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Stuckey TD, Meine FJ, McMinn TR, Depta JP, Bennett BA, McGarry TF, Carroll WS, Suh DD, Steuter JA, Roberts MC, Gillins HR, Fathieh F, Burton T, Nemati N, Shadforth IP, Ramchandani S, Bridges CR, Rabbat MG. Clinical Validation of a Machine-Learned, Point-of-Care System to IDENTIFY Functionally Significant Coronary Artery Disease. Diagnostics (Basel) 2024; 14:987. [PMID: 38786284 PMCID: PMC11120588 DOI: 10.3390/diagnostics14100987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 05/02/2024] [Accepted: 05/06/2024] [Indexed: 05/25/2024] Open
Abstract
Many clinical studies have shown wide performance variation in tests to identify coronary artery disease (CAD). Coronary computed tomography angiography (CCTA) has been identified as an effective rule-out test but is not widely available in the USA, particularly so in rural areas. Patients in rural areas are underserved in the healthcare system as compared to urban areas, rendering it a priority population to target with highly accessible diagnostics. We previously developed a machine-learned algorithm to identify the presence of CAD (defined by functional significance) in patients with symptoms without the use of radiation or stress. The algorithm requires 215 s temporally synchronized photoplethysmographic and orthogonal voltage gradient signals acquired at rest. The purpose of the present work is to validate the performance of the algorithm in a frozen state (i.e., no retraining) in a large, blinded dataset from the IDENTIFY trial. IDENTIFY is a multicenter, selectively blinded, non-randomized, prospective, repository study to acquire signals with paired metadata from subjects with symptoms indicative of CAD within seven days prior to either left heart catheterization or CCTA. The algorithm's sensitivity and specificity were validated using a set of unseen patient signals (n = 1816). Pre-specified endpoints were chosen to demonstrate a rule-out performance comparable to CCTA. The ROC-AUC in the validation set was 0.80 (95% CI: 0.78-0.82). This performance was maintained in both male and female subgroups. At the pre-specified cut point, the sensitivity was 0.85 (95% CI: 0.82-0.88), and the specificity was 0.58 (95% CI: 0.54-0.62), passing the pre-specified endpoints. Assuming a 4% disease prevalence, the NPV was 0.99. Algorithm performance is comparable to tertiary center testing using CCTA. Selection of a suitable cut-point results in the same sensitivity and specificity performance in females as in males. Therefore, a medical device embedding this algorithm may address an unmet need for a non-invasive, front-line point-of-care test for CAD (without any radiation or stress), thus offering significant benefits to the patient, physician, and healthcare system.
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Affiliation(s)
- Thomas D Stuckey
- Cone Health Heart and Vascular Center, Greensboro, NC 27401, USA
| | - Frederick J Meine
- Novant Health New Hanover Regional Medical Center, Wilmington, NC 28401, USA
| | | | | | | | | | | | - David D Suh
- Atlanta Heart Specialists, Tucker, GA 30084, USA
| | | | - Michael C Roberts
- Lexington Medical Center Heart & Vascular, West Columbia, SC 29169, USA
| | | | | | | | - Navid Nemati
- Analytics for Life, Inc., Toronto, ON M5X 1C9, Canada
| | | | | | | | - Mark G Rabbat
- Loyola University Medical Center, Maywood, IL 60153, USA
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Nemati N, Burton T, Fathieh F, Gillins HR, Shadforth I, Ramchandani S, Bridges CR. Pulmonary Hypertension Detection Non-Invasively at Point-of-Care Using a Machine-Learned Algorithm. Diagnostics (Basel) 2024; 14:897. [PMID: 38732312 PMCID: PMC11083349 DOI: 10.3390/diagnostics14090897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 04/10/2024] [Accepted: 04/22/2024] [Indexed: 05/13/2024] Open
Abstract
Artificial intelligence, particularly machine learning, has gained prominence in medical research due to its potential to develop non-invasive diagnostics. Pulmonary hypertension presents a diagnostic challenge due to its heterogeneous nature and similarity in symptoms to other cardiovascular conditions. Here, we describe the development of a supervised machine learning model using non-invasive signals (orthogonal voltage gradient and photoplethysmographic) and a hand-crafted library of 3298 features. The developed model achieved a sensitivity of 87% and a specificity of 83%, with an overall Area Under the Receiver Operator Characteristic Curve (AUC-ROC) of 0.93. Subgroup analysis showed consistent performance across genders, age groups and classes of PH. Feature importance analysis revealed changes in metrics that measure conduction, repolarization and respiration as significant contributors to the model. The model demonstrates promising performance in identifying pulmonary hypertension, offering potential for early detection and intervention when embedded in a point-of-care diagnostic system.
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Affiliation(s)
- Navid Nemati
- Analytics for Life, Toronto, ON M5X 1C9, Canada; (N.N.); (F.F.)
| | - Timothy Burton
- Analytics for Life, Toronto, ON M5X 1C9, Canada; (N.N.); (F.F.)
| | - Farhad Fathieh
- Analytics for Life, Toronto, ON M5X 1C9, Canada; (N.N.); (F.F.)
| | - Horace R. Gillins
- Analytics for Life, Bethesda, MD 20814, USA; (H.R.G.); (I.S.); (C.R.B.)
| | - Ian Shadforth
- Analytics for Life, Bethesda, MD 20814, USA; (H.R.G.); (I.S.); (C.R.B.)
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Burton T, Fathieh F, Nemati N, Gillins HR, Shadforth IP, Ramchandani S, Bridges CR. Development of a Non-Invasive Machine-Learned Point-of-Care Rule-Out Test for Coronary Artery Disease. Diagnostics (Basel) 2024; 14:719. [PMID: 38611631 PMCID: PMC11012183 DOI: 10.3390/diagnostics14070719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 03/22/2024] [Accepted: 03/25/2024] [Indexed: 04/14/2024] Open
Abstract
The current standard of care for coronary artery disease (CAD) requires an intake of radioactive or contrast enhancement dyes, radiation exposure, and stress and may take days to weeks for referral to gold-standard cardiac catheterization. The CAD diagnostic pathway would greatly benefit from a test to assess for CAD that enables the physician to rule it out at the point of care, thereby enabling the exploration of other diagnoses more rapidly. We sought to develop a test using machine learning to assess for CAD with a rule-out profile, using an easy-to-acquire signal (without stress/radiation) at the point of care. Given the historic disparate outcomes between sexes and urban/rural geographies in cardiology, we targeted equal performance across sexes in a geographically accessible test. Noninvasive photoplethysmogram and orthogonal voltage gradient signals were simultaneously acquired in a representative clinical population of subjects before invasive catheterization for those with CAD (gold-standard for the confirmation of CAD) and coronary computed tomographic angiography for those without CAD (excellent negative predictive value). Features were measured from the signal and used in machine learning to predict CAD status. The machine-learned algorithm achieved a sensitivity of 90% and specificity of 59%. The rule-out profile was maintained across both sexes, as well as all other relevant subgroups. A test to assess for CAD using machine learning on a noninvasive signal has been successfully developed, showing high performance and rule-out ability. Confirmation of the performance on a large clinical, blinded, enrollment-gated dataset is required before implementation of the test in clinical practice.
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Affiliation(s)
- Timothy Burton
- Analytics for Life, Toronto, ON M5X 1C9, Canada; (T.B.); (F.F.); (N.N.)
| | - Farhad Fathieh
- Analytics for Life, Toronto, ON M5X 1C9, Canada; (T.B.); (F.F.); (N.N.)
| | - Navid Nemati
- Analytics for Life, Toronto, ON M5X 1C9, Canada; (T.B.); (F.F.); (N.N.)
| | | | | | - Shyam Ramchandani
- Analytics for Life, Toronto, ON M5X 1C9, Canada; (T.B.); (F.F.); (N.N.)
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Cuevas-Chávez A, Hernández Y, Ortiz-Hernandez J, Sánchez-Jiménez E, Ochoa-Ruiz G, Pérez J, González-Serna G. A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases. Healthcare (Basel) 2023; 11:2240. [PMID: 37628438 PMCID: PMC10454027 DOI: 10.3390/healthcare11162240] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 07/21/2023] [Accepted: 07/24/2023] [Indexed: 08/27/2023] Open
Abstract
According to the Pan American Health Organization, cardiovascular disease is the leading cause of death worldwide, claiming an estimated 17.9 million lives each year. This paper presents a systematic review to highlight the use of IoT, IoMT, and machine learning to detect, predict, or monitor cardiovascular disease. We had a final sample of 164 high-impact journal papers, focusing on two categories: cardiovascular disease detection using IoT/IoMT technologies and cardiovascular disease using machine learning techniques. For the first category, we found 82 proposals, while for the second, we found 85 proposals. The research highlights list of IoT/IoMT technologies, machine learning techniques, datasets, and the most discussed cardiovascular diseases. Neural networks have been popularly used, achieving an accuracy of over 90%, followed by random forest, XGBoost, k-NN, and SVM. Based on the results, we conclude that IoT/IoMT technologies can predict cardiovascular diseases in real time, ensemble techniques obtained one of the best performances in the accuracy metric, and hypertension and arrhythmia were the most discussed diseases. Finally, we identified the lack of public data as one of the main obstacles for machine learning approaches for cardiovascular disease prediction.
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Affiliation(s)
- Alejandra Cuevas-Chávez
- Computer Science Department, Tecnológico Nacional de México/Cenidet, Cuernavaca 62490, Mexico; (J.O.-H.); (E.S.-J.); (J.P.); (G.G.-S.)
| | - Yasmín Hernández
- Computer Science Department, Tecnológico Nacional de México/Cenidet, Cuernavaca 62490, Mexico; (J.O.-H.); (E.S.-J.); (J.P.); (G.G.-S.)
| | - Javier Ortiz-Hernandez
- Computer Science Department, Tecnológico Nacional de México/Cenidet, Cuernavaca 62490, Mexico; (J.O.-H.); (E.S.-J.); (J.P.); (G.G.-S.)
| | - Eduardo Sánchez-Jiménez
- Computer Science Department, Tecnológico Nacional de México/Cenidet, Cuernavaca 62490, Mexico; (J.O.-H.); (E.S.-J.); (J.P.); (G.G.-S.)
| | - Gilberto Ochoa-Ruiz
- School of Engineering and Sciences, Tecnologico de Monterrey, Av. Eugenio Garza Sada 2501, Monterrey 64849, Mexico;
| | - Joaquín Pérez
- Computer Science Department, Tecnológico Nacional de México/Cenidet, Cuernavaca 62490, Mexico; (J.O.-H.); (E.S.-J.); (J.P.); (G.G.-S.)
| | - Gabriel González-Serna
- Computer Science Department, Tecnológico Nacional de México/Cenidet, Cuernavaca 62490, Mexico; (J.O.-H.); (E.S.-J.); (J.P.); (G.G.-S.)
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Bhavnani SP, Khedraki R, Cohoon TJ, Meine FJ, Stuckey TD, McMinn T, Depta JP, Bennett B, McGarry T, Carroll W, Suh D, Steuter JA, Roberts M, Gillins HR, Shadforth I, Lange E, Doomra A, Firouzi M, Fathieh F, Burton T, Khosousi A, Ramchandani S, Sanders WE, Smart F. Multicenter validation of a machine learning phase space electro-mechanical pulse wave analysis to predict elevated left ventricular end diastolic pressure at the point-of-care. PLoS One 2022; 17:e0277300. [PMID: 36378672 PMCID: PMC9665374 DOI: 10.1371/journal.pone.0277300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 10/25/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Phase space is a mechanical systems approach and large-scale data representation of an object in 3-dimensional space. Whether such techniques can be applied to predict left ventricular pressures non-invasively and at the point-of-care is unknown. OBJECTIVE This study prospectively validated a phase space machine-learned approach based on a novel electro-mechanical pulse wave method of data collection through orthogonal voltage gradient (OVG) and photoplethysmography (PPG) for the prediction of elevated left ventricular end diastolic pressure (LVEDP). METHODS Consecutive outpatients across 15 US-based healthcare centers with symptoms suggestive of coronary artery disease were enrolled at the time of elective cardiac catheterization and underwent OVG and PPG data acquisition immediately prior to angiography with signals paired with LVEDP (IDENTIFY; NCT #03864081). The primary objective was to validate a ML algorithm for prediction of elevated LVEDP using a definition of ≥25 mmHg (study cohort) and normal LVEDP ≤ 12 mmHg (control cohort), using AUC as the measure of diagnostic accuracy. Secondary objectives included performance of the ML predictor in a propensity matched cohort (age and gender) and performance for an elevated LVEDP across a spectrum of comparative LVEDP (<12 through 24 at 1 mmHg increments). Features were extracted from the OVG and PPG datasets and were analyzed using machine-learning approaches. RESULTS The study cohort consisted of 684 subjects stratified into three LVEDP categories, ≤12 mmHg (N = 258), LVEDP 13-24 mmHg (N = 347), and LVEDP ≥25 mmHg (N = 79). Testing of the ML predictor demonstrated an AUC of 0.81 (95% CI 0.76-0.86) for the prediction of an elevated LVEDP with a sensitivity of 82% and specificity of 68%, respectively. Among a propensity matched cohort (N = 79) the ML predictor demonstrated a similar result AUC 0.79 (95% CI: 0.72-0.8). Using a constant definition of elevated LVEDP and varying the lower threshold across LVEDP the ML predictor demonstrated and AUC ranging from 0.79-0.82. CONCLUSION The phase space ML analysis provides a robust prediction for an elevated LVEDP at the point-of-care. These data suggest a potential role for an OVG and PPG derived electro-mechanical pulse wave strategy to determine if LVEDP is elevated in patients with symptoms suggestive of cardiac disease.
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Affiliation(s)
- Sanjeev P. Bhavnani
- Division of Cardiovascular Medicine, Healthcare Innovation & Practice Transformation Laboratory, Scripps Clinic, San Diego, California, United States of America
- * E-mail:
| | - Rola Khedraki
- Division of Cardiology, Section Advanced Heart Failure, Scripps Clinic, San Diego, California, United States of America
| | - Travis J. Cohoon
- Division of Cardiovascular Medicine, Healthcare Innovation & Practice Transformation Laboratory, Scripps Clinic, San Diego, California, United States of America
| | - Frederick J. Meine
- Novant Health New Hanover Regional Medical Center, Wilmington, North Carolina, United States of America
| | - Thomas D. Stuckey
- Cone Health Heart and Vascular Center, Greensboro, North Carolina, United States of America
| | - Thomas McMinn
- Austin Heart, Austin, Texas, United States of America
| | - Jeremiah P. Depta
- Rochester General Hospital, Rochester, New York, United States of America
| | - Brett Bennett
- Jackson Heart Clinic, Jackson, Mississippi, United States of America
| | - Thomas McGarry
- Oklahoma Heart Hospital, Oklahoma City, Oklahoma, United States of America
| | - William Carroll
- Cardiology Associates of North Mississippi, Tupelo, Mississippi, United States of America
| | - David Suh
- Atlanta Heart Specialists, Atlanta, Georgia, United States of America
| | | | - Michael Roberts
- Lexington Medical Center, West Columbia, South Carolina, United States of America
| | | | - Ian Shadforth
- CorVista Health, Inc., Washington, DC, United States of America
| | - Emmanuel Lange
- CorVista Health, Toronto, Ontario, Canada
- Analytics For Life Inc., d.b.a CorVista Health, Toronto, Canada
| | - Abhinav Doomra
- CorVista Health, Toronto, Ontario, Canada
- Analytics For Life Inc., d.b.a CorVista Health, Toronto, Canada
| | - Mohammad Firouzi
- CorVista Health, Toronto, Ontario, Canada
- Analytics For Life Inc., d.b.a CorVista Health, Toronto, Canada
| | - Farhad Fathieh
- CorVista Health, Toronto, Ontario, Canada
- Analytics For Life Inc., d.b.a CorVista Health, Toronto, Canada
| | - Timothy Burton
- CorVista Health, Toronto, Ontario, Canada
- Analytics For Life Inc., d.b.a CorVista Health, Toronto, Canada
| | - Ali Khosousi
- CorVista Health, Toronto, Ontario, Canada
- Analytics For Life Inc., d.b.a CorVista Health, Toronto, Canada
| | - Shyam Ramchandani
- CorVista Health, Toronto, Ontario, Canada
- Analytics For Life Inc., d.b.a CorVista Health, Toronto, Canada
| | | | - Frank Smart
- LSU Health Science Center, New Orleans, Louisiana, United States of America
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Burton T, Ramchandani S, Bhavnani SP, Khedraki R, Cohoon TJ, Stuckey TD, Steuter JA, Meine FJ, Bennett BA, Carroll WS, Lange E, Fathieh F, Khosousi A, Rabbat M, Sanders WE. Identifying novel phenotypes of elevated left ventricular end diastolic pressure using hierarchical clustering of features derived from electromechanical waveform data. Front Cardiovasc Med 2022; 9:980625. [PMID: 36211581 PMCID: PMC9539436 DOI: 10.3389/fcvm.2022.980625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 08/22/2022] [Indexed: 11/13/2022] Open
Abstract
Introduction Elevated left ventricular end diastolic pressure (LVEDP) is a consequence of compromised left ventricular compliance and an important measure of myocardial dysfunction. An algorithm was developed to predict elevated LVEDP utilizing electro-mechanical (EM) waveform features. We examined the hierarchical clustering of selected features developed from these EM waveforms in order to identify important patient subgroups and assess their possible prognostic significance. Materials and methods Patients presenting with cardiovascular symptoms (N = 396) underwent EM data collection and direct LVEDP measurement by left heart catheterization. LVEDP was classified as non-elevated ( ≤ 12 mmHg) or elevated (≥25 mmHg). The 30 most contributive features to the algorithm output were extracted from EM data and input to an unsupervised hierarchical clustering algorithm. The resultant dendrogram was divided into five clusters, and patient metadata overlaid. Results The cluster with highest LVEDP (cluster 1) was most dissimilar from the lowest LVEDP cluster (cluster 5) in both clustering and with respect to clinical characteristics. In contrast to the cluster demonstrating the highest percentage of elevated LVEDP patients, the lowest was predominantly non-elevated LVEDP, younger, lower BMI, and males with a higher rate of significant coronary artery disease (CAD). The next adjacent cluster (cluster 2) to that of the highest LVEDP (cluster 1) had the second lowest LVEDP of all clusters. Cluster 2 differed from Cluster 1 primarily based on features extracted from the electrical data, and those that quantified predictability and variability of the signal. There was a low predictability and high variability in the highest LVEDP cluster 1, and the opposite in adjacent cluster 2. Conclusion This analysis identified subgroups of patients with varying degrees of LVEDP elevation based on waveform features. An approach to stratify movement between clusters and possible progression of myocardial dysfunction may include changes in features that differentiate clusters; specifically, reductions in electrical signal predictability and increases in variability. Identification of phenotypes of myocardial dysfunction evidenced by elevated LVEDP and knowledge of factors promoting transition to clusters with higher levels of left ventricular filling pressures could permit early risk stratification and improve patient selection for novel therapeutic interventions.
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Affiliation(s)
- Timothy Burton
- CorVista Health (Analytics For Life Inc., d.b.a CorVista Health) Toronto, Toronto, ON, Canada
| | - Shyam Ramchandani
- CorVista Health (Analytics For Life Inc., d.b.a CorVista Health) Toronto, Toronto, ON, Canada
| | | | - Rola Khedraki
- Scripps Clinic Division of Cardiology, San Diego, CA, United States
| | - Travis J. Cohoon
- Scripps Clinic Division of Cardiology, San Diego, CA, United States
| | - Thomas D. Stuckey
- Cone Health Heart and Vascular Center, Greensboro, NC, United States
| | | | - Frederick J. Meine
- Novant Health New Hanover Regional Medical Center, Wilmington, NC, United States
| | | | | | - Emmanuel Lange
- CorVista Health (Analytics For Life Inc., d.b.a CorVista Health) Toronto, Toronto, ON, Canada
| | - Farhad Fathieh
- CorVista Health (Analytics For Life Inc., d.b.a CorVista Health) Toronto, Toronto, ON, Canada
| | - Ali Khosousi
- CorVista Health (Analytics For Life Inc., d.b.a CorVista Health) Toronto, Toronto, ON, Canada
| | - Mark Rabbat
- Division of Cardiology, Loyola University Medical Center, Maywood, IL, United States
| | - William E. Sanders
- CorVista Health, Inc., Washington, DC, United States
- University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
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Loh HW, Xu S, Faust O, Ooi CP, Barua PD, Chakraborty S, Tan RS, Molinari F, Acharya UR. Application of photoplethysmography signals for healthcare systems: An in-depth review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 216:106677. [PMID: 35139459 DOI: 10.1016/j.cmpb.2022.106677] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 01/30/2022] [Accepted: 01/30/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVES Photoplethysmography (PPG) is a device that measures the amount of light absorbed by the blood vessel, blood, and tissues, which can, in turn, translate into various measurements such as the variation in blood flow volume, heart rate variability, blood pressure, etc. Hence, PPG signals can produce a wide variety of biological information that can be useful for the detection and diagnosis of various health problems. In this review, we are interested in the possible health disorders that can be detected using PPG signals. METHODS We applied PRISMA guidelines to systematically search various journal databases and identified 43 PPG studies that fit the criteria of this review. RESULTS Twenty-five health issues were identified from these studies that were classified into six categories: cardiac, blood pressure, sleep health, mental health, diabetes, and miscellaneous. Various routes were employed in these PPG studies to perform the diagnosis: machine learning, deep learning, and statistical routes. The studies were reviewed and summarized. CONCLUSIONS We identified limitations such as poor standardization of sampling frequencies and lack of publicly available PPG databases. We urge that future work should consider creating more publicly available databases so that a wide spectrum of health problems can be covered. We also want to promote the use of PPG signals as a potential precision medicine tool in both ambulatory and hospital settings.
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Affiliation(s)
- Hui Wen Loh
- School of Science and Technology, Singapore University of Social Sciences, Singapore
| | - Shuting Xu
- Cogninet Australia, Sydney, New South Wales 2010, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Australia
| | - Oliver Faust
- Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield S1 1WB, United Kingdom
| | - Chui Ping Ooi
- School of Science and Technology, Singapore University of Social Sciences, Singapore
| | - Prabal Datta Barua
- Faculty of Engineering and Information Technology, University of Technology Sydney, Australia; School of Business (Information Systems), Faculty of Business, Education, Law and Arts, University of Southern Queensland, Australia
| | - Subrata Chakraborty
- School of Science and Technology, Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW 2351, Australia; Centre for Advanced Modelling and Geospatial lnformation Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
| | - Ru-San Tan
- Department of Cardiology, National Heart Centre Singapore, 169609, Singapore; Duke-NUS Medical School, 169857, Singapore
| | - Filippo Molinari
- Department of Electronics and Telecommunications, Politecnico di Torino, Italy
| | - U Rajendra Acharya
- School of Science and Technology, Singapore University of Social Sciences, Singapore; School of Business (Information Systems), Faculty of Business, Education, Law and Arts, University of Southern Queensland, Australia; School of Engineering, Ngee Ann Polytechnic, 535 Clementi Road, 599489, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan; Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan.
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