Cuff-Less Blood Pressure Estimation Using Only the ECG Signal in Frequency Domain

Mousavi, Seyedeh Somayyeh; Hemmati, Mohammad; Charmi, Mostafa; Moghadam, Maryam; Firouzmand, Mohammad; Ghorbani, Yadollah

doi:10.1109/iccke.2018.8566583

Cited by 12 publications

(7 citation statements)

References 14 publications

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“…This method usually requires two physiological signals, such as electrocardiogram (ECG) and PPG signals. This approach has been explored by several past studies (for example, [ 7 , 8 , 9 ]) which verified the feasibility of the solution.…”

Section: Introductionmentioning

confidence: 90%

“…There are so many established regression methods, such as support vector machines (SVM), linear regression, regression trees, model trees, ensemble of trees, and random forest [ 9 , 10 ]. The regression models for BP estimation involve the unknown parameters of the model denoted as α (algorithm-specific), the independent variables X (features from PPG or ECG) and the dependent variable Y (either SBP or DBP).…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Cuffless Continuous Blood Pressure Estimation Using Deep Learning Model

Harfiya

Purwandari

et al. 2020

Sensors

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Blood pressure monitoring is one avenue to monitor people’s health conditions. Early detection of abnormal blood pressure can help patients to get early treatment and reduce mortality associated with cardiovascular diseases. Therefore, it is very valuable to have a mechanism to perform real-time monitoring for blood pressure changes in patients. In this paper, we propose deep learning regression models using an electrocardiogram (ECG) and photoplethysmogram (PPG) for the real-time estimation of systolic blood pressure (SBP) and diastolic blood pressure (DBP) values. We use a bidirectional layer of long short-term memory (LSTM) as the first layer and add a residual connection inside each of the following layers of the LSTMs. We also perform experiments to compare the performance between the traditional machine learning methods, another existing deep learning model, and the proposed deep learning models using the dataset of Physionet’s multiparameter intelligent monitoring in intensive care II (MIMIC II) as the source of ECG and PPG signals as well as the arterial blood pressure (ABP) signal. The results show that the proposed model outperforms the existing methods and is able to achieve accurate estimation which is promising in order to be applied in clinical practice effectively.

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Section: Introductionmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Real-Time Cuffless Continuous Blood Pressure Estimation Using Deep Learning Model

Harfiya

Purwandari

et al. 2020

Sensors

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show abstract

“…Notwithstanding the effectiveness of using PPG signal for BP estimation, motion artefacts are often found diminishing the signal quality which caused feature extraction failure. Several studies [ 17 , 18 , 19 ] have attempted to use the raw signal and discard feature engineering. The raw signal is preprocessed and segmented for some defined time length.…”

Section: Introductionmentioning

confidence: 99%

“…Subsequently, the signal segments are fed as inputs of the model. Several machine learning regressor models, namely decision tree, support vector regression, adaptive boosting, and random forest are being compared in [ 18 ] to predict SBP, DBP, and mean arterial blood pressure (MAP) values. Convolutional layers and recurrent layer of GRU are applied to build an end-to-end neural network model in [ 19 ] to estimate SBP and DBP values.…”

Section: Introductionmentioning

confidence: 99%

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

Harfiya

Chang

2021

Sensors

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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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“…In some researches, using only the ECG signal for blood pressure estimation has been investigated. For example, in [17], after extracting the R peaks in the ECG signal, the Fourier transform was applied to the signal between two R peaks to extract whole based frequency domain features.…”

Section: Introductionmentioning

confidence: 99%

Poincaré’s Section Analysis of Photoplethysmography Signals for Cuff-Less Non-Invasive Blood Pressure Measurement

Shoeibi¹,

Aghdam²,

Ebrahimi³

2021

Preprint

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Background and Objective: Hypertension is a serious problem that has become dramatically more common in recent decades. Hypertension can be managed in its early stages by regular monitoring of blood pressure. Blood pressure, as a vital signal, has an essential role in the prediction of many cardiovascular diseases. Therefore, non-invasive, cuff-less, continuous monitoring of blood pressure has special importance in personal health care. Recently, due to the capabilities of PPG sensors in embedding and compacting as a wearable device, application of the PPG signal and its characteristics as a useful facilities for BP measurement have been highlighted. Methods: This study attempts to provide a new indicator of PPG waveforms to help the rapid developments in this research area. The proof of the feasibility of using Poincaré’s section for extracting the profitable features of the PPG signal for BP estimation is one of the key achievements of this paper. Results: The performance of the method was evaluated on 101 subject’s clinical data from the MIMIC II database. The proposed method obtains a mean absolute error of 2.1 mmHg for systolic pressure and 1.4 mmHg for diastolic pressure prediction. Also, the results meet the AAMI and BHS standards, which demonstrate the feasibility of Poincaré’s section-based indices in BP estimation. Conclusions: The results confirm the proficiency of this method in the blood pressure estimation and a straightforward way to reduce the computational and hardware complexity, which in turn helps to achieve a real-time wearable BP monitoring system.

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Cuff-Less Blood Pressure Estimation Using Only the ECG Signal in Frequency Domain

Cited by 12 publications

References 14 publications

Real-Time Cuffless Continuous Blood Pressure Estimation Using Deep Learning Model

Real-Time Cuffless Continuous Blood Pressure Estimation Using Deep Learning Model

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

Poincaré’s Section Analysis of Photoplethysmography Signals for Cuff-Less Non-Invasive Blood Pressure Measurement

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