Blood pressure estimation with complexity features from electrocardiogram and photoplethysmogram signals

Yang, Shicheng; Zaki, Wan Suhaimizan Wan; Morgan, Stephen P.; Cho, Siu-Yeung; Correia, Ricardo; Zhang, Yaping

doi:10.1007/s11082-020-2260-7

Cited by 15 publications

(8 citation statements)

References 32 publications

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“…Many researchers have implemented ANN models for hypertension prediction, and some of these recent researches are [19][20][21][22][23][24][25][26][27][28][29][30]. Among these, Bani-Salameh et al [26] developed a multilayer perceptron (MLP) neural network model with six inputs: age, weight, fat ratio, blood pressure, alcohol, and smoking; one hidden layer and one output layer of hypertension and nonhypertension classes were implemented to train and test a sample size of 760 patients.…”

Section: Related Workmentioning

confidence: 99%

A Multilayer Perceptron Neural Network Model to Classify Hypertension in Adolescents Using Anthropometric Measurements: A Cross-Sectional Study in Sarawak, Malaysia

Chai

Cheah

Goh³

et al. 2021

Computational and Mathematical Methods in Medicine

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This study outlines and developed a multilayer perceptron (MLP) neural network model for adolescent hypertension classification focusing on the use of simple anthropometric and sociodemographic data collected from a cross-sectional research study in Sarawak, Malaysia. Among the 2,461 data collected, 741 were hypertensive (30.1%) and 1720 were normal (69.9%). During the data gathering process, eleven anthropometric measurements and sociodemographic data were collected. The variable selection procedure in the methodology proposed selected five parameters: weight, weight-to-height ratio (WHtR), age, sex, and ethnicity, as the input of the network model. The developed MLP model with a single hidden layer of 50 hidden neurons managed to achieve a sensitivity of 0.41, specificity of 0.91, precision of 0.65, F -score of 0.50, accuracy of 0.76, and Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of 0.75 using the imbalanced data set. Analyzing the performance metrics obtained from the training, validation and testing data sets show that the developed network model is well-generalized. Using Bayes’ Theorem, an adolescent classified as hypertensive using this created model has a 66.2% likelihood of having hypertension in the Sarawak adolescent population, which has a hypertension prevalence of 30.1%. When the prevalence of hypertension in the Sarawak population was increased to 50%, the developed model could predict an adolescent having hypertension with an 82.0% chance, whereas when the prevalence of hypertension was reduced to 10%, the developed model could only predict true positive hypertension with a 33.6% chance. With the sensitivity of the model increasing to 65% and 90% while retaining a specificity of 91%, the true positivity of an adolescent being hypertension would be 75.7% and 81.2%, respectively, according to Bayes’ Theorem. The findings show that simple anthropometric measurements paired with sociodemographic data are feasible to be used to classify hypertension in adolescents using the developed MLP model in Sarawak adolescent population with modest hypertension prevalence. However, a model with higher sensitivity and specificity is required for better positive hypertension predictive value when the prevalence is low. We conclude that the developed classification model could serve as a quick and easy preliminary warning tool for screening high-risk adolescents of developing hypertension.

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Section: Related Workmentioning

confidence: 99%

A Multilayer Perceptron Neural Network Model to Classify Hypertension in Adolescents Using Anthropometric Measurements: A Cross-Sectional Study in Sarawak, Malaysia

Chai

Cheah

Goh³

et al. 2021

Computational and Mathematical Methods in Medicine

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“…Prior studies focusing on the BP estimation task can be observed dealing with two kind of approaches, using either PPG signal only or PPG signal along with other signals. In [ 9 , 10 , 11 ], feature-based methods from PPG and electrocardiogram (ECG) signals are carried out. Using the same source from [ 12 ] for the ECG, PPG, and ABP signals as the training and testing set, these studies extract informative features based on the physiological parameters and the time-related indicators.…”

Section: Introductionmentioning

confidence: 99%

“…Recent studies [ 11 , 13 ] try to compare the performance of BP assessment between machine learning models that use the combination features from ECG and PPG signals and models that use features from PPG only. It is reported from both studies that using combination features from ECG and PPG signals results in a comparatively better performance.…”

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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“…Neural networks are considered as more sophisticated approaches with the aim to generate physiological signals [53,54]. We can note WaveNet, which is based on the auto-regressive neural network [55,56]. Generative Adversarial Networks (GANs) is other powerful alternative, which enables the development of physiological signals [57,58].…”

Section: Recent Workmentioning

confidence: 99%

An Artificial Heart System for Testing and Evaluation of Cardiac Pacemakers

Augustýnek¹,

Kubicek²,

Thomas³

et al. 2022

Computers, Materials &Amp; Continua

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The usability assessment of a pacemaker is a complex task where the dedicated programmer for testing programmed algorithms is necessary. This paper provides the outcomes of development and complex testing of the artificial cardiac system to evaluate the pacemaker's functionality. In this work, we used the modular laboratory platform ELVIS II and created graphical user interface in LabVIEW programming environment. The electrical model of the heart allows signals generation (right atrium, right ventricle) and the monitoring of the stimulation pulses. The LabVIEW user interface allows to set the parameters of the generated signals and the simulation of the cardiac rhythm disorders as well as the monitoring and visualization of the pacemaker behavior in real-time. The results demonstrate the capability of proposed system to evaluate the paced and sensed pulses. The proposed solution allows the scientists to test the behavior of any cardiac pacemaker for its pre-programmed settings and pacing mode. In addition, the proposed system can simulate various disorders and test cardiac pacemakers in different working modes.

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Blood pressure estimation with complexity features from electrocardiogram and photoplethysmogram signals

Cited by 15 publications

References 32 publications

A Multilayer Perceptron Neural Network Model to Classify Hypertension in Adolescents Using Anthropometric Measurements: A Cross-Sectional Study in Sarawak, Malaysia

A Multilayer Perceptron Neural Network Model to Classify Hypertension in Adolescents Using Anthropometric Measurements: A Cross-Sectional Study in Sarawak, Malaysia

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

An Artificial Heart System for Testing and Evaluation of Cardiac Pacemakers

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