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Applied Sciences
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Applications of Photoplethysmography (PPG) in Healthcare and Wellbeing
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Applications of Photoplethysmography (PPG) in Healthcare and Wellbeing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: closed (20 June 2023) | Viewed by 8144

Special Issue Editors

Prof. Dr. Panicos Kyriacou

E-Mail Website
Guest Editor
Research Centre for Biomedical Engineering, School of Mathematics, Computer Science and Engineering, City, University of London, Northampton Square, London EC1V 0HB, UK
Interests: photoplethysmography; pulse oximetry; wearables; biosignal processing; optical sensors; cardiovascular monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Laszlo Hejjel

E-Mail Website1 Website2
Guest Editor
Heart Institute, University of Pécs, 7624 Pécs, Hungary
Interests: photoplethysmography; biomedical signal processing; heart rate variability; wearable monitoring

Special Issue Information

Dear Colleagues,

We are inviting submissions to the Special Issue on Applications of PPG in Healthcare and Wellbeing.

Photoplethysmography is considered a non-obtrusive optical method to detect subcutaneous blood volume variations related to the heart beat. Due to the wide availability of the technique in mobile or wearable smart devices, several applications and a huge amount of data can become accessible for analysis and further decision-making. Besides pulse rate monitoring in high-risk patients or during sport activities, an increasing number of studies are dealing with screening or follow-up in peripheral vascular disease, measuring blood pressure, pulse rate variability or arterial stiffness, and extracting respiratory rate, among others. The signal quality is crucial for the reliability and repeatability of the measurements, hence, the validation must precede commercialization.

This Special Issue is open to original research manuscripts on applications and validation of photoplethysmography, in particular in mobile/wearable electronics.

Prof. Dr. Panicos Kyriacou
Dr. Laszlo Hejjel
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • photoplethysmography
  • pulse oximetry
  • wearable monitoring
  • biosignal processing
  • optical sensors
  • cardiovascular monitoring

Published Papers (5 papers)

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Research

14 pages, 2155 KiB  
Article
The Effect of Device-Controlled Breathing on the Pulse Arrival Time and the Heart Rate Asymmetry Parameters in Healthy Volunteers
by Bella Eszter Ajtay, Szabolcs Béres and László Hejjel
Appl. Sci. 2023, 13(9), 5642; https://doi.org/10.3390/app13095642 - 04 May 2023
Viewed by 1088
Abstract
Background: The development of wearables has facilitated the monitoring of biomedical parameters in everyday life. One of the most common sensors of these gadgets is the photoplethysmograph (PPG); hence, the proper processing and interpretation of the PPG signal are essential. Besides pulse rate [...] Read more.
Background: The development of wearables has facilitated the monitoring of biomedical parameters in everyday life. One of the most common sensors of these gadgets is the photoplethysmograph (PPG); hence, the proper processing and interpretation of the PPG signal are essential. Besides pulse rate detection, these devices—together with an ECG—compute the pulse arrival time (PAT), from which the actual beat-to-beat blood pressure can be estimated. The heart rate shows asymmetrical accelerations and decelerations, quantified by the parameters of heart rate asymmetry (HRA). In the present study, we investigated the influences of different breathing-patterns on the PATs and HRA parameters. Methods: The authors evaluated 5 min simultaneous respiratory-, ECG- and PPG-signal recordings of 35 healthy, young volunteers specifically expressing the following breathing patterns: metronome-controlled inspiration, and both inspiration and expiration controlled at 1:1 and 1:2 ratios, respectively. The records were analyzed by HRVScan_Merge v3.2 software. The PAT values were calculated at eight different reference points. The HRA parameters and the PAT values at different breathing patterns were compared using the Friedman test and post hoc Wilcoxon paired-sample test. Results: Porta- and Guzik-indices significantly increased at 1:1 breathing compared to 1:2 and single-paced breathing. PATs increased significantly in dual-paced series compared to single-paced series at each reference point. Conclusion: Based on our results, the increased PATs at dual-paced versus single-paced breathing may indicate the involvement of cognitive functions. The symmetrical respiration ratio increases the heart rate symmetry; however, this effect is not detectable in the periphery through the PATs. Full article
(This article belongs to the Special Issue Applications of Photoplethysmography (PPG) in Healthcare and Wellbeing)
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12 pages, 446 KiB  
Article
Photoplethysmograph Based Biofeedback for Stress Reduction under Real-Life Conditions in Healthcare Frontline
by Emese Rudics, Ádám Nagy, József Dombi, Emőke Adrienn Hompoth, Zoltán Szabó, Rózsa Horváth, Mária Balogh, András Lovas, Vilmos Bilicki and István Szendi
Appl. Sci. 2023, 13(2), 835; https://doi.org/10.3390/app13020835 - 07 Jan 2023
Viewed by 1481
Abstract
Biofeedback (BF) therapy methods have evolved considerably in recent years. The best known is biofeedback training based on heart rate variability (HRV), which is used to treat asthma, depression, stress, and anxiety, among other conditions, by synchronizing the rhythm of breathing and heartbeat. [...] Read more.
Biofeedback (BF) therapy methods have evolved considerably in recent years. The best known is biofeedback training based on heart rate variability (HRV), which is used to treat asthma, depression, stress, and anxiety, among other conditions, by synchronizing the rhythm of breathing and heartbeat. The aim of our research was to develop a methodology and test its applicability using photoplethysmographs and smartphones to conduct biofeedback sessions for frontline healthcare workers under their everyday stressful conditions. Our hypothesis is that such a methodology is not only comparable to traditional training itself, but can make regular sessions increasingly effective in reducing real-life stress by providing appropriate feedback to the subject. The sample consisted 28 participants. Our proprietary method based on HRV biofeedback is able to determine the resonance frequency of the subjects, i.e., the number at which the pulse and respiration are in sync. Our research app then uses visual feedback to help the subject reach this frequency, which, if maintained, can significantly reduce stress. By comparing BF with Free relaxation, we conclude that BF does not lose effectiveness over time and repetitions, but increases it. This paper is our pilot study in which we discuss the method used to select participants, the development and operation of the protocol and algorithm, and present and analyze the results obtained. The showcased results demonstrate our hypothesis that purely IT-based relaxation techniques can effectively compete with spontaneous relaxation through biofeedback. This provides a basis for further investigation and development of the methodology and its widespread use to effectively reduce workplace stress. Full article
(This article belongs to the Special Issue Applications of Photoplethysmography (PPG) in Healthcare and Wellbeing)
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12 pages, 2588 KiB  
Article
In Vitro Evaluation of a Non-Invasive Photoplethysmography Based Intracranial Pressure Sensor
by Tomas Y. Abay, Justin P. Phillips, Christopher Uff, Maria Roldan and Panicos A. Kyriacou
Appl. Sci. 2023, 13(1), 534; https://doi.org/10.3390/app13010534 - 30 Dec 2022
Cited by 4 | Viewed by 1381
Abstract
Intracranial pressure (ICP) is an important measurement in the treatment of Traumatic Brain Injury (TBI). Currently, ICP can only be measured invasively, which exposes patients to operative risk and can only be performed by neurosurgeons. Hence, there is a significant need for a [...] Read more.
Intracranial pressure (ICP) is an important measurement in the treatment of Traumatic Brain Injury (TBI). Currently, ICP can only be measured invasively, which exposes patients to operative risk and can only be performed by neurosurgeons. Hence, there is a significant need for a non-invasive ICP technology. This paper describes the evaluation of a novel non-invasive intracranial pressure (nICP) monitor which uses the Photoplethysmogram (PPG) to measure the ICP. The monitor was evaluated in an in vitro model that simulated cerebral haemodynamics and allowed the controlled manipulation of ICP. A number of features from the PPG were extracted and utilised in a machine learning model to estimate ICP. Three separate measurements in which the ICP was varied were performed, and the estimated ICP (nICP) was compared with reference (invasive) ICP measurements. The ICP estimated by the nICP monitor was highly correlated with reference ICP measurements (Pearson’s correlation coefficient between 0.95 and 0.98). The nICP monitor also showed a low Root Mean Square Error from the reference ICP measure (3.12, 1.48, and 1.45 mmHg). Analysis of agreement by Bland and Altman also revealed good agreement between the two techniques. The optical nICP monitor was able to estimate the ICP non-invasively from an in vitro model simulating intracranial hypertension. The non-invasive ICP monitor showed very promising results which can set the base for further investigations. This work contributes significantly to the quest for non-invasive ICP monitoring in Traumatic Brain Injury (TBI), and paves the way for further research in this field. Full article
(This article belongs to the Special Issue Applications of Photoplethysmography (PPG) in Healthcare and Wellbeing)
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14 pages, 2076 KiB  
Article
Hypertension Detection Based on Photoplethysmography Signal Morphology and Machine Learning Techniques
by Lucian Evdochim, Dragoș Dobrescu, Stela Halichidis, Lidia Dobrescu and Silviu Stanciu
Appl. Sci. 2022, 12(16), 8380; https://doi.org/10.3390/app12168380 - 22 Aug 2022
Cited by 9 | Viewed by 2037
Abstract
In our modern digitalized world, hypertension detection represents a key feature that enables self-monitoring of cardiovascular parameters, using a wide range of smart devices. Heart rate and blood oxygen saturation rate are some of the most important ones, easily computed by wearable products [...] Read more.
In our modern digitalized world, hypertension detection represents a key feature that enables self-monitoring of cardiovascular parameters, using a wide range of smart devices. Heart rate and blood oxygen saturation rate are some of the most important ones, easily computed by wearable products that are provided by the photoplethysmography (PPG) technique. Therefore, this low-cost technology has opened a new horizon for health monitoring in the last decade. Another important parameter is blood pressure, a major predictor for cardiovascular characterization and health related events. Analyzing only PPG signal morphology and combining the medical observation with machine learning (ML) techniques, this paper develops a hypertension diagnosis tool, named the ANC Test™. During the development process, distinguishable characteristics have been observed among certain waveforms and certain types of patients that leads to an increased confidence level of the algorithm. The test was enchanted by machine learning models to improve blood pressure class detection between systolic normotensive and hypertensive patients. A total of 359 individual recordings were manually selected to build reference signals using open-source available databases. During the development and testing phases, different ML models accuracy of detecting systolic hypertension scored in many cases around 70% with a maximum value of 72.9%. This was resulted from original waveform classification into four main classes with an easy-to-understand nomenclature. An important limitation during the recording processing phase was given by a different PPG acquisition standard among the consulted free available databases. Full article
(This article belongs to the Special Issue Applications of Photoplethysmography (PPG) in Healthcare and Wellbeing)
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26 pages, 3662 KiB  
Article
Photoplethysmography-Based Pulse Rate Variability and Haemodynamic Changes in the Absence of Heart Rate Variability: An In-Vitro Study
by Elisa Mejía-Mejía and Panicos A. Kyriacou
Appl. Sci. 2022, 12(14), 7238; https://doi.org/10.3390/app12147238 - 18 Jul 2022
Viewed by 1204
Abstract
Pulse rate variability (PRV), measured from pulsatile signals such as the photoplethysmogram (PPG), has been largely used in recent years as a surrogate of heart rate variability (HRV), which is measured from electrocardiograms (ECG). However, different studies have shown that PRV does not [...] Read more.
Pulse rate variability (PRV), measured from pulsatile signals such as the photoplethysmogram (PPG), has been largely used in recent years as a surrogate of heart rate variability (HRV), which is measured from electrocardiograms (ECG). However, different studies have shown that PRV does not always replicate HRV as there are multiple factors that could affect their relationship, such as respiration and pulse transit time. In this study, an in-vitro model was developed for the simulation of the upper-circulatory system, and PPG signals were acquired from it when haemodynamic changes were induced. PRV was obtained from these signals and time-domain, frequency-domain and non-linear indices were extracted. Factorial analyses were performed to understand the effects of changing blood pressure and flow on PRV indices in the absence of HRV. Results showed that PRV indices are affected by these haemodynamic changes and that these may explain some of the differences between HRV and PRV. Future studies should aim to replicate these results in healthy volunteers and patients, as well as to include the HRV information in the in-vitro model for a more profound understanding of these differences. Full article
(This article belongs to the Special Issue Applications of Photoplethysmography (PPG) in Healthcare and Wellbeing)
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