Wearable Blood Pressure Monitoring Devices: Understanding Heterogeneity in Design and Evaluation

Overview

Journal IEEE Trans Biomed Eng

Specialties Biomedical Engineering
Biophysics

Date 2024 Aug 6

PMID 39106139

Authors

Matt Y Cheung

Ashutosh Sabharwal

Gerard L Cote

Ashok Veeraraghavan

Affiliations

Soon will be listed here.

Abstract

Objective: Rapid advances in cuffless blood pressure (BP) monitoring have the potential to radically transform clinical care for cardiovascular health. However, due to the large heterogeneity in device design and evaluation, it is difficult to critically and quantitatively evaluate research progress. In this two-part manuscript, we provide a principled way of describing and accounting for heterogeneity in device and study design.

Methods: We first provide an overview of foundational elements and design principles of three critical aspects: 1) sensors and systems, 2) pre-processing and feature extraction, and 3) BP estimation algorithms. Then, we critically analyze the state-of-the-art methods via a systematic review.

Results: First, we find large heterogeneity in study designs, making fair comparisons extremely challenging. Moreover, many study designs have data leakage and are underpowered. We suggest a first open-contribution BP estimation benchmark for standardization. Next, we observe that BP distribution in the study sample and the time between calibration and test in emerging personalized devices confound BP estimation error. We suggest accounting for these using a convenient metric coined "explained deviation". Finally, we complement this manuscript with a website, https://wearablebp.github.io, containing a bibliography, meta-analysis results, datasets, and benchmarks, providing a timely plaWorm to understand state-of-the-art devices.

Conclusion: There is large heterogeneity in device and study design, which should be carefully accounted for when designing, comparing, and contrasting studies.

Significance: Our findings will allow readers to parse out the heterogeneous literature and move toward promising directions for safer and more reliable devices in clinical practice and beyond.

References

Jeong D, Lim K . Combined deep CNN-LSTM network-based multitasking learning architecture for noninvasive continuous blood pressure estimation using difference in ECG-PPG features. Sci Rep. 2021; 11(1):13539. PMC: 8242087. DOI: 10.1038/s41598-021-92997-0. View

Kumar M, Veeraraghavan A, Sabharwal A . DistancePPG: Robust non-contact vital signs monitoring using a camera. Biomed Opt Express. 2015; 6(5):1565-88. PMC: 4467696. DOI: 10.1364/BOE.6.001565. View

Liu Z, Liu J, Wen B, He Q, Li Y, Miao F . Cuffless Blood Pressure Estimation Using Pressure Pulse Wave Signals. Sensors (Basel). 2018; 18(12). PMC: 6308537. DOI: 10.3390/s18124227. View

Giltvedt J, Sira A, Helme P . Pulsed multifrequency photoplethysmograph. Med Biol Eng Comput. 1984; 22(3):212-5. DOI: 10.1007/BF02442745. View

Chandrasekhar A, Yavarimanesh M, Natarajan K, Hahn J, Mukkamala R . PPG Sensor Contact Pressure Should Be Taken Into Account for Cuff-Less Blood Pressure Measurement. IEEE Trans Biomed Eng. 2020; 67(11):3134-3140. PMC: 8856571. DOI: 10.1109/TBME.2020.2976989. View

OBrien E, Kario K, Staessen J, De La Sierra A, Ohkubo T . Patterns of ambulatory blood pressure: clinical relevance and application. J Clin Hypertens (Greenwich). 2018; 20(7):1112-1115. PMC: 8030861. DOI: 10.1111/jch.13277. View

Natarajan K, Block R, Yavarimanesh M, Chandrasekhar A, Mestha L, Inan O . Photoplethysmography Fast Upstroke Time Intervals Can Be Useful Features for Cuff-Less Measurement of Blood Pressure Changes in Humans. IEEE Trans Biomed Eng. 2021; 69(1):53-62. PMC: 8782151. DOI: 10.1109/TBME.2021.3087105. View

Aydemir T, Sahin M, Aydemir O . Determination of hypertension disease using chirp z-transform and statistical features of optimal band-pass filtered short-time photoplethysmography signals. Biomed Phys Eng Express. 2021; 6(6). DOI: 10.1088/2057-1976/abc634. View

Kachel E, Constantini K, Nachman D, Carasso S, Littman R, Eisenkraft A . A Pilot Study of Blood Pressure Monitoring After Cardiac Surgery Using a Wearable, Non-invasive Sensor. Front Med (Lausanne). 2021; 8:693926. PMC: 8375406. DOI: 10.3389/fmed.2021.693926. View

10.

Ibrahim B, Jafari R . Cuffless blood pressure monitoring from a wristband with calibration-free algorithms for sensing location based on bio-impedance sensor array and autoencoder. Sci Rep. 2022; 12(1):319. PMC: 8748973. DOI: 10.1038/s41598-021-03612-1. View

11.

Mukkamala R, Hahn J, Inan O, Mestha L, Kim C, Toreyin H . Toward Ubiquitous Blood Pressure Monitoring via Pulse Transit Time: Theory and Practice. IEEE Trans Biomed Eng. 2015; 62(8):1879-901. PMC: 4515215. DOI: 10.1109/TBME.2015.2441951. View

12.

Hickey M, Phillips J, Kyriacou P . Investigation of peripheral photoplethysmographic morphology changes induced during a hand-elevation study. J Clin Monit Comput. 2015; 30(5):727-36. DOI: 10.1007/s10877-015-9761-0. View

13.

Mukkamala R, Hahn J . Toward Ubiquitous Blood Pressure Monitoring via Pulse Transit Time: Predictions on Maximum Calibration Period and Acceptable Error Limits. IEEE Trans Biomed Eng. 2017; 65(6):1410-1420. PMC: 6014705. DOI: 10.1109/TBME.2017.2756018. View

14.

Park D, Cho S, Kim K, Woo H, Kim J, Lee J . Prediction Algorithms for Blood Pressure Based on Pulse Wave Velocity Using Health Checkup Data in Healthy Korean Men: Algorithm Development and Validation. JMIR Med Inform. 2021; 9(12):e29212. PMC: 8701706. DOI: 10.2196/29212. View

15.

Boonya-Ananta T, Rodriguez A, Ajmal A, Du Le V, Hansen A, Hutcheson J . Synthetic photoplethysmography (PPG) of the radial artery through parallelized Monte Carlo and its correlation to body mass index (BMI). Sci Rep. 2021; 11(1):2570. PMC: 7843978. DOI: 10.1038/s41598-021-82124-4. View

16.

Dagamseh A, Qananwah Q, Al Quran H, Ibrahim K . Towards a portable-noninvasive blood pressure monitoring system utilizing the photoplethysmogram signal. Biomed Opt Express. 2022; 12(12):7732-7751. PMC: 8713675. DOI: 10.1364/BOE.444535. View

17.

Kei Fong M, Ng E, Er Zi Jian K, Hong T . SVR ensemble-based continuous blood pressure prediction using multi-channel photoplethysmogram. Comput Biol Med. 2019; 113:103392. DOI: 10.1016/j.compbiomed.2019.103392. View

18.

Poon C, Zhang Y . Cuff-less and noninvasive measurements of arterial blood pressure by pulse transit time. Conf Proc IEEE Eng Med Biol Soc. 2007; 2005:5877-80. DOI: 10.1109/IEMBS.2005.1615827. View

19.

Simjanoska M, Gjoreski M, Gams M, Madevska Bogdanova A . Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques. Sensors (Basel). 2018; 18(4). PMC: 5949031. DOI: 10.3390/s18041160. View

20.

Mendelson Y, Ochs B . Noninvasive pulse oximetry utilizing skin reflectance photoplethysmography. IEEE Trans Biomed Eng. 1988; 35(10):798-805. DOI: 10.1109/10.7286. View