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van Dijk W, Huizink AC, Oosterman M, Lemmers-Jansen ILJ, de Vente W. Validation of Photoplethysmography Using a Mobile Phone Application for the Assessment of Heart Rate Variability in the Context of Heart Rate Variability-Biofeedback. Psychosom Med 2023; 85:568-576. [PMID: 37678565 DOI: 10.1097/psy.0000000000001236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
OBJECTIVE Heart rate variability-biofeedback (HRV-BF) is an effective intervention to reduce stress and anxiety and requires accurate measures of real-time HRV. HRV can be measured through photoplethysmography (PPG) using the camera of a mobile phone. No studies have directly compared HRV-BF supported through PPG against classical electrocardiogram (ECG). The current study aimed to validate PPG HRV measurements during HRV-BF against ECG. METHODS Fifty-seven healthy participants (70% women) with a mean (standard deviation) age of 26.70 (9.86) years received HRV-BF in the laboratory. Participants filled out questionnaires and performed five times a 5-minute diaphragmatic breathing exercise at different paces (range, ~6.5 to ~4.5 breaths/min). Four HRV indices obtained through PPG, using the Happitech software development kit, and ECG, using the validated NeXus apparatus, were calculated and compared: RMSSD, pNN50, LFpower, and HFpower. Resonance frequency (i.e., optimal breathing pace) was also compared between methods. RESULTS All intraclass correlation coefficient values of the five different breathing paces were "near perfect" (>0.90) for all HRV indices: lnRMSSD, lnpNN50, lnLFpower, and lnHFpower. All Bland-Altman analyses (with just three incidental exceptions) showed good interchangeability of PPG- and ECG-derived HRV indices. No systematic evidence for proportional bias was found for any of the HRV indices. In addition, correspondence in resonance frequency detection was good with 76.6% agreement between PPG and ECG. CONCLUSIONS PPG is a potentially reliable and valid method for the assessment of HRV. PPG is a promising replacement of ECG assessment to measure resonance frequency during HRV-BF.
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Affiliation(s)
- Willeke van Dijk
- From the Departments of Clinical, Neuro and Developmental Psychology (van Dijk, Huizink, Lemmers-Jansen) and Clinical Child and Family Studies (Oosterman), Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam; Institute for Brain and Behavior Amsterdam (IBBA), Amsterdam, the Netherlands; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom (Lemmers-Jansen); and Research Institute of Child Development and Education, University of Amsterdam, Amsterdam, the Netherlands (de Vente)
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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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Shchelkanova E, Shchapova L, Shchelkanov A, Shibata T. Blue as an Underrated Alternative to Green: Photoplethysmographic Heartbeat Intervals Estimation under Two Temperature Conditions. SENSORS 2021; 21:s21124241. [PMID: 34205706 PMCID: PMC8233784 DOI: 10.3390/s21124241] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 05/21/2021] [Accepted: 06/08/2021] [Indexed: 12/04/2022]
Abstract
Since photoplethysmography (PPG) sensors are usually placed on open skin areas, temperature interference can be an issue. Currently, green light is the most widely used in the reflectance PPG for its relatively low artifact susceptibility. However, it has been known that hemoglobin absorption peaks at the blue part of the spectrum. Despite this fact, blue light has received little attention in the PPG field. Blue wavelengths are commonly used in phototherapy. Combining blue light-based treatments with simultaneous blue PPG acquisition could be potentially used in patients monitoring and studying the biological effects of light. Previous studies examining the PPG in blue light compared to other wavelengths employed photodetectors with inherently lower sensitivity to blue, thereby biasing the results. The present study assessed the accuracy of heartbeat intervals (HBIs) estimation from blue and green PPG signals, acquired under baseline and cold temperature conditions. Our PPG system is based on TCS3472 Color Sensor with equal sensitivity to both parts of the light spectrum to ensure unbiased comparison. The accuracy of the HBIs estimates, calculated with five characteristic points (PPG systolic peak, maximum of the first PPG derivative, maximum of the second PPG derivative, minimum of the second PPG derivative, and intersecting tangents) on both PPG signal types, was evaluated based on the electrocardiographic values. The statistical analyses demonstrated that in all cases, the HBIs estimation accuracy of blue PPG was nearly equivalent to the G PPG irrespective of the characteristic point and measurement condition. Therefore, blue PPG can be used for cardiovascular parameter acquisition. This paper is an extension of work originally presented at the 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
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Affiliation(s)
- Evgeniia Shchelkanova
- Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, 2-4 Hibikino, Wakamatsu, Kitakyushu, Fukuoka 808-0135, Japan;
- Correspondence:
| | - Liia Shchapova
- Omsk State Technical University, 11 Prospekt Mira, 644050 Omsk, Russia; (L.S.); (A.S.)
| | - Alexander Shchelkanov
- Omsk State Technical University, 11 Prospekt Mira, 644050 Omsk, Russia; (L.S.); (A.S.)
| | - Tomohiro Shibata
- Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, 2-4 Hibikino, Wakamatsu, Kitakyushu, Fukuoka 808-0135, Japan;
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Shchelkanova E, Shchelkanov A, Shchapova L, Shibata T. An Exploration of Blue PPG Signal Using a Novel Color Sensorbased PPG System. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:4414-4420. [PMID: 33018974 DOI: 10.1109/embc44109.2020.9175745] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Reflectance photoplethysmography is a widely employed method in personal health assessment devices. We developed a remote PPG system for obtaining the signal from palm skin. For the handle-mounted design, it is crucial to ensure accurate measurements under a range of skin temperatures. Under lower temperatures, green PPG was proven to provide superior signal quality in comparison to infrared PPG. However, since hemoglobin has maximum absorption in both green and blue parts of the spectrum, to ensure a stable performance of handle-mounted PPG system under different temperature conditions, it is worth exploring the PPG signal in both wavelengths. In our PPG system, TCS3472 Color Sensor was chosen as a photodetector. In distinction from video cameras with Bayer pattern, the TCS3472 has an equal number of R, G, and B sensing elements, thereby allowing the comparison of these wavelengths performance. Using the developed system, we compared green and blue PPG signals from six subjects in terms of amplitude and accuracy of heartbeat intervals estimation under two temperature conditions. According to our preliminary results, the performance of blue PPG was comparable to green PPG under both temperature conditions, suggesting further investigation with our system is worth conducting.
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