Smartphones and Video Cameras: Future Methods for Blood Pressure Measurement

Steinman, Joe; Barszczyk, Andrew; Sun, Hong‐Shuo; Lee, Kang; Feng, Zhong‐Ping

doi:10.3389/fdgth.2021.770096

Cited by 14 publications

(10 citation statements)

References 90 publications

(118 reference statements)

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“…These previous results suggest that spatiotemporal maps of facial videos can be used to calculate BP values by using the hidden blood-flow information in the facial videos. Recently, remote methods that utilized facial videos to estimate BP have continually undergone research and development for improved accuracy [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Remote Blood Pressure Estimation via the Spatiotemporal Mapping of Facial Videos

Chen

Zhuang

et al. 2023

Sensors

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Blood pressure (BP) monitoring is vital in daily healthcare, especially for cardiovascular diseases. However, BP values are mainly acquired through a contact-sensing method, which is inconvenient and unfriendly for BP monitoring. This paper proposes an efficient end-to-end network for estimating BP values from a facial video to achieve remote BP estimation in daily life. The network first derives a spatiotemporal map of a facial video. Then, it regresses the BP ranges with a designed blood pressure classifier and simultaneously calculates the specific value with a blood pressure calculator in each BP range based on the spatiotemporal map. In addition, an innovative oversampling training strategy was developed to handle the problem of unbalanced data distribution. Finally, we trained the proposed blood pressure estimation network on a private dataset, MPM-BP, and tested it on a popular public dataset, MMSE-HR. As a result, the proposed network achieved a mean absolute error (MAE) and root mean square error (RMSE) of 12.35 mmHg and 16.55 mmHg on systolic BP estimations, and those for diastolic BP were 9.54 mmHg and 12.22 mmHg, which were better than the values obtained in recent works. It can be concluded that the proposed method has excellent potential for camera-based BP monitoring in the indoor scenarios in the real world.

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Section: Introductionmentioning

confidence: 99%

Remote Blood Pressure Estimation via the Spatiotemporal Mapping of Facial Videos

Chen

Zhuang

et al. 2023

Sensors

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“…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

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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.

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“…In the current body of literature, the rPPG signal is frequently compared solely to the extracted health-related information, such as heart rate or blood pressure, rather than to the ground truth PPG signal [ 5 , 6 ]. The error metrics used in this case are often the mean absolute error (MAE) or Pearson’s correlation coefficient ( r ) between the estimated and ground truth health-related information.…”

Section: Introductionmentioning

confidence: 99%

Effectiveness of Remote PPG Construction Methods: A Preliminary Analysis

2022

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The contactless recording of a photoplethysmography (PPG) signal with a Red-Green-Blue (RGB) camera is known as remote photoplethysmography (rPPG). Studies have reported on the positive impact of using this technique, particularly in heart rate estimation, which has led to increased research on this topic among scientists. Therefore, converting from RGB signals to constructing an rPPG signal is an important step. Eight rPPG methods (plant-orthogonal-to-skin (POS), local group invariance (LGI), the chrominance-based method (CHROM), orthogonal matrix image transformation (OMIT), GREEN, independent component analysis (ICA), principal component analysis (PCA), and blood volume pulse (PBV) methods) were assessed using dynamic time warping, power spectrum analysis, and Pearson’s correlation coefficient, with different activities (at rest, during exercising in the gym, during talking, and while head rotating) and four regions of interest (ROI) : the forehead, the left cheek, the right cheek, and a combination of all three ROIs. The best performing rPPG methods in all categories were the POS, LGI, and OMI methods; each performed well in all activities. Recommendations for future work are provided.

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Smartphones and Video Cameras: Future Methods for Blood Pressure Measurement

Cited by 14 publications

References 90 publications

Remote Blood Pressure Estimation via the Spatiotemporal Mapping of Facial Videos

Remote Blood Pressure Estimation via the Spatiotemporal Mapping of Facial Videos

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

Effectiveness of Remote PPG Construction Methods: A Preliminary Analysis

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