood Pressure ◽

Learned Features

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Study of continuous blood pressure estimation based on pulse transit time, heart rate and photoplethysmography-derived hemodynamic covariates

Australasian Physical & Engineering Sciences in Medicine ◽

10.1007/s13246-018-0637-8 ◽

2018 ◽

Vol 41 (2) ◽

pp. 403-413 ◽

Cited By ~ 9

Author(s):

Jingjie Feng ◽

Zhongyi Huang ◽

Congcong Zhou ◽

Xuesong Ye

Keyword(s):

Blood Pressure ◽

Heart Rate ◽

Transit Time ◽

Pulse Transit Time ◽

Pressure Estimation ◽

Blood Pressure Estimation ◽

Continuous Blood Pressure

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Continuous Blood Pressure Estimation Through Optimized Echo State Networks

Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions - Lecture Notes in Computer Science ◽

10.1007/978-3-030-30493-5_5 ◽

2019 ◽

pp. 48-61

Author(s):

Giuseppe Franco ◽

Luca Cerina ◽

Claudio Gallicchio ◽

Alessio Micheli ◽

Marco Domenico Santambrogio

Keyword(s):

Blood Pressure ◽

Echo State Networks ◽

Pressure Estimation ◽

Blood Pressure Estimation ◽

Continuous Blood Pressure

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

10.21203/rs.3.rs-137994/v1 ◽

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.

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