Cuffless blood pressure estimation from electrocardiogram and photoplethysmogram using waveform based ANN-LSTM network

Tanveer, Md. Sayed; Hasan, Md. Kamrul

doi:10.1016/j.bspc.2019.02.028

Cited by 157 publications

(102 citation statements)

References 32 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: 89%

“…Nowadays, most researchers apply deep neural networks because they allow them to have large amounts of labeled input data and be capable of modeling extremely complex and non-linear relationships between inputs and outputs. For BP estimation, the output layer of the network often consists of two neurons, one for SBP and the other for DBP [ 7 , 12 , 13 , 14 ]. The metric used for the performance evaluation of the model was mean absolute error (MAE) [ 7 , 12 , 13 , 14 , 15 ].…”

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

102

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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: 89%

Section: Introductionmentioning

confidence: 99%

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

Harfiya

Purwandari

et al. 2020

Sensors

102

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“…There are several existing approaches that can calculate PWV and, among them, the most widely used one for PWV calculation is pulse wave transit time, commonly referred to as pulse transit time (PTT). The relation between PWV and PTT can be represented as follows [ 7 ]:

where PTT is the time interval between a pulse wave being detected by two sensors and

is the distance between the sensors on the artery. In (1), the elastic modulus

is assumed as a constant when in fact the value of

in the artery is testified to be exponentially escalated with the blood pressure, as follows [ 8 ]:

where

denotes the elastic modulus at 0 mmHg (the unit of blood pressure) and

is a parameter larger than zero that is closely related to arterial stiffness.…”

Section: Introductionmentioning

confidence: 99%

“…The other approach is done by extracting a set of representative time indices, including PTT (p), PTT (d) and PTT (f), as shown in Figure 2 , from the relative location between the PPG and ECG signals [ 9 , 10 ]. However, it is still a very challenging task since the ECG waveform, in particular, has higher variability [ 7 ] and its accuracy is still limited for clinical uses [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Generalized Deep Neural Network Model for Cuffless Blood Pressure Estimation with Photoplethysmogram Signal Only

Hsu

Chang

et al. 2020

Sensors

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Due to the growing public awareness of cardiovascular disease (CVD), blood pressure (BP) estimation models have been developed based on physiological parameters extracted from both electrocardiograms (ECGs) and photoplethysmograms (PPGs). Still, in order to enhance the usability as well as reduce the sensor cost, researchers endeavor to establish a generalized BP estimation model using only PPG signals. In this paper, we propose a deep neural network model capable of extracting 32 features exclusively from PPG signals for BP estimation. The effectiveness and accuracy of our proposed model was evaluated by the root mean square error (RMSE), mean absolute error (MAE), the Association for the Advancement of Medical Instrumentation (AAMI) standard and the British Hypertension Society (BHS) standard. Experimental results showed that the RMSEs in systolic blood pressure (SBP) and diastolic blood pressure (DBP) are 4.643 mmHg and 3.307 mmHg, respectively, across 9000 subjects, with 80.63% of absolute errors among estimated SBP records lower than 5 mmHg and 90.19% of absolute errors among estimated DBP records lower than 5 mmHg. We demonstrated that our proposed model has remarkably high accuracy on the largest BP database found in the literature, which shows its effectiveness compared to some prior works.

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“…This architecture makes it possible to process medical text data which have complex internal relationships, deep learning technology has been widely used in various fields. 15,16 The deep learning technology-based text data classification method replaces the mathematical distance-based traditional clustering method, which greatly improves the performance of the model in processing text data. The key elements of traditional automatic diagnosis method using medical text data are: (1) patient description pathological characteristics, (2) researchers extract features manually based on patient descriptions or patient's EHR, (3) the extracted features are encoded according to the requirements, (4) classification algorithm is used to classify the coded physiological features.…”

Section: Introductionmentioning

confidence: 99%

<p>Deep Learning-Based Method of Diagnosing Hyperlipidemia and Providing Diagnostic Markers Automatically</p>

Liu¹,

Zhang²,

Zhao³

et al. 2020

DMSO

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Introduction: The research of auxiliary diagnosis has always been one of the hotspots in the world. The implementation of auxiliary diagnosis support algorithm for medical text data faces challenges with interpretability and creditability. The improvement of clinical diagnostic techniques means not only the improvement of diagnostic accuracy but also the further study of diagnostic basis. Traditional research methods for diagnostic markers often require a large amount of time and economic costs. Research objects are often dozens of samples, and it is, therefore, difficult to synthesize large amounts of data. Therefore, the comprehensiveness and reliability of traditional methods have yet to be improved. Therefore, the establishment of a model that can automatically diagnose diseases and automatically provide a diagnostic basis at the same time has a positive effect on the improvement of medical diagnostic techniques. Methods: Here, we established an auxiliary diagnostic tool based on attention deep learning algorithm to diagnostic hyperlipemia and automatically predict the corresponding diagnostic markers using hematological parameters. In this paper, we not only demonstrated the ability of the proposed model to automatically diagnose diseases using text-based medical data, such as physiological parameters, but also demonstrated its ability to forecast disease diagnostic markers. Human physiological parameters are used as input to the model, and the doctor's diagnosis results as an output. Through the attention layer, the degree of attention of the model to different physiological parameters can be obtained, that is, the model provides a diagnostic basis. Results: It achieved 94% ACC, 97.48% AUC, 96% sensitivity and 92% specificity with the test dataset. All the above samples are drawn from clinical practice. Moreover, the model predicted the diagnostic markers of hyperlipidemia by the attention mechanism, and the results were fully agreeable to the golden criteria. Discussion: The auxiliary diagnosis system proposed in this paper not only achieves the accurate and robust performance, and can be used for the preliminary diagnosis of patients, but also showing its great potential to discover new diagnostic markers. Therefore, it not only can improve the efficiency of clinical diagnosis but also shorten the research period of researching a diagnosis basis to an extent. It has a positive significance to the development of the medical diagnosis level.

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Cuffless blood pressure estimation from electrocardiogram and photoplethysmogram using waveform based ANN-LSTM network

Cited by 157 publications

References 32 publications

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

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

Generalized Deep Neural Network Model for Cuffless Blood Pressure Estimation with Photoplethysmogram Signal Only

<p>Deep Learning-Based Method of Diagnosing Hyperlipidemia and Providing Diagnostic Markers Automatically</p>

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