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Poliński A. Continuous blood pressure monitoring by photoplethysmography - signal preprocessing requirements based on blood flow modelling. Physiol Meas 2023; 44. [PMID: 36827709 DOI: 10.1088/1361-6579/acbf00] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 02/24/2023] [Indexed: 02/26/2023]
Abstract
Objective.The aim of the study is to investigate the effect of the signal sampling frequency and low-pass filtering on the accuracy of the localisation of the fiducial points of the photoplethysmographic signal (PPG), and thus on the estimation of the blood pressure (i.e. the accuracy of the estimation).Approach.Statistical analysis was performed on 3,799 data samples taken from a publicly available database. Four PPG fiducial points of each sample signal were examined in the study.Main results.Simulation suggests that for noise-free data, cubic spline interpolation causes the sampling frequency (in the considered range of 62.5-500 Hz) to have only limited influence on localisation of the fiducial point. Better results were obtained for the pulse transit time (PTT) than pulse arrival time (PAT) approach. The acceptable filter band depends on the selected fiducial point and PAT or PTT approach. The best results were obtained for the tangent fiducial point.Significance.The presented results make it possible to estimate the minimum requirements for the sampling frequency and filtering of the PPG signal in order to obtain a reliable estimation of blood pressure.
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Affiliation(s)
- Artur Poliński
- Department of Biomedical Engineering, Faculty of Electronics, Telecommunication and Informatics, Gdańsk University of Technology, Gabriela Narutowicza 11/12, Gdańsk, Poland
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Fine J, McShane MJ, Coté GL, Scully CG. A Computational Modeling and Simulation Workflow to Investigate the Impact of Patient-Specific and Device Factors on Hemodynamic Measurements from Non-Invasive Photoplethysmography. BIOSENSORS 2022; 12:bios12080598. [PMID: 36004994 PMCID: PMC9405581 DOI: 10.3390/bios12080598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 07/16/2022] [Accepted: 07/27/2022] [Indexed: 11/23/2022]
Abstract
Cardiovascular disease is the leading cause of death globally. To provide continuous monitoring of blood pressure (BP), a parameter which has shown to improve health outcomes when monitored closely, many groups are trying to measure blood pressure via noninvasive photoplethysmography (PPG). However, the PPG waveform is subject to variation as a function of patient-specific and device factors and thus a platform to enable the evaluation of these factors on the PPG waveform and subsequent hemodynamic parameter prediction would enable device development. Here, we present a computational workflow that combines Monte Carlo modeling (MC), gaussian combination, and additive noise to create synthetic dataset of volar fingertip PPG waveforms representative of a diverse cohort. First, MC is used to determine PPG amplitude across age, skin tone, and device wavelength. Then, gaussian combination generates accurate PPG waveforms, and signal processing enables data filtration and feature extraction. We improve the limitations of current synthetic PPG frameworks by enabling inclusion of physiological and anatomical effects from body site, skin tone, and age. We then show how the datasets can be used to examine effects of device characteristics such as wavelength, analog to digital converter specifications, filtering method, and feature extraction. Lastly, we demonstrate the use of this framework to show the insensitivity of a support vector machine predictive algorithm compared to a neural network and bagged trees algorithm.
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Affiliation(s)
- Jesse Fine
- Department of Biomedical Engineering, Texas A&M University, College Station, TX 77843, USA
| | - Michael J. McShane
- Department of Biomedical Engineering, Texas A&M University, College Station, TX 77843, USA
- Center for Remote Health Technologies and Systems, Texas A&M Engineering Experiment Station, Texas A&M University, College Station, TX 77843, USA
- Department of Materials Science and Engineering, Texas A&M University, College Station, TX 77843, USA
| | - Gerard L. Coté
- Department of Biomedical Engineering, Texas A&M University, College Station, TX 77843, USA
- Center for Remote Health Technologies and Systems, Texas A&M Engineering Experiment Station, Texas A&M University, College Station, TX 77843, USA
- Correspondence:
| | - Christopher G. Scully
- Office of Science and Engineering Laboratories, Division of Biomedical Physics, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD 20993, USA
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Cuffless Blood Pressure Monitoring: Academic Insights and Perspectives Analysis. MICROMACHINES 2022; 13:mi13081225. [PMID: 36014147 PMCID: PMC9415520 DOI: 10.3390/mi13081225] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 07/22/2022] [Accepted: 07/28/2022] [Indexed: 11/16/2022]
Abstract
In recent decades, cuffless blood pressure monitoring technology has been a point of research in the field of health monitoring and public media. Based on the web of science database, this paper evaluated the publications in the field from 1990 to 2020 using bibliometric analysis, described the developments in recent years, and presented future research prospects in the field. Through the comparative analysis of keywords, citations, H-index, journals, research institutions, national authors and reviews, this paper identified research hotspots and future research trends in the field of cuffless blood pressure monitoring. From the results of the bibliometric analysis, innovative methods such as machine learning technologies related to pulse transmit time and pulse wave analysis have been widely applied in blood pressure monitoring. The 2091 articles related to cuffless blood pressure monitoring technology were published in 1131 journals. In the future, improving the accuracy of monitoring to meet the international medical blood pressure standards, and achieving portability and miniaturization will remain the development goals of cuffless blood pressure measurement technology. The application of flexible electronics and machine learning strategy in the field will be two major development directions to guide the practical applications of cuffless blood pressure monitoring technology.
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Charlton PH, Paliakaitė B, Pilt K, Bachler M, Zanelli S, Kulin D, Allen J, Hallab M, Bianchini E, Mayer CC, Terentes-Printzios D, Dittrich V, Hametner B, Veerasingam D, Žikić D, Marozas V. Assessing hemodynamics from the photoplethysmogram to gain insights into vascular age: A review from VascAgeNet. Am J Physiol Heart Circ Physiol 2021; 322:H493-H522. [PMID: 34951543 PMCID: PMC8917928 DOI: 10.1152/ajpheart.00392.2021] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
Abstract
The photoplethysmogram (PPG) signal is widely measured by clinical and consumer devices, and it is emerging as a potential tool for assessing vascular age. The shape and timing of the PPG pulse wave are both influenced by normal vascular aging, changes in arterial stiffness and blood pressure, and atherosclerosis. This review summarizes research into assessing vascular age from the PPG. Three categories of approaches are described: 1) those which use a single PPG signal (based on pulse wave analysis), 2) those which use multiple PPG signals (such as pulse transit time measurement), and 3) those which use PPG and other signals (such as pulse arrival time measurement). Evidence is then presented on the performance, repeatability and reproducibility, and clinical utility of PPG-derived parameters of vascular age. Finally, the review outlines key directions for future research to realize the full potential of photoplethysmography for assessing vascular age.
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Affiliation(s)
- Peter H Charlton
- Department of Public Health and Primary Care, University of Cambridge, United Kingdom.,Research Centre for Biomedical Engineering, City, University of London, London, United Kingdom
| | - Birutė Paliakaitė
- Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania
| | - Kristjan Pilt
- Department of Health Technologies, Tallinn University of Technology, Tallinn, Estonia
| | - Martin Bachler
- Biomedical Systems, Center for Health and Bioresources, AIT Austrian Institute of Technology, Vienna, Austria
| | - Serena Zanelli
- Laboratoire Analyse, Géométrie et Applications (LAGA), University Sorbonne Paris Nord, Paris, France.,Axelife, 44460 Saint Nicolas de Redon, France
| | - Daniel Kulin
- Institute of Translational Medicine, Semmelweis University, Budapest, Hungary.,E-Med4All Europe Ltd., Budapest, Hungary
| | - John Allen
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, United Kingdom.,Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Magid Hallab
- Axelife, 44460 Saint Nicolas de Redon, France.,Centre de recherche et d'Innovation, Clinique Bizet, Paris, France
| | | | - Christopher C Mayer
- Biomedical Systems, Center for Health and Bioresources, AIT Austrian Institute of Technology, Vienna, Austria
| | - Dimitrios Terentes-Printzios
- Hypertension and Cardiometabolic Unit, First Department of Cardiology, Hippokration Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | | | - Bernhard Hametner
- Biomedical Systems, Center for Health and Bioresources, AIT Austrian Institute of Technology, Vienna, Austria
| | - Dave Veerasingam
- Department of Cardiothoracic Surgery, Galway University Hospitals, Ireland
| | - Dejan Žikić
- Institute of Biophysics, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Vaidotas Marozas
- Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania
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Lin W, Demirel BU, Al Faruque MA, Li GP. Energy-efficient Blood Pressure Monitoring based on Single-site Photoplethysmogram on Wearable Devices. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:504-507. [PMID: 34891343 DOI: 10.1109/embc46164.2021.9630488] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The paper proposes accurate Blood Pressure Monitoring (BPM) based on a single-site Photoplethysmographic (PPG) sensor and provides an energy-efficient solution on edge cuffless wearable devices. Continuous PPG signal preprocessed and used as input of the Artificial Neural Network (ANN), and outputs systolic BP (SBP), diastolic BP (DBP), and mean arterial BP (MAP) values for each heartbeat. The improvement of the BPM accuracy is obtained by removing outliers in the preprocessing step and the whole-based inputs compared to parameter-based inputs extracted from the PPG signal. Performance obtained is 3.42 ± 5.42 mmHg (MAE ± RMSD) for SBP, 1.92 ± 3.29 mmHg for DBP, and 2.21 ± 3.50 mmHg for MAP which is competitive compared to other studies. This is the first BPM solution with edge computing artificial intelligence as we have learned so far. Evaluation experiments on real hardware show that the solution takes 42.2 ms, 18.2 KB RAM, and 2.1 mJ average energy per reading.
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Smart Healthcare System Based on Cloud-Internet of Things and Deep Learning. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:4109102. [PMID: 34257851 PMCID: PMC8260290 DOI: 10.1155/2021/4109102] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 05/31/2021] [Accepted: 06/20/2021] [Indexed: 11/17/2022]
Abstract
Introduction Health monitoring and remote diagnosis can be realized through Smart Healthcare. In view of the existing problems such as simple measurement parameters of wearable devices, huge computing pressure of cloud servers, and lack of individualization of diagnosis, a novel Cloud-Internet of Things (C-IOT) framework for medical monitoring is put forward. Methods Smart phones are adopted as gateway devices to achieve data standardization and preprocess to generate health gray-scale map uploaded to the cloud server. The cloud server realizes the business logic processing and uses the deep learning model to carry out the gray-scale map calculation of health parameters. A deep learning model based on the convolution neural network (CNN) is constructed, in which six volunteers are selected to participate in the experiment, and their health data are marked by private doctors to generate initial data set. Results Experimental results show the feasibility of the proposed framework. The test data set is used to test the CNN model after training; the forecast accuracy is over 77.6%. Conclusion The CNN model performs well in the recognition of health status. Collectively, this Smart Healthcare System is expected to assist doctors by improving the diagnosis of health status in clinical practice.
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CHAKRABORTY ABHISHEK, SADHUKHAN DEBOLEENA, PAL SAURABH, MITRA MADHUCHHANDA. PPG-BASED AUTOMATED ESTIMATION OF BLOOD PRESSURE USING PATIENT-SPECIFIC NEURAL NETWORK MODELING. J MECH MED BIOL 2020. [DOI: 10.1142/s0219519420500372] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Recently, photoplethysmography (PPG)-based techniques have been extensively used for cuff-less, automated estimation of blood pressure because of their inexpensive and effortless acquisition technology compared to other conventional approaches. However, most of the reported PPG-based, generalized BP estimation methods often lack the desired accuracy due to pathophysiological diversity. Moreover, some methods rely on several correction factors, which are not globalized yet and require further investigation. In this paper, a simple and automated systolic (SBP) and diastolic (DBP) blood pressure estimation method is proposed based on patient-specific neural network (NN) modeling. Initially, 15 time-plane PPG features are extracted and after feature selection, only four selected features are used in the NN model for beat-to-beat estimation of SBP and DBP, respectively. The proposed technique also presents reasonable accuracy while used for generalized estimation of BP. Performance of the algorithm is evaluated on 670 records of 50 intensive care unit (ICU) patients taken from MIMIC, MIMIC II and MIMIC Challenge databases. The proposed algorithm exhibits high average accuracy with (mean[Formula: see text][Formula: see text][Formula: see text]SD) of the estimated SBP as ([Formula: see text]) mmHg and DBP as ([Formula: see text]) mmHg. Compared to the other generalized models, the use of patient-specific approach eliminates the necessity of individual correction factors, thus increasing the robustness, accuracy and potential of the method to be implemented in personal healthcare applications.
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Affiliation(s)
- ABHISHEK CHAKRABORTY
- Department of Applied Physics, University of Calcutta, Kolkata, West Bengal, India
| | - DEBOLEENA SADHUKHAN
- Department of Applied Physics, University of Calcutta, Kolkata, West Bengal, India
| | - SAURABH PAL
- Department of Applied Physics, University of Calcutta, Kolkata, West Bengal, India
| | - MADHUCHHANDA MITRA
- Department of Applied Physics, University of Calcutta, Kolkata, West Bengal, India
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Girčys R, Kazanavičius E, Maskeliūnas R, Damaševičius R, Woźniak M. Wearable system for real-time monitoring of hemodynamic parameters: Implementation and evaluation. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.101873] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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