Wavelet based pulse rate and Blood pressure estimation system from ECG and PPG signals

Sahoo, Ashima; Manimegalai, P.; Thanushkodi, K.

doi:10.1109/icccet.2011.5762486

Cited by 31 publications

(7 citation statements)

References 8 publications

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“…Regarding the research on BP estimation, most studies use a combination of electrocardiogram (ECG) and photoplethysmogram (PPG) sensors [ 16 , 17 , 18 ], making the problem even more complex and equipment necessary. The common techniques used for obtaining measurements on BP mainly rely on Pulse Wave Velocity (PWV), Pulse Arrival Time (PAT), and Pulse Transit Time (PTT) [ 19 , 20 , 21 , 22 ], all of which require an accurate PPG measurement, which is not simple to get unobtrusively yet, and no clear proof on the PPG measurement’s relation to the BP has been provided [ 23 , 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques

Simjanoska

Gjoreski

Gams

et al. 2018

Sensors

134

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Background: Blood pressure (BP) measurements have been used widely in clinical and private environments. Recently, the use of ECG monitors has proliferated; however, they are not enabled with BP estimation. We have developed a method for BP estimation using only electrocardiogram (ECG) signals. Methods: Raw ECG data are filtered and segmented, and, following this, a complexity analysis is performed for feature extraction. Then, a machine-learning method is applied, combining a stacking-based classification module and a regression module for building systolic BP (SBP), diastolic BP (DBP), and mean arterial pressure (MAP) predictive models. In addition, the method allows a probability distribution-based calibration to adapt the models to a particular user. Results: Using ECG recordings from 51 different subjects, 3129 30-s ECG segments are constructed, and seven features are extracted. Using a train-validation-test evaluation, the method achieves a mean absolute error (MAE) of 8.64 mmHg for SBP, 18.20 mmHg for DBP, and 13.52 mmHg for the MAP prediction. When models are calibrated, the MAE decreases to 7.72 mmHg for SBP, 9.45 mmHg for DBP and 8.13 mmHg for MAP. Conclusion: The experimental results indicate that, when a probability distribution-based calibration is used, the proposed method can achieve results close to those of a certified medical device for BP estimation.

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Section: Introductionmentioning

confidence: 99%

Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques

Simjanoska

Gjoreski

Gams

et al. 2018

Sensors

134

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“…However valuable information about cardiovascular vital parameters can be extracted from frequency-domain and time-frequency features of PPG [55][56][57][58]. FMD-induced changes in these features may help in a better evaluation of the endothelial function.…”

Section: Future Directionsmentioning

confidence: 99%

Discrimination between different degrees of coronary artery disease using time-domain features of the finger photoplethysmogram in response to reactive hyperemia

Hosseini

Zahedi

Attar

et al. 2015

Biomedical Signal Processing and Control

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“…However, many conventional cuffless PTT-based BP estimation studies have some drawbacks, divided into four categories: (1) analysis of biased, (2) small datasets, (3) studies with devices that required calibration, and (4) insufficient accuracy as required by international standards. Some studies analyzed biased data that included only young participants with a narrow blood pressure range (12,13), while others had insufficient participants (13)(14)(15)(16)(17)(18). Other studies needed additional advanced preparations such as frequent calibrations (19)(20)(21), required additional parameters (22,23), or used devices that needed to be anchored to the body, resulting in annoyance for some users (24).…”

Section: Introductionmentioning

confidence: 99%

Calibration-Free Cuffless Blood Pressure Estimation Based on a Population With a Diverse Range of Age and Blood Pressure

Yamanaka

Morikawa²,

Morita

et al. 2021

Front. Med. Technol.

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This study presents a new blood pressure (BP) estimation algorithm utilizing machine learning (ML). A cuffless device that can measure BP without calibration would be precious for portability, continuous measurement, and comfortability, but unfortunately, it does not currently exist. Conventional BP measurement with a cuff is standard, but this method has various problems like inaccurate BP measurement, poor portability, and painful cuff pressure. To overcome these disadvantages, many researchers have developed cuffless BP estimation devices. However, these devices are not clinically applicable because they require advanced preparation before use, such as calibration, do not follow international standards (81060-1:2007), or have been designed using insufficient data sets. The present study was conducted to combat these issues. We recruited 127 participants and obtained 878 raw datasets. According to international standards, our diverse data set included participants from different age groups with a wide variety of blood pressures. We utilized ML to formulate a BP estimation method that did not require calibration. The present study also conformed to the method required by international standards while calculating the level of error in BP estimation. Two essential methods were applied in this study: (a) grouping the participants into five subsets based on the relationship between the pulse transit time and systolic BP by a support vector machine ensemble with bagging (b) applying the information from the wavelet transformation of the pulse wave and the electrocardiogram to the linear regression BP estimation model for each group. For systolic BP, the standard deviation of error for the proposed BP estimation results with cross-validation was 7.74 mmHg, which was an improvement from 17.05 mmHg, as estimated by the conventional pulse-transit-time-based methods. For diastolic BP, the standard deviation of error was 6.42 mmHg for the proposed BP estimation, which was an improvement from 14.05mmHg. The purpose of the present study was to demonstrate and evaluate the performance of the newly developed BP estimation ML method that meets the international standard for non-invasive sphygmomanometers in a population with a diverse range of age and BP.

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Wavelet based pulse rate and Blood pressure estimation system from ECG and PPG signals

Cited by 31 publications

References 8 publications

Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques

Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques

Discrimination between different degrees of coronary artery disease using time-domain features of the finger photoplethysmogram in response to reactive hyperemia

Calibration-Free Cuffless Blood Pressure Estimation Based on a Population With a Diverse Range of Age and Blood Pressure

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