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Nissen M, Flaucher M, Jaeger KM, Huebner H, Danzberger N, Titzmann A, Pontones CA, Fasching PA, Eskofier BM, Leutheuser H. WebPPG: Feasibility and Usability of Self-Performed, Browser-Based Smartphone Photoplethysmography. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082860 DOI: 10.1109/embc40787.2023.10340204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Smartphones enable and facilitate biomedical studies as they allow the recording of various biomedical signals, including photoplethysmograms (PPG). However, user engagement rates in mobile health studies are reduced when an application (app) needs to be installed. This could be alleviated by using installation-free web apps. We evaluate the feasibility of browser-based PPG recording, conducting the first usability study on smartphone-based PPG. We present an at-home study using a web app and library for PPG recording using the rear camera and flash. The underlying library is freely made available to researchers. 25 Android users participated, using their own smartphones. The study consisted of a demographic and anamnestic questionnaire, the signal recording itself (60 s), and a consecutive usability questionnaire. After filtering, heart rate was extracted (14/17 successful), signal-to-noise ratios assessed (0.64 ± 0.50 dB, mean ± standard deviation), and quality was visually inspected (12/17 usable for diagnosis). Recording was not supported in 9 cases. This was due to the browser's insufficient support for the flash light API. The app received a System Usability Scale score of 82 ± 9, which is above the 90th percentile. Overall, browser flash light support is the main limiting factor for broad device support. Thus, browser-based PPG is not yet widely applicable, although most participants feel comfortable with the recording itself. The utilization of the user-facing camera might represent a more promising approach. This study contributes to the development of low-barrier, user-friendly, installation-free smartphone signal acquisition. This enables profound, comprehensive data collection for research and clinical practice.Clinical relevance- WebPPG offers low-barrier remote diagnostic capabilities without the need for app installation.
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Saul M, Rostami S. Assessing performance of artificial neural networks and re-sampling techniques for healthcare datasets. Health Informatics J 2022; 28:14604582221087109. [PMID: 35357976 DOI: 10.1177/14604582221087109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Re-sampling methods to solve class imbalance problems have shown to improve classification accuracy by mitigating the bias introduced by differences in class size. However, it is possible that a model which uses a specific re-sampling technique prior to Artificial neural networks (ANN) training may not be suitable for aid in classifying varied datasets from the healthcare industry. Five healthcare-related datasets were used across three re-sampling conditions: under-sampling, over-sampling and combi-sampling. Within each condition, different algorithmic approaches were applied to the dataset and the results were statistically analysed for a significant difference in ANN performance. The combi-sampling condition showed that four out of the five datasets did not show significant consistency for the optimal re-sampling technique between the f1-score and Area Under the Receiver Operating Characteristic Curve performance evaluation methods. Contrarily, the over-sampling and under-sampling condition showed all five datasets put forward the same optimal algorithmic approach across performance evaluation methods. Furthermore, the optimal combi-sampling technique (under-, over-sampling and convergence point), were found to be consistent across evaluation measures in only two of the five datasets. This study exemplifies how discrete ANN performances on datasets from the same industry can occur in two ways: how the same re-sampling technique can generate varying ANN performance on different datasets, and how different re-sampling techniques can generate varying ANN performance on the same dataset.
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Estimation of Motion and Respiratory Characteristics during the Meditation Practice Based on Video Analysis. SENSORS 2021; 21:s21113771. [PMID: 34072291 PMCID: PMC8199391 DOI: 10.3390/s21113771] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 05/20/2021] [Accepted: 05/25/2021] [Indexed: 12/27/2022]
Abstract
Meditation practice is mental health training. It helps people to reduce stress and suppress negative thoughts. In this paper, we propose a camera-based meditation evaluation system, that helps meditators to improve their performance. We rely on two main criteria to measure the focus: the breathing characteristics (respiratory rate, breathing rhythmicity and stability), and the body movement. We introduce a contactless sensor to measure the respiratory rate based on a smartphone camera by detecting the chest keypoint at each frame, using an optical flow based algorithm to calculate the displacement between frames, filtering and de-noising the chest movement signal, and calculating the number of real peaks in this signal. We also present an approach to detecting the movement of different body parts (head, thorax, shoulders, elbows, wrists, stomach and knees). We have collected a non-annotated dataset for meditation practice videos consists of ninety videos and the annotated dataset consists of eight videos. The non-annotated dataset was categorized into beginner and professional meditators and was used for the development of the algorithm and for tuning the parameters. The annotated dataset was used for evaluation and showed that human activity during meditation practice could be correctly estimated by the presented approach and that the mean absolute error for the respiratory rate is around 1.75 BPM, which can be considered tolerable for the meditation application.
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Lei R, Ling BWK, Feng P, Chen J. Estimation of Heart Rate and Respiratory Rate from PPG Signal Using Complementary Ensemble Empirical Mode Decomposition with both Independent Component Analysis and Non-Negative Matrix Factorization. SENSORS (BASEL, SWITZERLAND) 2020; 20:E3238. [PMID: 32517226 PMCID: PMC7309083 DOI: 10.3390/s20113238] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 06/03/2020] [Accepted: 06/04/2020] [Indexed: 11/24/2022]
Abstract
This paper proposes a framework combining the complementary ensemble empirical mode decomposition with both the independent component analysis and the non-negative matrix factorization for estimating both the heart rate and the respiratory rate from the photoplethysmography (PPG) signal. After performing the complementary ensemble empirical mode decomposition on the PPG signal, a finite number of intrinsic mode functions are obtained. Then, these intrinsic mode functions are divided into two groups to perform the further analysis via both the independent component analysis and the non-negative matrix factorization. The surrogate cardiac signal related to the heart activity and another surrogate respiratory signal related to the respiratory activity are reconstructed to estimate the heart rate and the respiratory rate, respectively. Finally, different records of signals acquired from the Medical Information Mart for Intensive Care database downloaded from the Physionet Automated Teller Machine (ATM) data bank are employed for demonstrating the outperformance of our proposed method. The results show that our proposed method outperforms both the digital filtering approach and the conventional empirical mode decomposition based methods in terms of reconstructing both the surrogate cardiac signal and the respiratory signal from the PPG signal as well as both achieving the higher accuracy and the higher reliability for estimating both the heart rate and the respiratory rate.
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Affiliation(s)
| | - Bingo Wing-Kuen Ling
- School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China; (R.L.); (P.F.); (J.C.)
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L'Her E, N'Guyen QT, Pateau V, Bodenes L, Lellouche F. Photoplethysmographic determination of the respiratory rate in acutely ill patients: validation of a new algorithm and implementation into a biomedical device. Ann Intensive Care 2019; 9:11. [PMID: 30666472 PMCID: PMC6340913 DOI: 10.1186/s13613-019-0485-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Accepted: 01/09/2019] [Indexed: 11/30/2022] Open
Abstract
Background Respiratory rate is among the first vital signs to change in deteriorating patients. The aims of this study were to evaluate the accuracy of respiratory rate measurements using a specifically dedicated reflection-mode photoplethysmographic signal analysis in a pathological condition (PPG-RR) and to validate its implementation within medical devices. Methods This study is derived from a data mining project, including all consecutive patients admitted to our ICU (ReaSTOC study, ClinicalTrials.gov identifier: NCT02893462). During the evaluation phase of the algorithm, PPG-RR calculations were retrospectively performed on PPG waveforms extracted from the data warehouse and compared with RR reference values. During the prospective phase, PPG-RR calculations were automatically and continuously performed using a dedicated device (FreeO2, Oxynov, Québec, QC, Canada). In all phases, reference RR was measured continuously using electrical thoracic impedance and chronometric evaluation (Manual-RR) over a 30-s period. Results In total, 201 ICU patients’ recordings (SAPS II 51.7 ± 34.6) were analysed during the retrospective evaluation phase, most of them being admitted for a respiratory failure and requiring invasive mechanical ventilation. PPG-RR determination was available in 95.5% cases, similar to reference (22 ± 4 vs. 22 ± 5 c/min, respectively; p = 1), and well correlated with reference values (R = 0.952; p < 0.0001), with a low bias (0.1 b/min) and deviation (± 3.5 b/min). Prospective estimation of the PPG-RR on 30 ICU patients’ recordings was well correlated with the reference method (Manual-RR; r = 0.78; p < 0.001). Comparison of the methods depicted a low bias (0.5 b/min) and acceptable deviation (< ± 5.5 b/min). Conclusion According to our results, PPG-RR is an interesting approach for ventilation monitoring, as this technique would make simultaneous monitoring of respiratory rate and arterial oxygen saturation possible, thus minimizing the number of sensors attached to the patient. Trial registry number ClinicalTrials.gov identifier NCT02893462
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Affiliation(s)
- Erwan L'Her
- Réanimation Médicale, LATIM INSERM UMR 1101, CHRU de la Cavale Blanche, Bvd Tanguy-Prigent, 22 rue Camille Desmoulins, 29609, Brest Cedex, France. .,Médecine Intensive et Réanimation, CHRU de la Cavale Blanche, Bvd Tanguy-Prigent, 29609, Brest Cedex, France. .,Centre de Recherche de l'Institut Universitaire de Cardiologie et de Pneumologie de Québec, 2725 Ch Ste-Foy, Quebec, QC, G1V 4G5, Canada.
| | - Quang-Thang N'Guyen
- Oxynov Inc, Technopole Brest Iroise, 135 rue Claude Chappe, 29280, Plouzané, France
| | - Victoire Pateau
- Réanimation Médicale, LATIM INSERM UMR 1101, CHRU de la Cavale Blanche, Bvd Tanguy-Prigent, 22 rue Camille Desmoulins, 29609, Brest Cedex, France.,Oxynov Inc, Technopole Brest Iroise, 135 rue Claude Chappe, 29280, Plouzané, France
| | - Laetitia Bodenes
- Médecine Intensive et Réanimation, CHRU de la Cavale Blanche, Bvd Tanguy-Prigent, 29609, Brest Cedex, France
| | - François Lellouche
- Centre de Recherche de l'Institut Universitaire de Cardiologie et de Pneumologie de Québec, 2725 Ch Ste-Foy, Quebec, QC, G1V 4G5, Canada
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Charlton PH, Birrenkott DA, Bonnici T, Pimentel MAF, Johnson AEW, Alastruey J, Tarassenko L, Watkinson PJ, Beale R, Clifton DA. Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review. IEEE Rev Biomed Eng 2017; 11:2-20. [PMID: 29990026 PMCID: PMC7612521 DOI: 10.1109/rbme.2017.2763681] [Citation(s) in RCA: 127] [Impact Index Per Article: 18.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Breathing rate (BR) is a key physiological parameter used in a range of clinical settings. Despite its diagnostic and prognostic value, it is still widely measured by counting breaths manually. A plethora of algorithms have been proposed to estimate BR from the electrocardiogram (ECG) and pulse oximetry (photoplethysmogram, PPG) signals. These BR algorithms provide opportunity for automated, electronic, and unobtrusive measurement of BR in both healthcare and fitness monitoring. This paper presents a review of the literature on BR estimation from the ECG and PPG. First, the structure of BR algorithms and the mathematical techniques used at each stage are described. Second, the experimental methodologies that have been used to assess the performance of BR algorithms are reviewed, and a methodological framework for the assessment of BR algorithms is presented. Third, we outline the most pressing directions for future research, including the steps required to use BR algorithms in wearable sensors, remote video monitoring, and clinical practice.
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Affiliation(s)
- Peter H. Charlton
- Department of Biomedical Engineering, King’s College London, London SE1 7EH, U.K., and also with the Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, U.K
| | - Drew A. Birrenkott
- Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, U.K
| | - Timothy Bonnici
- Nuffield Department of Medicine, University of Oxford, Oxford OX3 9DU, U.K., and also with the Department of Asthma, Allergy, and Lung Biology, King’s College London, London SE1 7EH, U.K
| | | | - Alistair E. W. Johnson
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Jordi Alastruey
- Department of Biomedical Engineering, King’s College London, London SE1 7EH, U.K
| | - Lionel Tarassenko
- Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, U.K
| | - Peter J. Watkinson
- Kadoorie Centre for Critical Care Research and Education, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, U.K
| | - Richard Beale
- Department of Asthma, Allergy and Lung Biology, King’s College London, London SE1 7EH, U.K
| | - David A. Clifton
- Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, U.K
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Motin MA, Karmakar CK, Palaniswami M. An EEMD-PCA approach to extract heart rate, respiratory rate and respiratory activity from PPG signal. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2016:3817-3820. [PMID: 28269118 DOI: 10.1109/embc.2016.7591560] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The pulse oximeter's photoplethysmographic (PPG) signals, measure the local variations of blood volume in tissues, reflecting the peripheral pulse modulated by cardiac activity, respiration and other physiological effects. Therefore, PPG can be used to extract the vital cardiorespiratory signals like heart rate (HR), respiratory rate (RR) and respiratory activity (RA) and this will reduce the number of sensors connected to the patient's body for recording vital signs. In this paper, we propose an algorithm based on ensemble empirical mode decomposition with principal component analysis (EEMD-PCA) as a novel approach to estimate HR, RR and RA simultaneously from PPG signal. To examine the performance of the proposed algorithm, we used 45 epochs of PPG, electrocardiogram (ECG) and respiratory signal extracted from the MIMIC database (Physionet ATM data bank). The ECG and capnograph based respiratory signal were used as the ground truth and several metrics such as magnitude squared coherence (MSC), correlation coefficients (CC) and root mean square (RMS) error were used to compare the performance of EEMD-PCA algorithm with most of the existing methods in the literature. Results of EEMD-PCA based extraction of HR, RR and RA from PPG signal showed that the median RMS error (quartiles) obtained for RR was 0 (0, 0.89) breaths/min, for HR was 0.62 (0.56, 0.66) beats/min and for RA the average value of MSC and CC was 0.95 and 0.89 respectively. These results illustrated that the proposed EEMD-PCA approach is more accurate in estimating HR, RR and RA than other existing methods.
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Motin MA, Karmakar CK, Palaniswami M. Ensemble Empirical Mode Decomposition With Principal Component Analysis: A Novel Approach for Extracting Respiratory Rate and Heart Rate From Photoplethysmographic Signal. IEEE J Biomed Health Inform 2017; 22:766-774. [PMID: 28287994 DOI: 10.1109/jbhi.2017.2679108] [Citation(s) in RCA: 66] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The photoplethysmographic (PPG) signal measures the local variations of blood volume in tissues, reflecting the peripheral pulse modulated by cardiac activity, respiration, and other physiological effects. Therefore, PPG can be used to extract the vital cardiorespiratory signals like heart rate (HR), and respiratory rate (RR) and this will reduce the number of sensors connected to the patient's body for recording these vital signs. In this paper, we propose an algorithm based on ensemble empirical mode decomposition with principal component analysis (EEMD-PCA) as a novel approach to estimate HR and RR simultaneously from PPG signal. To examine the performance of the proposed algorithm, we used 310 (from 35 subjects) and 632 (from 42 subjects) epochs of simultaneously recorded electrocardiogram, PPG, and respiratory signal extracted from MIMIC (Physionet ATM data bank) and Capnobase database, respectively. Results of EEMD-PCA-based extraction of HR and RR from PPG signal showed that the median RMS error (1st and 3rd quartiles) obtained in MIMIC data set for RR was 0.89 (0, 1.78) breaths/min, for HR was 0.57 (0.30, 0.71) beats/min and in Capnobase data set it was 2.77 (0.50, 5.9) breaths/min and 0.69 (0.54, 1.10) beats/min for RR and HR, respectively. These results illustrated that the proposed EEMD-PCA approach is more accurate in estimating HR and RR than other existing methods. Efficient and reliable extraction of HR and RR from the pulse oximeter's PPG signal will help patients for monitoring HR and RR with low cost and less discomfort.
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Addison PS. Respiratory effort from the photoplethysmogram. Med Eng Phys 2017; 41:9-18. [PMID: 28126420 DOI: 10.1016/j.medengphy.2016.12.010] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2016] [Revised: 12/19/2016] [Accepted: 12/21/2016] [Indexed: 11/17/2022]
Abstract
The potential for a simple, non-invasive measure of respiratory effort based on the pulse oximeter signal - the photoplethysmogram or 'pleth' - was investigated in a pilot study. Several parameters were developed based on a variety of manifestations of respiratory effort in the signal, including modulation changes in amplitude, baseline, frequency and pulse transit times, as well as distinct baseline signal shifts. Thirteen candidate parameters were investigated using data from healthy volunteers. Each volunteer underwent a series of controlled respiratory effort maneuvers at various set flow resistances and respiratory rates. Six oximeter probes were tested at various body sites. In all, over three thousand pleth-based effort-airway pressure (EP) curves were generated across the various airway constrictions, respiratory efforts, respiratory rates, subjects, probe sites, and the candidate parameters considered. Regression analysis was performed to determine the existence of positive monotonic relationships between the respiratory effort parameters and resulting airway pressures. Six of the candidate parameters investigated exhibited a distinct positive relationship (p<0.001 across all probes tested) with increasing upper airway pressure repeatable across the range of respiratory rates and flow constrictions studied. These were: the three fundamental modulations in amplitude (AM-Effort), baseline (BM-Effort) and respiratory sinus arrhythmia (RSA-Effort); two pulse transit time modulations - one using a pulse oximeter probe and an ECG (P2E-Effort) and the other using two pulse oximeter probes placed at different peripheral body sites (P2-Effort); and baseline shifts in heart rate, (BL-HR-Effort). In conclusion, a clear monotonic relationship was found between several pleth-based parameters and imposed respiratory loadings at the mouth across a range of respiratory rates and flow constrictions. The results suggest that the pleth may provide a measure of changing upper airway dynamics indicative of the effort to breathe.
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Affiliation(s)
- Paul S Addison
- Minimally Invasive Therapies Group, Medtronic, The Technopole Centre, Edinburgh EH26 0PJ, Scotland, United Kingdom .
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Addison PS. Identifying stable phase coupling associated with cerebral autoregulation using the synchrosqueezed cross-wavelet transform and low oscillation morlet wavelets. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2015:5960-3. [PMID: 26737649 DOI: 10.1109/embc.2015.7319749] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
A novel method of identifying stable phase coupling behavior of two signals within the wavelet transform time-frequency plane is presented. The technique employs the cross-wavelet transform to provide a map of phase coupling followed by synchrosqueezing to collect the stable phase regime information. The resulting synchrosqueezed cross-wavelet transform method (Synchro-CrWT) is illustrated using a synthetic signal and then applied to the analysis of the relationship between biosignals used in the analysis of cerebral autoregulation function.
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Shah SA, Fleming S, Thompson M, Tarassenko L. Respiratory rate estimation during triage of children in hospitals. J Med Eng Technol 2016; 39:514-24. [PMID: 26548638 DOI: 10.3109/03091902.2015.1105316] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Accurate assessment of a child's health is critical for appropriate allocation of medical resources and timely delivery of healthcare in Emergency Departments. The accurate measurement of vital signs is a key step in the determination of the severity of illness and respiratory rate is currently the most difficult vital sign to measure accurately. Several previous studies have attempted to extract respiratory rate from photoplethysmogram (PPG) recordings. However, the majority have been conducted in controlled settings using PPG recordings from healthy subjects. In many studies, manual selection of clean sections of PPG recordings was undertaken before assessing the accuracy of the signal processing algorithms developed. Such selection procedures are not appropriate in clinical settings. A major limitation of AR modelling, previously applied to respiratory rate estimation, is an appropriate selection of model order. This study developed a novel algorithm that automatically estimates respiratory rate from a median spectrum constructed applying multiple AR models to processed PPG segments acquired with pulse oximetry using a finger probe. Good-quality sections were identified using a dynamic template-matching technique to assess PPG signal quality. The algorithm was validated on 205 children presenting to the Emergency Department at the John Radcliffe Hospital, Oxford, UK, with reference respiratory rates up to 50 breaths per minute estimated by paediatric nurses. At the time of writing, the authors are not aware of any other study that has validated respiratory rate estimation using data collected from over 200 children in hospitals during routine triage.
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Affiliation(s)
- Syed Ahmar Shah
- a Department of Engineering Science , Institute of Biomedical Engineering, University of Oxford , Oxford , UK
| | - Susannah Fleming
- b Nuffield Department of Primary Care Health Sciences , University of Oxford , Oxford , UK , and
| | - Matthew Thompson
- c Department of Family Medicine , University of Washington , Seattle , WA , USA
| | - Lionel Tarassenko
- a Department of Engineering Science , Institute of Biomedical Engineering, University of Oxford , Oxford , UK
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Monitoring of Heart and Breathing Rates Using Dual Cameras on a Smartphone. PLoS One 2016; 11:e0151013. [PMID: 26963390 PMCID: PMC4786286 DOI: 10.1371/journal.pone.0151013] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2015] [Accepted: 02/23/2016] [Indexed: 11/19/2022] Open
Abstract
Some smartphones have the capability to process video streams from both the front- and rear-facing cameras simultaneously. This paper proposes a new monitoring method for simultaneous estimation of heart and breathing rates using dual cameras of a smartphone. The proposed approach estimates heart rates using a rear-facing camera, while at the same time breathing rates are estimated using a non-contact front-facing camera. For heart rate estimation, a simple application protocol is used to analyze the varying color signals of a fingertip placed in contact with the rear camera. The breathing rate is estimated from non-contact video recordings from both chest and abdominal motions. Reference breathing rates were measured by a respiration belt placed around the chest and abdomen of a subject; reference heart rates (HR) were determined using the standard electrocardiogram. An automated selection of either the chest or abdominal video signal was determined by choosing the signal with a greater autocorrelation value. The breathing rate was then determined by selecting the dominant peak in the power spectrum. To evaluate the performance of the proposed methods, data were collected from 11 healthy subjects. The breathing ranges spanned both low and high frequencies (6-60 breaths/min), and the results show that the average median errors from the reflectance imaging on the chest and the abdominal walls based on choosing the maximum spectral peak were 1.43% and 1.62%, respectively. Similarly, HR estimates were also found to be accurate.
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13
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Addison PS, Watson JN, Mestek ML, Ochs JP, Uribe AA, Bergese SD. Pulse oximetry-derived respiratory rate in general care floor patients. J Clin Monit Comput 2015; 29:113-20. [PMID: 24796734 PMCID: PMC4309914 DOI: 10.1007/s10877-014-9575-5] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2013] [Accepted: 04/02/2014] [Indexed: 11/02/2022]
Abstract
Respiratory rate is recognized as a clinically important parameter for monitoring respiratory status on the general care floor (GCF). Currently, intermittent manual assessment of respiratory rate is the standard of care on the GCF. This technique has several clinically-relevant shortcomings, including the following: (1) it is not a continuous measurement, (2) it is prone to observer error, and (3) it is inefficient for the clinical staff. We report here on an algorithm designed to meet clinical needs by providing respiratory rate through a standard pulse oximeter. Finger photoplethysmograms were collected from a cohort of 63 GCF patients monitored during free breathing over a 25-min period. These were processed using a novel in-house algorithm based on continuous wavelet-transform technology within an infrastructure incorporating confidence-based averaging and logical decision-making processes. The computed oximeter respiratory rates (RRoxi) were compared to an end-tidal CO2 reference rate (RRETCO2). RRETCO2 ranged from a lowest recorded value of 4.7 breaths per minute (brpm) to a highest value of 32.0 brpm. The mean respiratory rate was 16.3 brpm with standard deviation of 4.7 brpm. Excellent agreement was found between RRoxi and RRETCO2, with a mean difference of -0.48 brpm and standard deviation of 1.77 brpm. These data demonstrate that our novel respiratory rate algorithm is a potentially viable method of monitoring respiratory rate in GCF patients. This technology provides the means to facilitate continuous monitoring of respiratory rate, coupled with arterial oxygen saturation and pulse rate, using a single non-invasive sensor in low acuity settings.
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Affiliation(s)
- Paul S Addison
- Covidien Respiratory and Monitoring Solutions, Edinburgh, Scotland, UK,
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Wertheim D, Parsley C, Burgess S, Dakin C, Seddon P. Pulse oximetry plethysmogram analysis could help identify infants with possible apnoeas requiring full investigation. Acta Paediatr 2014; 103:e222-4. [PMID: 24471706 DOI: 10.1111/apa.12575] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2013] [Revised: 12/18/2013] [Accepted: 01/22/2014] [Indexed: 11/28/2022]
Affiliation(s)
- David Wertheim
- Faculty of Science, Engineering and Computing; Kingston University; Kingston UK
| | - Chloe Parsley
- Department of Respiratory and Sleep Medicine; Mater Children's Hospital; Brisbane Australia
| | - Scott Burgess
- Department of Respiratory and Sleep Medicine; Mater Children's Hospital; Brisbane Australia
| | - Carolyn Dakin
- Department of Respiratory and Sleep Medicine; Mater Children's Hospital; Brisbane Australia
| | - Paul Seddon
- Respiratory Unit; Royal Alexandra Children's Hospital; Brighton UK
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15
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Nam Y, Lee J, Chon KH. Respiratory Rate Estimation from the Built-in Cameras of Smartphones and Tablets. Ann Biomed Eng 2013; 42:885-98. [DOI: 10.1007/s10439-013-0944-x] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2013] [Accepted: 11/14/2013] [Indexed: 10/26/2022]
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Monitoring respiration in wheezy preschool children by pulse oximetry plethysmogram analysis. Med Biol Eng Comput 2013; 51:965-70. [PMID: 23543306 DOI: 10.1007/s11517-013-1068-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2012] [Accepted: 03/22/2013] [Indexed: 10/27/2022]
Abstract
The aim of this study was to investigate whether respiratory information can be derived from pulse oximetry plethysmogram (pleth) recordings in acutely wheezy preschool children. A digital pulse oximeter was connected via 'Bluetooth' to a notebook computer in order to acquire pleth data. Low pass filtering and frequency analysis were used to derive respiratory rate from the pleth trace; the ratio of heart rate to respiratory rate (HR/RR) was also calculated. Recordings were obtained during acute wheezy episodes in 18 children of median age 31 months and follow-up recordings from 16 of the children were obtained when they were wheeze-free. For the acutely wheezy children, frequency analysis of the pleth waveform was within 10 breaths/min of clinical assessment in 25 of 29 recordings in 15 children. For the follow-up measurements, frequency analysis of the pleth waveform showed similarly good agreement in recordings on 15 of the 16 children. Respiratory rate was higher (p < 0.001), and HR/RR ratio was lower (p = 0.03) during acute wheeze than at follow-up. This study suggests that respiratory rate can be derived from pleth traces in wheezy preschool children.
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Addison PS, Watson JN, Mestek ML, Mecca RS. Developing an algorithm for pulse oximetry derived respiratory rate (RR(oxi)): a healthy volunteer study. J Clin Monit Comput 2012; 26:45-51. [PMID: 22231359 PMCID: PMC3268017 DOI: 10.1007/s10877-011-9332-y] [Citation(s) in RCA: 64] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2011] [Accepted: 12/21/2011] [Indexed: 11/29/2022]
Abstract
Objective The presence of respiratory information within the pulse oximeter signal (PPG) is a well-documented phenomenon. However, extracting this information for the purpose of continuously monitoring respiratory rate requires: (1) the recognition of the multi-faceted manifestations of respiratory modulation components within the PPG and the complex interactions among them; (2) the implementation of appropriate advanced signal processing techniques to take full advantage of this information; and (3) the post-processing infrastructure to deliver a clinically useful reported respiratory rate to the end user. A holistic algorithmic approach to the problem is therefore required. We have developed the RROXI algorithm based on this principle and its performance on healthy subject trial data is described herein. Methods Finger PPGs were collected from a cohort of 139 healthy adult volunteers monitored during free breathing over an 8-min period. These were subsequently processed using a novel in-house algorithm based on continuous wavelet transform technology within an infrastructure incorporating weighted averaging and logical decision making processes. The computed oximeter respiratory rates (RRoxi) were then compared to an end-tidal CO2 reference rate (\documentclass[12pt]{minimal}
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\begin{document}$$ {\text{RR}}_{{{\text{ETCO}}_{ 2} }} $$\end{document}). Results\documentclass[12pt]{minimal}
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\begin{document}$$ {\text{RR}}_{{{\text{ETCO}}_{ 2} }} $$\end{document} ranged from a lowest recorded value of 2.97 breaths per min (br/min) to a highest value of 28.02 br/min. The mean rate was 14.49 br/min with standard deviation of 4.36 br/min. Excellent agreement was found between RRoxi and \documentclass[12pt]{minimal}
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\begin{document}$$ {\text{RR}}_{{{\text{ETCO}}_{ 2} }} $$\end{document}, with a mean difference of −0.23 br/min and standard deviation of 1.14 br/min. The two measures are tightly spread around the line of agreement with a strong correlation observable between them (R2 = 0.93). Conclusions These data indicate that RRoxi represents a viable technology for the measurement of respiratory rate of healthy individuals.
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Affiliation(s)
- Paul S Addison
- Advanced Research Group, Covidien Respiratory and Monitoring Solutions, Technopole Centre, Edinburgh, EH26 0PJ, UK.
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A Novel Approach to Monitor Nonstationary Dynamics in Physiological Signals: Application to Blood Pressure, Pulse Oximeter, and Respiratory Data. Ann Biomed Eng 2010; 38:3478-88. [DOI: 10.1007/s10439-010-0090-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2010] [Accepted: 05/27/2010] [Indexed: 11/26/2022]
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Lee J, Chon KH. An autoregressive model-based particle filtering algorithms for extraction of respiratory rates as high as 90 breaths per minute from pulse oximeter. IEEE Trans Biomed Eng 2010; 57:2158-67. [PMID: 20542761 DOI: 10.1109/tbme.2010.2051330] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
We present particle filtering (PF) algorithms for an accurate respiratory rate extraction from pulse oximeter recordings over a broad range: 12-90 breaths/min. These methods are based on an autoregressive (AR) model, where the aim is to find the pole angle with the highest magnitude as it corresponds to the respiratory rate. However, when SNR is low, the pole angle with the highest magnitude may not always lead to accurate estimation of the respiratory rate. To circumvent this limitation, we propose a probabilistic approach, using a sequential Monte Carlo method, named PF, which is combined with the optimal parameter search (OPS) criterion for an accurate AR model-based respiratory rate extraction. The PF technique has been widely adopted in many tracking applications, especially for nonlinear and/or non-Gaussian problems. We examine the performances of five different likelihood functions of the PF algorithm: the strongest neighbor, nearest neighbor (NN), weighted nearest neighbor (WNN), probability data association (PDA), and weighted probability data association (WPDA). The performance of these five combined OPS-PF algorithms was measured against a solely OPS-based AR algorithm for respiratory rate extraction from pulse oximeter recordings. The pulse oximeter data were collected from 33 healthy subjects with breathing rates ranging from 12 to 90 breaths/ min. It was found that significant improvement in accuracy can be achieved by employing particle filters, and that the combined OPS-PF employing either the NN or WNN likelihood function achieved the best results for all respiratory rates considered in this paper. The main advantage of the combined OPS-PF with either the NN or WNN likelihood function is that for the first time, respiratory rates as high as 90 breaths/min can be accurately extracted from pulse oximeter recordings.
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Affiliation(s)
- Jinseok Lee
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, USA.
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Dash S, Shelley KH, Silverman DG, Chon KH. Estimation of Respiratory Rate From ECG, Photoplethysmogram, and Piezoelectric Pulse Transducer Signals: A Comparative Study of Time–Frequency Methods. IEEE Trans Biomed Eng 2010; 57:1099-107. [DOI: 10.1109/tbme.2009.2038226] [Citation(s) in RCA: 61] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Fleming S, Tarassenko L, Thompson M, Mant D. Non-invasive measurement of respiratory rate in children using the photoplethysmogram. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2009; 2008:1886-9. [PMID: 19163057 DOI: 10.1109/iembs.2008.4649554] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Respiratory rate is recognised as a valuable predictor of the severity of illness in children, but it is not currently feasible to measure this automatically in a triage environment. Autoregressive modelling on data from the pulse oximeter photoplethysmogram has the potential to introduce automated breathing measurement into the realm of paediatric triage. Using autoregressive modelling, it is shown that respiratory rate can be extracted from the paediatric photoplethysmogram with a mean error of 3.4 breaths per minute.
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Affiliation(s)
- Susannah Fleming
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, OX3 7DQ, UK
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Chon KH, Dash S, Ju K. Estimation of respiratory rate from photoplethysmogram data using time-frequency spectral estimation. IEEE Trans Biomed Eng 2009; 56:2054-63. [PMID: 19369147 DOI: 10.1109/tbme.2009.2019766] [Citation(s) in RCA: 102] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
We present a new method that uses the pulse oximeter signal to estimate the respiratory rate. The method uses a recently developed time-frequency spectral estimation method, variable-frequency complex demodulation (VFCDM), to identify frequency modulation (FM) of the photoplethysmogram waveform. This FM has a measurable periodicity, which provides an estimate of the respiration period. We compared the performance of VFCDM to the continuous wavelet transform (CWT) and autoregressive (AR) model approaches. The CWT method also utilizes the respiratory sinus arrhythmia effect as represented by either FM or AM to estimate respiratory rates. Both CWT and AR model methods have been previously shown to provide reasonably good estimates of breathing rates that are in the normal range (12-26 breaths/min). However, to our knowledge, breathing rates higher than 26 breaths/min and the real-time performance of these algorithms are yet to be tested. Our analysis based on 15 healthy subjects reveals that the VFCDM method provides the best results in terms of accuracy (smaller median error), consistency (smaller interquartile range of the median value), and computational efficiency (less than 0.3 s on 1 min of data using a MATLAB implementation) to extract breathing rates that varied from 12-36 breaths/min.
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Affiliation(s)
- Ki H Chon
- Department of Biomedical Engineering, State University of New York (SUNY) at Stony Brook, Stony Brook, NY 11794 USA.
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Clifton D, Douglas JG, Addison PS, Watson JN. Measurement of respiratory rate from the photoplethysmogram in chest clinic patients. J Clin Monit Comput 2006; 21:55-61. [PMID: 17131084 DOI: 10.1007/s10877-006-9059-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2006] [Accepted: 10/30/2006] [Indexed: 10/23/2022]
Abstract
OBJECTIVE We studied the application of our algorithm for the robust extraction of respiratory information from the pulse oximeter signal acquired from a selection of patients attending the chest clinic. METHODS Photoplethysmograms were obtained from 16 individuals: 13 patients with various conditions in the respiratory ward and three healthy subjects. Wavelet transforms were generated from which respiratory information was extracted to obtain a measure of respiratory rate. This measured rate was compared with the respiratory rate determined by one of a variety of other means (a digital end tidal CO(2) signal, the output from a non-invasive ventilation device, or a switch actuated by the patient or observer.) RESULTS Respiratory rates varied from 6.2 to 35.8 breaths per minute (bpm). The oximeter rate determined through our method matched the marker rate obtained for all patients to within 1 bpm. CONCLUSION The technique allows the measurement of respiratory rate directly from the photoplethysmogram of a pulse oximeter, and leads the way for development of a simple non-invasive combined respiration and saturation monitor useful for patients with all forms of breathlessness.
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Affiliation(s)
- David Clifton
- CardioDigital Ltd, Elvingston Science Centre, Glasdmuir, East Lothian, EH33 1EH, Scotland, UK
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