Sensitivity of Video-Based Pulse Arrival Time to Dynamic Blood Pressure Changes

Shirbani, Fatemeh; Blackmore, Conner; Kazzi, Christina; Tan, Isabella; Butlin, Mark; Avolio, Alberto

doi:10.1109/embc.2018.8513058

Cited by 6 publications

(3 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This task is challenging, and no consistent criteria have been established yet, which often needs to be determined empirically or experimentally. First, the poor signals can be removed by some empirical values, such as limiting systolic blood pressure (SBP) and diastolic blood pressure (DBP) to 80-180 mmHg and 60-130 mmHg, respectively [ 26 , 27 ], deleting the PPG signals with heart rate below 60 beats per minute [ 28 ], and rejecting outliers by using quartile [ 29 ] or 3-sigma criterion [ 30 ]. Moreover, considering the periodicity of PPG, some researches adopted autocorrelation between PPG periodic signals [ 31 ], dynamic time warping [ 32 ], or high-order statistics, such as skewness and kurtosis [ 33 , 34 ], to further standardize the quality of PPG signals Normalization: converting the amplitude of the PPG signal to the range of [0-1] can simplify and enhance the analysis process of the PPG signal and ensure that the extracted values are meaningful and fair to the subsequent characterization process.…”

Section: Methodsmentioning

confidence: 99%

Advances in Cuffless Continuous Blood Pressure Monitoring Technology Based on PPG Signals

Qin

Wang

et al. 2022

BioMed Research International

View full text Add to dashboard Cite

Objective. To review the progress of research on photoplethysmography- (PPG-) based cuffless continuous blood pressure monitoring technologies and prospect the challenges that need to be addressed in the future. Methods. Using Web of Science and PubMed as search engines, the literature on cuffless continuous blood pressure studies using PPG signals in the recent five years were searched. Results. Based on the retrieved literature, this paper describes the available open datasets, commonly used signal preprocessing methods, and model evaluation criteria. Early researches employed multisite PPG signals to calculate pulse wave velocity or time and predicted blood pressure by a simple linear equation. Later, extensive researches were dedicated to mine the features of PPG signals related to blood pressure and regressed blood pressure by machine learning models. Most recently, many researches have emerged to experiment with complex deep learning models for blood pressure prediction with the raw PPG signal as input. Conclusion. This paper summarized the methods in the retrieved literature, provided insight into the artificial intelligence algorithms employed in the literature, and concluded with a discussion of the challenges and opportunities for the development of cuffless continuous blood pressure monitoring technologies.

show abstract

Section: Methodsmentioning

confidence: 99%

Advances in Cuffless Continuous Blood Pressure Monitoring Technology Based on PPG Signals

Qin

Wang

et al. 2022

BioMed Research International

View full text Add to dashboard Cite

show abstract

“…Their study used high speed video recording (420 fps) to capture 1-min videos between/before/after exercise protocols, with the subject seated and hand on a table. Other studies utilizing PTT to estimate BP from single channel processing include: Yoshioka et al ( 46 ), Huang et al ( 50 ), Khong et al ( 51 ), Murakami et al ( 44 ) and Shirbani et al ( 57 ). As an alternative to PTT, it may be possible to capture videos of hands at different heights, relating the difference in pulse amplitudes between the hands to BP ( 58 ).…”

Section: Non-contact Video Camera Measurement Of Blood Pressurementioning

confidence: 99%

Smartphones and Video Cameras: Future Methods for Blood Pressure Measurement

Steinman

Barszczyk

Sun

et al. 2021

Front. Digit. Health

View full text Add to dashboard Cite

Regular blood pressure (BP) monitoring enables earlier detection of hypertension and reduces cardiovascular disease. Cuff-based BP measurements require equipment that is inconvenient for some individuals and deters regular home-based monitoring. Since smartphones contain sensors such as video cameras that detect arterial pulsations, they could also be used to assess cardiovascular health. Researchers have developed a variety of image processing and machine learning techniques for predicting BP via smartphone or video camera. This review highlights research behind smartphone and video camera methods for measuring BP. These methods may in future be used at home or in clinics, but must be tested over a larger range of BP and lighting conditions. The review concludes with a discussion of the advantages of the various techniques, their potential clinical applications, and future directions and challenges. Video cameras may potentially measure multiple cardiovascular metrics including and beyond BP, reducing the risk of cardiovascular disease.

show abstract

“…By regarding our main interest in PPG-ABP dataset for BP estimation, there are still many opportunities for further data cleaning. Continuous non-invasive BP may be carried out through wearable devices [24]or remotely by camera [25][26][27].Other than BP estimation based on pulse arrival time (PAT) techniques [28][29][30][31][32]where the information resides in the phase shift between PPG and ECG signals, BP estimation from PPG only [5,33] imposes higher challenges in model fitting. PPG-based BP estimation is preferred over PPG-ECG approaches because ECG sensors can be relaxed by one PPG sensor.…”

Section: Introductionmentioning

confidence: 99%

Beat-Based PPG-ABP Cleaning Technique for Blood Pressure Estimation

et al. 2022

View full text Add to dashboard Cite

This work is supported in part by the Information Technology Industry Development Agency (ITIDA) under number ARP2019.R27.4.ABSTRACTA growing attention is given to exploiting Photoplethysmography (PPG) signals in noninvasively measuring many physiological vital signs. Many machine deep learning models were trained for predicting the continuous arterial blood pressure (ABP) or just the systolic and diastolic blood pressure (BP) values based on a public database. However, jointly cleaning the PPG-ABP dataset that is the most critical pre-processing step for training quality is still in need for more investigations. There is a considerable amount of anomaly data that has to be excluded before any training stage. This paper introduces a two-level joint PPG-ABP cleaning technique conducted at a signal level and per-beat level. Many quality metrics have been checked successively for excluding improper data. These metrics achieve a coarse cleaning step. Finally, principal component analysis (PCA) is exploited for fine cleaning the remaining data from the former stage. The cleaning efficiency is evaluated by measuring its impact on the deep-learning-based BP estimation models. The trained model based on our cleaned data shows performance enhancement in terms of prediction error and the correlation between the predicted and ground-truth BP. Segmented and cleaned PPG/ABP dataset will be publicly available at both signal level and beat level. Based on the simulation results, the proposed cleaning technique enhances the standard deviation of the prediction error of systolic and diastolic blood pressure by 11.68 % and 10.81 %, respectively. Also, it enhances the mean absolute error of the prediction of systolic and diastolic blood pressure by 14.79 % and 11.70 %, respectively.

show abstract

Sensitivity of Video-Based Pulse Arrival Time to Dynamic Blood Pressure Changes

Cited by 6 publications

References 17 publications

Advances in Cuffless Continuous Blood Pressure Monitoring Technology Based on PPG Signals

Advances in Cuffless Continuous Blood Pressure Monitoring Technology Based on PPG Signals

Smartphones and Video Cameras: Future Methods for Blood Pressure Measurement

Beat-Based PPG-ABP Cleaning Technique for Blood Pressure Estimation

Contact Info

Product

Resources

About