Continuous blood pressure estimation based on multiple parameters from eletrocardiogram and photoplethysmogram by Back-propagation neural network

Xu, Zhihong; Liu, Jiexin; Chen, Xianxiang; Wang, Yilong; Zhao, Zhan

doi:10.1016/j.compind.2017.04.003

Cited by 62 publications

(33 citation statements)

References 33 publications

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“…Cuffless blood pressure measurement using PTT has been studied by many researchers [6,7,10]. The pulse wave can be measured by the PPG [7,10], impedance plethysmography [17], and a pressure sensor [14].…”

Section: Discussionmentioning

confidence: 99%

“…Some studies have also used the parameters extracted from a PPG and ECG to estimate blood pressure using a multilayer perceptron neural network [10,11], where the results showed good…”

Section: Discussionmentioning

confidence: 99%

“…In the Windkessel model [8], the pulse contour method was used to estimate cardiac output, and it can be used to estimate arterial stiffness using the Augmentation Index [9]. Xu et al used the PTT and characteristics of the PPG wave to estimate the continuous blood pressure using a neural network (NN) [10]. Wu et al used parameters extracted from the ECG and blood pressure waveform to estimate the blood pressure using a multilayer perceptron neural network [11].…”

Section: Introductionmentioning

confidence: 99%

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Using the Characteristics of Pulse Waveform to Enhance the Accuracy of Blood Pressure Measurement by a Multi-Dimension Regression Model

Liu

Pan

et al. 2019

Applied Sciences

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With the advancement of wearable technology, many physiological monitoring instruments are gradually being converted into wearable devices. However, as a consumer product, the blood pressure monitor is still a cuff-type device, which does perform a beat-by-beat continuous blood pressure measurement. Consequently, the cuffless blood pressure measurement device was developed and it is based on the pulse transit time (PTT), although its accuracy remains inadequate. According to the cardiac hemodynamic theorem, blood pressure relates to the arterial characteristics and the contours of the pulse wave include some characteristics of the artery. Therefore, the purpose of this study was to use the contour characteristics of the pulses measured by photoplethysmography (PPG) to estimate the blood pressure using a linear multi-dimension regression model. Ten subjects participated in the experiment, and the blood pressure levels of the subjects were elevated by exercise. The results showed that the mean and standard deviation (mean ± SD) of the root mean square error of the estimated systolic and diastolic pressures within the best five parameters were 6.9 ± 2.81 mmHg and 4.0 ± 0.65 mmHg, respectively. Compared to the results that used one parameter, the PTT, for estimating the systolic and diastolic pressures, 8.2 ± 2.1 mmHg and 4.5 ± 0.79 mmHg, respectively, our results were better.

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Section: Discussionmentioning

confidence: 99%

“…Some studies have also used the parameters extracted from a PPG and ECG to estimate blood pressure using a multilayer perceptron neural network [10,11], where the results showed good…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using the Characteristics of Pulse Waveform to Enhance the Accuracy of Blood Pressure Measurement by a Multi-Dimension Regression Model

Liu

Pan

et al. 2019

Applied Sciences

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“…Artificial neural networks (ANNs) were applied by Wu et al, who predicted SBP from body mass index, age, exercise, alcohol, and smoke level data, and We et al, who predicted SBP using data consisting of gender, serum cholesterol, fasting blood sugar, and features from electrocardiogram (ECG) signal. There are also several studies considering prediction of SBP and DBP using features extracted from ECG and PPG signals (eg, PAT times, peak widths, and positions) applying, for example, linear regression, support vector machine regression, decision trees, random forests, and ANNs …”

Section: Introductionmentioning

confidence: 99%

“…There are also several studies considering prediction of SBP and DBP using features extracted from ECG and PPG signals (eg, PAT times, peak widths, and positions) applying, for example, linear regression, 17,18 support vector machine regression, [17][18][19][20] decision trees, 17 random forests, 17,21 and ANNs. 22 In our previous work, 23 we carried out a numerical study to assess the accuracy of aortic pulse wave velocity (aPWV), DBP/SBP, and SV predictions based on PTT or PAT measurements. The study was based on a "virtual database" approach originally proposed by Willemet et al 24,25 With the cardiovascular simulator by Mynard and Smolich, 26 we were able to generate a large database of virtual blood circulations with sufficient variety physiological conditions for ML without difficult and expensive data collection from real human subjects.…”

Section: Introductionmentioning

confidence: 99%

Deep learning for prediction of cardiac indices from photoplethysmographic waveform: A virtual database approach

Huttunen

Kärkkäinen

Honkala

et al. 2020

Numer Methods Biomed Eng

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Deep learning methods combined with large datasets have recently shown significant progress in solving several medical tasks. However, collecting and annotating large datasets can be a very cumbersome and expensive task. We tackle these problems with a virtual database approach where training data is generated using computer simulations of related phenomena. Specifically, we concentrate on the following problem: can cardiovascular indices such as aortic elasticity, diastolic and systolic blood pressures, and blood flow from heart be predicted continuously using wearable photoplethysmographic sensors? We simulate the blood flow using a haemodynamic model consisting of the entire human circulation. Repeated evaluation of the simulator allows us to create a database of “virtual subjects” with size that is only limited by available computational resources. Using this database, we train neural networks to predict the cardiac indices from photoplethysmographic signal waveform. We consider two approaches: neural networks based on predefined input features and deep convolutional neural networks taking waveform directly as the input. The performance of the methods is demonstrated using numerical examples, thus carrying out a preliminary assessment of the approaches. The results show improvements in accuracy compared with the previous methods. The improvements are especially significant with indices related to aortic elasticity and maximum blood flow. The proposed approach would provide new means to measure cardiovascular health continuously, for example, with a simple wrist device.

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On Data Mining Technique for Differential Diagnostics Based on Data of Arterial Oscillography

Мінцер

Martsenyuk

Vakulenko

2019

Mechanisms and Machine Science

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Continuous blood pressure estimation based on multiple parameters from eletrocardiogram and photoplethysmogram by Back-propagation neural network

Cited by 62 publications

References 33 publications

Using the Characteristics of Pulse Waveform to Enhance the Accuracy of Blood Pressure Measurement by a Multi-Dimension Regression Model

Using the Characteristics of Pulse Waveform to Enhance the Accuracy of Blood Pressure Measurement by a Multi-Dimension Regression Model

Deep learning for prediction of cardiac indices from photoplethysmographic waveform: A virtual database approach

On Data Mining Technique for Differential Diagnostics Based on Data of Arterial Oscillography

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