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Kasbekar RS, Ji S, Clancy EA, Goel A. Optimizing the input feature sets and machine learning algorithms for reliable and accurate estimation of continuous, cuffless blood pressure. Sci Rep 2023; 13:7750. [PMID: 37173370 PMCID: PMC10181996 DOI: 10.1038/s41598-023-34677-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 05/05/2023] [Indexed: 05/15/2023] Open
Abstract
The advent of mobile devices, wearables and digital healthcare has unleashed a demand for accurate, reliable, and non-interventional ways to measure continuous blood pressure (BP). Many consumer products claim to measure BP with a cuffless device, but their lack of accuracy and reliability limit clinical adoption. Here, we demonstrate how multimodal feature datasets, comprising: (i) pulse arrival time (PAT); (ii) pulse wave morphology (PWM), and (iii) demographic data, can be combined with optimized Machine Learning (ML) algorithms to estimate Systolic BP (SBP), Diastolic BP (DBP) and Mean Arterial Pressure (MAP) within a 5 mmHg bias of the gold standard Intra-Arterial BP, well within the acceptable limits of the IEC/ANSI 80601-2-30 (2018) standard. Furthermore, DBP's calculated using 126 datasets collected from 31 hemodynamically compromised patients had a standard deviation within 8 mmHg, while SBP's and MAP's exceeded these limits. Using ANOVA and Levene's test for error means and standard deviations, we found significant differences in the various ML algorithms but found no significant differences amongst the multimodal feature datasets. Optimized ML algorithms and key multimodal features obtained from larger real-world data (RWD) sets could enable more reliable and accurate estimation of continuous BP in cuffless devices, accelerating wider clinical adoption.
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Affiliation(s)
- Rajesh S Kasbekar
- Department of Biomedical Engineering, Worcester Polytechnic Institute (WPI), Worcester, MA, USA.
| | - Songbai Ji
- Department of Biomedical Engineering, Worcester Polytechnic Institute (WPI), Worcester, MA, USA
| | - Edward A Clancy
- Department of Biomedical Engineering, Worcester Polytechnic Institute (WPI), Worcester, MA, USA
- Department of Electrical and Computer Engineering, Worcester Polytechnic Institute (WPI), Worcester, MA, USA
| | - Anita Goel
- Nanobiosym Research Institute, Nanobiosym, Inc. and Department of Physics, Harvard University, Cambridge, MA, USA
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2
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Mahmud S, Ibtehaz N, Khandakar A, Sohel Rahman M, JR. Gonzales A, Rahman T, Shafayet Hossain M, Sakib Abrar Hossain M, Ahasan Atick Faisal M, Fuad Abir F, Musharavati F, E. H. Chowdhury M. NABNet: A Nested Attention-guided BiConvLSTM network for a robust prediction of Blood Pressure components from reconstructed Arterial Blood Pressure waveforms using PPG and ECG signals. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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3
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Qin K, Huang W, Zhang T, Tang S. Machine learning and deep learning for blood pressure prediction: a methodological review from multiple perspectives. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10353-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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4
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Ibtehaz N, Mahmud S, Chowdhury MEH, Khandakar A, Salman Khan M, Ayari MA, Tahir AM, Rahman MS. PPG2ABP: Translating Photoplethysmogram (PPG) Signals to Arterial Blood Pressure (ABP) Waveforms. Bioengineering (Basel) 2022; 9:692. [PMID: 36421093 PMCID: PMC9687508 DOI: 10.3390/bioengineering9110692] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 11/08/2022] [Accepted: 11/11/2022] [Indexed: 08/13/2023] Open
Abstract
Cardiovascular diseases are one of the most severe causes of mortality, annually taking a heavy toll on lives worldwide. Continuous monitoring of blood pressure seems to be the most viable option, but this demands an invasive process, introducing several layers of complexities and reliability concerns due to non-invasive techniques not being accurate. This motivates us to develop a method to estimate the continuous arterial blood pressure (ABP) waveform through a non-invasive approach using Photoplethysmogram (PPG) signals. We explore the advantage of deep learning, as it would free us from sticking to ideally shaped PPG signals only by making handcrafted feature computation irrelevant, which is a shortcoming of the existing approaches. Thus, we present PPG2ABP, a two-stage cascaded deep learning-based method that manages to estimate the continuous ABP waveform from the input PPG signal with a mean absolute error of 4.604 mmHg, preserving the shape, magnitude, and phase in unison. However, the more astounding success of PPG2ABP turns out to be that the computed values of Diastolic Blood Pressure (DBP), Mean Arterial Pressure (MAP), and Systolic Blood Pressure (SBP) from the estimated ABP waveform outperform the existing works under several metrics (mean absolute error of 3.449 ± 6.147 mmHg, 2.310 ± 4.437 mmHg, and 5.727 ± 9.162 mmHg, respectively), despite that PPG2ABP is not explicitly trained to do so. Notably, both for DBP and MAP, we achieve Grade A in the BHS (British Hypertension Society) Standard and satisfy the AAMI (Association for the Advancement of Medical Instrumentation) standard.
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Affiliation(s)
- Nabil Ibtehaz
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Sakib Mahmud
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | | | - Amith Khandakar
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | | | - Mohamed Arselene Ayari
- Department of Civil and Architectural Engineering, Qatar University, Doha 2713, Qatar
- Technology Innovation and Engineering Education Unit (TIEE), Qatar University, Doha 2713, Qatar
| | - Anas M. Tahir
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - M. Sohel Rahman
- Department of CSE, BUET, ECE Building, West Palashi, Dhaka 1205, Bangladesh
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5
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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 2022; 322:H493-H522. [PMID: 34951543 PMCID: PMC8917928 DOI: 10.1152/ajpheart.00392.2021] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 12/21/2021] [Accepted: 12/21/2021] [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, Cambridge, United Kingdom
- Research Centre for Biomedical Engineering, 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, Seibersdorf, Austria
| | - Serena Zanelli
- Laboratoire Analyze, Géométrie et Applications, University Sorbonne Paris Nord, Paris, France
- Axelife, Redon, France
| | - Dániel Kulin
- Institute of Translational Medicine, Semmelweis University, Budapest, Hungary
- E-Med4All Europe, Limited, 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, 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, Seibersdorf, Austria
| | - Dimitrios Terentes-Printzios
- Hypertension and Cardiometabolic Unit, First Department of Cardiology, Hippokration Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Verena Dittrich
- Redwave Medical, Gesellschaft mit beschränkter Haftung, Jena, Germany
| | - Bernhard Hametner
- Biomedical Systems, Center for Health and Bioresources, AIT Austrian Institute of Technology, Seibersdorf, Austria
| | - Dave Veerasingam
- Department of Cardiothoracic Surgery, Galway University Hospitals, Galway, Ireland
| | - Dejan Žikić
- Faculty of Medicine, Institute of Biophysics, University of Belgrade, Belgrade, Serbia
| | - Vaidotas Marozas
- Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania
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6
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Phan DT, Nguyen CH, Nguyen TDP, Tran LH, Park S, Choi J, Lee BI, Oh J. A Flexible, Wearable, and Wireless Biosensor Patch with Internet of Medical Things Applications. BIOSENSORS 2022; 12:bios12030139. [PMID: 35323409 PMCID: PMC8945966 DOI: 10.3390/bios12030139] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 02/19/2022] [Accepted: 02/20/2022] [Indexed: 05/05/2023]
Abstract
Monitoring the vital signs and physiological responses of the human body in daily activities is particularly useful for the early diagnosis and prevention of cardiovascular diseases. Here, we proposed a wireless and flexible biosensor patch for continuous and longitudinal monitoring of different physiological signals, including body temperature, blood pressure (BP), and electrocardiography. Moreover, these modalities for tracking body movement and GPS locations for emergency rescue have been included in biosensor devices. We optimized the flexible patch design with high mechanical stretchability and compatibility that can provide reliable and long-term attachment to the curved skin surface. Regarding smart healthcare applications, this research presents an Internet of Things-connected healthcare platform consisting of a smartphone application, website service, database server, and mobile gateway. The IoT platform has the potential to reduce the demand for medical resources and enhance the quality of healthcare services. To further address the advances in non-invasive continuous BP monitoring, an optimized deep learning architecture with one-channel electrocardiogram signals is introduced. The performance of the BP estimation model was verified using an independent dataset; this experimental result satisfied the Association for the Advancement of Medical Instrumentation, and the British Hypertension Society standards for BP monitoring devices. The experimental results demonstrated the practical application of the wireless and flexible biosensor patch for continuous physiological signal monitoring with Internet of Medical Things-connected healthcare applications.
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Affiliation(s)
- Duc Tri Phan
- Industry 4.0 Convergence Bionics Engineering, Pukyong National University, Busan 48513, Korea; (D.T.P.); (C.H.N.); (T.D.P.N.); (L.H.T.); (S.P.); (J.C.)
- BK21 FOUR ‘New-Senior’ Oriented Smart Health Care Education, Pukyong National University, Busan 48513, Korea
| | - Cong Hoan Nguyen
- Industry 4.0 Convergence Bionics Engineering, Pukyong National University, Busan 48513, Korea; (D.T.P.); (C.H.N.); (T.D.P.N.); (L.H.T.); (S.P.); (J.C.)
- BK21 FOUR ‘New-Senior’ Oriented Smart Health Care Education, Pukyong National University, Busan 48513, Korea
| | - Thuy Dung Pham Nguyen
- Industry 4.0 Convergence Bionics Engineering, Pukyong National University, Busan 48513, Korea; (D.T.P.); (C.H.N.); (T.D.P.N.); (L.H.T.); (S.P.); (J.C.)
- BK21 FOUR ‘New-Senior’ Oriented Smart Health Care Education, Pukyong National University, Busan 48513, Korea
| | - Le Hai Tran
- Industry 4.0 Convergence Bionics Engineering, Pukyong National University, Busan 48513, Korea; (D.T.P.); (C.H.N.); (T.D.P.N.); (L.H.T.); (S.P.); (J.C.)
- BK21 FOUR ‘New-Senior’ Oriented Smart Health Care Education, Pukyong National University, Busan 48513, Korea
| | - Sumin Park
- Industry 4.0 Convergence Bionics Engineering, Pukyong National University, Busan 48513, Korea; (D.T.P.); (C.H.N.); (T.D.P.N.); (L.H.T.); (S.P.); (J.C.)
- BK21 FOUR ‘New-Senior’ Oriented Smart Health Care Education, Pukyong National University, Busan 48513, Korea
| | - Jaeyeop Choi
- Industry 4.0 Convergence Bionics Engineering, Pukyong National University, Busan 48513, Korea; (D.T.P.); (C.H.N.); (T.D.P.N.); (L.H.T.); (S.P.); (J.C.)
- BK21 FOUR ‘New-Senior’ Oriented Smart Health Care Education, Pukyong National University, Busan 48513, Korea
| | - Byeong-il Lee
- Department of Smart Healthcare, Pukyong National University, Busan 48513, Korea
- Correspondence: (B.-i.L.); (J.O.)
| | - Junghwan Oh
- Industry 4.0 Convergence Bionics Engineering, Pukyong National University, Busan 48513, Korea; (D.T.P.); (C.H.N.); (T.D.P.N.); (L.H.T.); (S.P.); (J.C.)
- BK21 FOUR ‘New-Senior’ Oriented Smart Health Care Education, Pukyong National University, Busan 48513, Korea
- Biomedical Engineering, Pukyong National University, Busan 48513, Korea
- Ohlabs Corporation, Busan 48513, Korea
- Correspondence: (B.-i.L.); (J.O.)
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Farki A, Baradaran Kazemzadeh R, Akhondzadeh Noughabi E. A Novel Clustering-Based Algorithm for Continuous and Noninvasive Cuff-Less Blood Pressure Estimation. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:3549238. [PMID: 35075386 PMCID: PMC8783699 DOI: 10.1155/2022/3549238] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 12/18/2021] [Accepted: 12/24/2021] [Indexed: 11/29/2022]
Abstract
Extensive research has been performed on continuous and noninvasive cuff-less blood pressure (BP) measurement using artificial intelligence algorithms. This approach involves extracting certain features from physiological signals, such as ECG, PPG, ICG, and BCG, as independent variables and extracting features from arterial blood pressure (ABP) signals as dependent variables and then using machine-learning algorithms to develop a blood pressure estimation model based on these data. The greatest challenge of this field is the insufficient accuracy of estimation models. This paper proposes a novel blood pressure estimation method with a clustering step for accuracy improvement. The proposed method involves extracting pulse transit time (PTT), PPG intensity ratio (PIR), and heart rate (HR) features from electrocardiogram (ECG) and photoplethysmogram (PPG) signals as the inputs of clustering and regression, extracting systolic blood pressure (SBP) and diastolic blood pressure (DBP) features from ABP signals as dependent variables, and finally developing regression models by applying gradient boosting regression (GBR), random forest regression (RFR), and multilayer perceptron regression (MLP) on each cluster. The method was implemented using the MIMIC-II data set with the silhouette criterion used to determine the optimal number of clusters. The results showed that because of the inconsistency, high dispersion, and multitrend behavior of the extracted features vectors, the accuracy can be significantly improved by running a clustering algorithm and then developing a regression model on each cluster and finally weighted averaging of the results based on the error of each cluster. When implemented with 5 clusters and GBR, this approach yielded an MAE of 2.56 for SBP estimates and 2.23 for DBP estimates, which were significantly better than the best results without clustering (DBP: 6.27, SBP: 6.36).
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Affiliation(s)
- Ali Farki
- Department of Information Technology Engineering Industrial and Systems Engineering Faculty, Tarbiat Modares University, Tehran, Iran
| | - Reza Baradaran Kazemzadeh
- Department of Information Technology Engineering Industrial and Systems Engineering Faculty, Tarbiat Modares University, Tehran, Iran
| | - Elham Akhondzadeh Noughabi
- Department of Information Technology Engineering Industrial and Systems Engineering Faculty, Tarbiat Modares University, Tehran, Iran
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8
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Deep generative model with domain adversarial training for predicting arterial blood pressure waveform from photoplethysmogram signal. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102972] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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9
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Esmaelpoor J, Sanat ZM, Moradi MH. A clinical set-up for noninvasive blood pressure monitoring using two photoplethysmograms and based on convolutional neural networks. ACTA ACUST UNITED AC 2021; 66:375-385. [PMID: 33826809 DOI: 10.1515/bmt-2020-0197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 03/22/2021] [Indexed: 11/15/2022]
Abstract
Blood pressure is a reliable indicator of many cardiac arrhythmias and rheological problems. This study proposes a clinical set-up using conventional monitoring systems to estimate systolic and diastolic blood pressures continuously based on two photoplethysmogram signals (PPG) taken from the earlobe and toe. Several amendments were applied to conventional clinical monitoring devices to construct our project plan. We used two monitors to acquire two PPGs, one ECG, and invasive blood pressure as the reference to evaluate the estimation accuracy. One of the most critical requirements was the synchronization of the acquired signals that was accomplished by using ECG as the time reference. Following data acquisition and preparation procedures, the performance of each PPG signal alone and together was investigated using deep convolutional neural networks. The proposed architecture was evaluated on 32 records acquired from 14 patients after cardiovascular surgery. The results showed a better performance for toe PPG in comparison with earlobe PPG. Moreover, they indicated the algorithm accuracy improves if both signals are applied together to the network. According to the British Hypertension Society standards, the results achieved grade A for both blood pressure measurements. The mean and standard deviation of estimation errors were +0.3 ± 4.9 and +0.1 ± 3.2 mmHg for systolic and diastolic BPs, respectively. Since the method is based on conventional monitoring equipment and provides a high estimation consistency, it can be considered as a possible alternative for inconvenient invasive BP monitoring in clinical environments.
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Affiliation(s)
- Jamal Esmaelpoor
- Department of Electrical Engineering, Islamic Azad University, Boukan Branch, Boukan, Iran
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Ding X, Clifton D, Ji N, Lovell NH, Bonato P, Chen W, Yu X, Xue Z, Xiang T, Long X, Xu K, Jiang X, Wang Q, Yin B, Feng G, Zhang YT. Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic. IEEE Rev Biomed Eng 2021; 14:48-70. [PMID: 32396101 DOI: 10.1109/rbme.2020.2992838] [Citation(s) in RCA: 103] [Impact Index Per Article: 34.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Coronavirus disease 2019 (COVID-19) has emerged as a pandemic with serious clinical manifestations including death. A pandemic at the large-scale like COVID-19 places extraordinary demands on the world's health systems, dramatically devastates vulnerable populations, and critically threatens the global communities in an unprecedented way. While tremendous efforts at the frontline are placed on detecting the virus, providing treatments and developing vaccines, it is also critically important to examine the technologies and systems for tackling disease emergence, arresting its spread and especially the strategy for diseases prevention. The objective of this article is to review enabling technologies and systems with various application scenarios for handling the COVID-19 crisis. The article will focus specifically on 1) wearable devices suitable for monitoring the populations at risk and those in quarantine, both for evaluating the health status of caregivers and management personnel, and for facilitating triage processes for admission to hospitals; 2) unobtrusive sensing systems for detecting the disease and for monitoring patients with relatively mild symptoms whose clinical situation could suddenly worsen in improvised hospitals; and 3) telehealth technologies for the remote monitoring and diagnosis of COVID-19 and related diseases. Finally, further challenges and opportunities for future directions of development are highlighted.
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11
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Investigation on the effect of Womersley number, ECG and PPG features for cuff less blood pressure estimation using machine learning. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.101942] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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12
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Screening System for Cardiac Problems through Non-Invasive Identification of Blood Pressure Waveform. INFORMATION 2020. [DOI: 10.3390/info11030150] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
This paper presents the initial development of a non-invasive system for identification of the pulse pressure waveform to be used for screening cardiac problems. The system employs a tonometric method using an off-the-shelf force sensor and custom-designed electronic circuits. Initial results on the use of the system are presented.
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