1
|
Fan J, Mei J, Yang Y, Lu J, Wang Q, Yang X, Chen G, Wang R, Han Y, Sheng R, Wang W, Ding F. Sleep-phasic heart rate variability predicts stress severity: Building a machine learning-based stress prediction model. Stress Health 2024; 40:e3386. [PMID: 38411360 DOI: 10.1002/smi.3386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 12/20/2023] [Accepted: 02/14/2024] [Indexed: 02/28/2024]
Abstract
We propose a novel approach for predicting stress severity by measuring sleep phasic heart rate variability (HRV) using a smart device. This device can potentially be applied for stress self-screening in large populations. Using a Holter electrocardiogram (ECG) and a Huawei smart device, we conducted 24-h dual recordings of 159 medical workers working regular shifts. Based on photoplethysmography (PPG) and accelerometer signals acquired by the Huawei smart device, we sorted episodes of cyclic alternating pattern (CAP; unstable sleep), non-cyclic alternating pattern (NCAP; stable sleep), wakefulness, and rapid eye movement (REM) sleep based on cardiopulmonary coupling (CPC) algorithms. We further calculated the HRV indices during NCAP, CAP and REM sleep episodes using both the Holter ECG and smart-device PPG signals. We later developed a machine learning model to predict stress severity based only on the smart device data obtained from the participants along with a clinical evaluation of emotion and stress conditions. Sleep phasic HRV indices predict individual stress severity with better performance in CAP or REM sleep than in NCAP. Using the smart device data only, the optimal machine learning-based stress prediction model exhibited accuracy of 80.3 %, sensitivity 87.2 %, and 63.9 % for specificity. Sleep phasic heart rate variability can be accurately evaluated using a smart device and subsequently can be used for stress predication.
Collapse
Affiliation(s)
- Jingjing Fan
- Department of Cardiology and Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Junhua Mei
- Department of Cardiology and Department of Neurology, The First Hospital of Wuhan City, Wuhan, China
| | - Yuan Yang
- Department of Cardiology and Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiajia Lu
- Department of Cardiology and Department of Neurology, The First Hospital of Wuhan City, Wuhan, China
| | - Quan Wang
- Department of Cardiology and Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaoyun Yang
- Department of Cardiology and Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Guohua Chen
- Department of Cardiology and Department of Neurology, The First Hospital of Wuhan City, Wuhan, China
| | - Runsen Wang
- Huawei Technologies Co., Ltd., Shenzhen, China
| | - Yujia Han
- Huawei Technologies Co., Ltd., Shenzhen, China
| | - Rong Sheng
- Huawei Technologies Co., Ltd., Shenzhen, China
| | - Wei Wang
- Department of Cardiology and Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Fengfei Ding
- Department of Pharmacology, Shanghai Medical College, Fudan University, Shanghai, China
| |
Collapse
|
2
|
Katan M, Pearl O, Tzroya A, Duadi H, Fixler D. A Self-Calibrated Single Wavelength Biosensor for Measuring Oxygen Saturation. BIOSENSORS 2024; 14:132. [PMID: 38534239 DOI: 10.3390/bios14030132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 02/24/2024] [Accepted: 03/01/2024] [Indexed: 03/28/2024]
Abstract
Traditional methods for measuring blood oxygen use multiple wavelengths, which produce an intrinsic error due to ratiometric measurements. These methods assume that the absorption changes with the wavelength, but in fact the scattering changes as well and cannot be neglected. We found that if one measures in a specific angle around a cylindrical tissue, called the iso-pathlength (IPL) point, the reemitted light intensity is unaffected by the tissue's scattering. Therefore, the absorption can be isolated from the scattering, which allows the extraction of the subject's oxygen saturation. In this work, we designed an optical biosensor for reading the light intensity reemitted from the tissue, using a single light source and multiple photodetectors (PDs), with one of them in the IPL point's location. Using this bio-device, we developed a methodology to extract the arterial oxygen saturation using a single wavelength light source. We proved this method is not dependent on the light source and is applicable to different measurement locations on the body, with an error of 0.5%. Moreover, we tested thirty-eight males and females with the biosensor under normal conditions. Finally, we show the results of measuring subjects in a hypoxic chamber that simulates extreme conditions with low oxygen.
Collapse
Affiliation(s)
- Michal Katan
- Faculty of Engineering, Bar Ilan University, Ramat Gan 5290002, Israel
- The Institute of Nanotechnology and Advanced Materials, Bar Ilan University, Ramat Gan 5290002, Israel
| | - Ori Pearl
- Faculty of Engineering, Bar Ilan University, Ramat Gan 5290002, Israel
- The Institute of Nanotechnology and Advanced Materials, Bar Ilan University, Ramat Gan 5290002, Israel
| | - Alon Tzroya
- Faculty of Engineering, Bar Ilan University, Ramat Gan 5290002, Israel
- The Institute of Nanotechnology and Advanced Materials, Bar Ilan University, Ramat Gan 5290002, Israel
| | - Hamootal Duadi
- Faculty of Engineering, Bar Ilan University, Ramat Gan 5290002, Israel
- The Institute of Nanotechnology and Advanced Materials, Bar Ilan University, Ramat Gan 5290002, Israel
| | - Dror Fixler
- Faculty of Engineering, Bar Ilan University, Ramat Gan 5290002, Israel
- The Institute of Nanotechnology and Advanced Materials, Bar Ilan University, Ramat Gan 5290002, Israel
| |
Collapse
|
3
|
Stankoski S, Kiprijanovska I, Mavridou I, Nduka C, Gjoreski H, Gjoreski M. Breathing Rate Estimation from Head-Worn Photoplethysmography Sensor Data Using Machine Learning. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22062079. [PMID: 35336250 PMCID: PMC8951087 DOI: 10.3390/s22062079] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 03/02/2022] [Accepted: 03/05/2022] [Indexed: 05/20/2023]
Abstract
Breathing rate is considered one of the fundamental vital signs and a highly informative indicator of physiological state. Given that the monitoring of heart activity is less complex than the monitoring of breathing, a variety of algorithms have been developed to estimate breathing activity from heart activity. However, estimating breathing rate from heart activity outside of laboratory conditions is still a challenge. The challenge is even greater when new wearable devices with novel sensor placements are being used. In this paper, we present a novel algorithm for breathing rate estimation from photoplethysmography (PPG) data acquired from a head-worn virtual reality mask equipped with a PPG sensor placed on the forehead of a subject. The algorithm is based on advanced signal processing and machine learning techniques and includes a novel quality assessment and motion artifacts removal procedure. The proposed algorithm is evaluated and compared to existing approaches from the related work using two separate datasets that contains data from a total of 37 subjects overall. Numerous experiments show that the proposed algorithm outperforms the compared algorithms, achieving a mean absolute error of 1.38 breaths per minute and a Pearson's correlation coefficient of 0.86. These results indicate that reliable estimation of breathing rate is possible based on PPG data acquired from a head-worn device.
Collapse
Affiliation(s)
- Simon Stankoski
- Emteq Ltd., Brighton BN1 9SB, UK; (I.K.); (I.M.); (C.N.); (H.G.)
- Correspondence:
| | | | | | - Charles Nduka
- Emteq Ltd., Brighton BN1 9SB, UK; (I.K.); (I.M.); (C.N.); (H.G.)
| | - Hristijan Gjoreski
- Emteq Ltd., Brighton BN1 9SB, UK; (I.K.); (I.M.); (C.N.); (H.G.)
- Faculty of Electrical Engineering and Information Technologies, Ss. Cyril and Methodius University in Skopje, 1000 Skopje, North Macedonia
| | - Martin Gjoreski
- Faculty of Informatics, Università della Svizzera Italiana, 6900 Lugano, Switzerland;
| |
Collapse
|
4
|
Argüello Prada EJ, Bravo Gallego CA, Castillo García JF. On the development of an efficient, low-complexity and highly reproducible method for systolic peak detection. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102606] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
|
5
|
Dong K, Zhao L, Cai Z, Li Y, Li J, Liu C. An integrated framework for evaluation on typical ECG-derived respiration waveform extraction and respiration. Comput Biol Med 2021; 135:104593. [PMID: 34198043 DOI: 10.1016/j.compbiomed.2021.104593] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 06/05/2021] [Accepted: 06/17/2021] [Indexed: 10/21/2022]
Abstract
OBJECTIVE ECG-derived respiration (EDR) methods have been developed during the past decades to obtain respiration-relevant information. However, it is still necessary to compare the performance of these methods under uniform conditions for reasonable application. APPROACH In this paper, the performance of 10 feature-based EDR methods was evaluated comprehensively on three aspects: sampling rate, noise, and window length. The Fantasia database was used in this study, as it contained ECG signals and simultaneously measured respiration signals. The performance was quantified by two parameters: waveform correlation and breathing rate (BR) errors. MAIN RESULTS The BR errors of AMarea, AMQR, AMR were all below 2 beats per minute (bpm) when the sampling rate was above 150 Hz, while they decreased sharply by about 60% when the sampling rate was below 150 Hz. FMRR presented stable performance with an error below 2 bpm at different sampling rates. The effect of noise was obviously found in amplitude-based EDR methods, with the maximum decreased by about 40% in waveform correlation. For all EDR methods, significant increase of BR errors occurred with the window shorting from 32 s to 16 s in the frequency-based technique. In addition, about 30%-40% of the window cannot obtain the BR error, calculated based on the time-based technique, within an 8 s window. SIGNIFICANCE We proposed a comprehensive and integrated evaluation on typical ECG-derived respiration waveform extraction and respiration rate calculation, providing references for algorithm selection based on different requirements.
Collapse
Affiliation(s)
- Kejun Dong
- School of Information Science and Engineering, Southeast University, Nanjing, 210096, PR China; School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, PR China
| | - Li Zhao
- School of Information Science and Engineering, Southeast University, Nanjing, 210096, PR China.
| | - Zhipeng Cai
- School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, PR China
| | - Yuwen Li
- School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, PR China
| | - Jianqing Li
- School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, PR China
| | - Chengyu Liu
- School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, PR China.
| |
Collapse
|
6
|
Chen M, Zhu Q, Wu M, Wang Q. Modulation Model of the Photoplethysmography Signal for Vital Sign Extraction. IEEE J Biomed Health Inform 2021; 25:969-977. [PMID: 32750983 DOI: 10.1109/jbhi.2020.3013811] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
This paper introduces an amplitude and frequency modulation (AM-FM) model to characterize the photoplethysmography (PPG) signal. The model indicates that the PPG signal spectrum contains one dominant frequency component - the heart rate (HR), which is guarded by two weaker frequency components on both sides; the distance from the dominant component to the guard components represents the respiratory rate (RR). Based on this model, an efficient algorithm is proposed to estimate both HR and RR by searching for the dominant frequency component and two guard components. The proposed method is performed in the frequency domain to estimate RR, which is more robust to additive noise than the prior art based on temporal features. Experiments were conducted on two types of PPG signals collected with a contact sensor (an oximeter) and a contactless visible imaging sensor (a color camera), respectively. The PPG signal from the contactless sensor is much noisier than the signal from the contact sensor. The experimental results demonstrate the effectiveness of the proposed algorithm, including under relatively noisy scenarios.
Collapse
|
7
|
Maurya L, Kaur P, Chawla D, Mahapatra P. Non-contact breathing rate monitoring in newborns: A review. Comput Biol Med 2021; 132:104321. [PMID: 33773194 DOI: 10.1016/j.compbiomed.2021.104321] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 03/04/2021] [Accepted: 03/04/2021] [Indexed: 02/07/2023]
Abstract
The neonatal period - the first 4 weeks of life - is the most critical time for a child's survival. Breathing rate is a vital indicator of the health condition and requires continuous monitoring in case of sickness or preterm birth. Breathing movements can be counted by contact and non-contact methods. In the case of newborn infants, the non-contact breathing rate monitoring need is high, as a contact-based approach may interfere while providing care and is subject to interference by non-breathing movements. This review article delivers a factual summary, and describes the methods and processing involved in non-contact based breathing rate monitoring. The article also provides the advantages, limitations, and clinical applications of these methods. Additionally, signal processing, feasibility, and future direction of different non-contact neonatal breathing rate monitoring are discussed.
Collapse
Affiliation(s)
- Lalit Maurya
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India; CSIR-Central Scientific Instruments Organisation (CSIR-CSIO), Sector 30-C, Chandigarh, 160030, India.
| | - Pavleen Kaur
- CSIR-Central Scientific Instruments Organisation (CSIR-CSIO), Sector 30-C, Chandigarh, 160030, India; Department of Biomedical Engineering, SRM University Delhi NCR, Sonepat, Haryana, India.
| | - Deepak Chawla
- Department of Neonatology, Government Medical College & Hospital (GMCH), Chandigarh, 160030, India.
| | - Prasant Mahapatra
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India; CSIR-Central Scientific Instruments Organisation (CSIR-CSIO), Sector 30-C, Chandigarh, 160030, India.
| |
Collapse
|
8
|
Sjöstrand S, Evertsson M, Jansson T. Magnetomotive Ultrasound Imaging Systems: Basic Principles and First Applications. ULTRASOUND IN MEDICINE & BIOLOGY 2020; 46:2636-2650. [PMID: 32753288 DOI: 10.1016/j.ultrasmedbio.2020.06.014] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 04/29/2020] [Accepted: 06/19/2020] [Indexed: 06/11/2023]
Abstract
This review discusses magnetomotive ultrasound, which is an emerging technique that uses superparamagnetic iron oxide nanoparticles as a contrast agent. The key advantage of using nanoparticle-based contrast agents is their ability to reach extravascular targets, whereas commercial contrast agents for ultrasound comprise microbubbles confined to the blood stream. This also extends possibilities for molecular imaging, where the contrast agent is labeled with specific targeting molecules (e.g., antibodies) so that pathologic tissue may be visualized directly. The principle of action is that an external time-varying magnetic field acts to displace the nanoparticles lodged in tissue and thereby their immediate surrounding. This movement is then detected with ultrasound using frequency- or time-domain analysis of echo data. As a contrast agent already approved for magnetic resonance imaging (MRI) by the US Food and Drug Administration, there is a shorter path to clinical translation, although safety studies of magnetomotion are necessary, especially if particle design is altered to affect biodistribution or signal strength. The external modulated magnetic field may be generated by electromagnets, permanent magnets, or a combination of the two. The induced nanoparticle motion may also reveal mechanical material properties of tissue, healthy or diseased, one of several interesting potential future aspects of the technique.
Collapse
Affiliation(s)
- Sandra Sjöstrand
- Department of Biomedical Engineering, Lund University, Lund, Sweden
| | - Maria Evertsson
- Department of Clinical Sciences Lund/Biomedical Engineering, Lund University, Lund, Sweden
| | - Tomas Jansson
- Department of Clinical Sciences Lund/Biomedical Engineering, Lund University, Lund, Sweden; Clinical Engineering Skåne, Digitalisering IT/MT, Region Skåne, Lund, Sweden.
| |
Collapse
|
9
|
Motin MA, Kumar Karmakar C, Kumar DK, Palaniswami M. PPG Derived Respiratory Rate Estimation in Daily living Conditions. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:2736-2739. [PMID: 33018572 DOI: 10.1109/embc44109.2020.9175682] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Respiratory rate (RR) derived from photoplethysmogram (PPG) during daily activities can be corrupted due to movement and other artefacts. We have investigated the use of ensemble empirical mode decomposition (EEMD) based smart fusion approach for improving the RR extraction from PPG. PPG was recorded while subjects performed five different activities: sitting, standing, climbing and descending stairs, walking, and running. RR was obtained using EEMD and smart fusion. The median absolute error (AE) of the proposed method is superior, median AE = 3.05 (range 3.01 to 3.18) breath/min in estimating RR during five different activities. Therefore, the proposed method can be implemented for overcoming the artefact problems when recording continuous RR monitoring during activities of daily living.
Collapse
|
10
|
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.
Collapse
Affiliation(s)
| | - Bingo Wing-Kuen Ling
- School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China; (R.L.); (P.F.); (J.C.)
| | | | | |
Collapse
|
11
|
FMCW Laser Fuze Multiple Scattering Model and Accurate Fixed-Distance Algorithm in a Smoke Environment. SENSORS 2020; 20:s20092604. [PMID: 32375216 PMCID: PMC7273217 DOI: 10.3390/s20092604] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 04/26/2020] [Accepted: 04/28/2020] [Indexed: 12/04/2022]
Abstract
In a smoke environment, suspended particles can scatter and absorb laser photons, making target echo signals extremely weak and difficult to extract and identify, which causes obvious difficulty in fixed-distance of laser fuze. In this paper, the multiple scattering model of frequency-modulated-continuous-wave (FMCW) laser fuze in a smoke environment was established. This model simulates multi-path propagation and multiple scattering of photons. At the same time, we use the correntropy spectral density (CSD) algorithm for accurate fixed-distance of FMCW laser fuze. The absolute error of distance does not exceed 0.15 m in smoke interference environment.
Collapse
|
12
|
Charvátová H, Procházka A, Vyšata O. Motion Assessment for Accelerometric and Heart Rate Cycling Data Analysis. SENSORS (BASEL, SWITZERLAND) 2020; 20:E1523. [PMID: 32164235 PMCID: PMC7085619 DOI: 10.3390/s20051523] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Revised: 03/04/2020] [Accepted: 03/05/2020] [Indexed: 11/16/2022]
Abstract
Motion analysis is an important topic in the monitoring of physical activities and recognition of neurological disorders. The present paper is devoted to motion assessment using accelerometers inside mobile phones located at selected body positions and the records of changes in the heart rate during cycling, under different body loads. Acquired data include 1293 signal segments recorded by the mobile phone and the Garmin device for uphill and downhill cycling. The proposed method is based upon digital processing of the heart rate and the mean power in different frequency bands of accelerometric data. The classification of the resulting features was performed by the support vector machine, Bayesian methods, k-nearest neighbor method, and neural networks. The proposed criterion is then used to find the best positions for the sensors with the highest discrimination abilities. The results suggest the sensors be positioned on the spine for the classification of uphill and downhill cycling, yielding an accuracy of 96.5% and a cross-validation error of 0.04 evaluated by a two-layer neural network system for features based on the mean power in the frequency bands 〈 3 , 8 〉 and 〈 8 , 15 〉 Hz. This paper shows the possibility of increasing this accuracy to 98.3% by the use of more features and the influence of appropriate sensor positioning for motion monitoring and classification.
Collapse
Affiliation(s)
- Hana Charvátová
- Faculty of Applied Informatics, Tomas Bata University in Zlín, 760 01 Zlín, Czech Republic
| | - Aleš Procházka
- Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, 166 28 Prague 6, Czech Republic;
- Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, 160 00 Prague 6, Czech Republic
- Department of Neurology, Faculty of Medicine in Hradec Králové, Charles University, 500 05 Hradec Králové, Czech Republic;
| | - Oldřich Vyšata
- Department of Neurology, Faculty of Medicine in Hradec Králové, Charles University, 500 05 Hradec Králové, Czech Republic;
| |
Collapse
|
13
|
Nguyen CV, Le Quang T, Vu TN, Le Thi H, Van KN, Trong TH, Trong TD, Sun G, Ishibashi K. A non-contact infection screening system using medical radar and Linux-embedded FPGA: Implementation and preliminary validation. INFORMATICS IN MEDICINE UNLOCKED 2019; 16:100225. [PMID: 32289073 PMCID: PMC7103934 DOI: 10.1016/j.imu.2019.100225] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
Objectives In this study, an infection screening system was developed to detect patients suffering from infectious diseases. In addition, the system was also designed to deal with the variability in age and gender, which would affect the accuracy of the detection. Furthermore, to enable a low-cost, non-contact and embedded system, multiple vital signs from a medical radar were measured and all algorithms were implemented on a Field Programmable Gate Array, named PYNQ-Z1. Methods The system consisted of two main stages: digital signal processing and data classification. In the former stage, Butterworth filters, with flexible cut-off frequencies depending on age and gender, and a time-domain peak detection algorithm were deployed to compute three vital signs, namely heart rate, respiratory rate, and standard deviation of heart beat-to-beat interval. For the classification problem, two machine learning models, Support Vector Machine and Quadratic Discriminant Analysis, were implemented. Results The Student's t-test showed that our proposed digital signal processing algorithms coped well with the variability of human cases in age and gender. Meanwhile, the f1-score of roughly 98.0% represented the high sensitivity and specificity of our proposed machine learning methods. Conclusion This study outlines the implementation of an infection screening system, which achieved competent performance. The system might be beneficial for fast screening of infected patients at public health centers in underdeveloped areas, where people have little access to healthcare.
Collapse
Affiliation(s)
- Cuong V Nguyen
- School of Electronics and Telecommunications, Hanoi University of Science and Technology, Hanoi, 100000, Viet Nam
| | - Truong Le Quang
- School of Electronics and Telecommunications, Hanoi University of Science and Technology, Hanoi, 100000, Viet Nam
| | - Trung Nguyen Vu
- National Hospital of Tropical Diseases, Hanoi, Viet Nam.,Hanoi Medical University, Hanoi, Vietnam, Hanoi, Viet Nam
| | - Hoi Le Thi
- National Hospital of Tropical Diseases, Hanoi, Viet Nam
| | | | - Thanh Han Trong
- School of Electronics and Telecommunications, Hanoi University of Science and Technology, Hanoi, 100000, Viet Nam
| | - Tuan Do Trong
- School of Electronics and Telecommunications, Hanoi University of Science and Technology, Hanoi, 100000, Viet Nam
| | - Guanghao Sun
- Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo, 182-8585, Japan.,Center for Neuroscience and Biomedical Engineering, The University of Electro-Communications, Tokyo, 182-8585, Japan
| | - Koichiro Ishibashi
- Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo, 182-8585, Japan
| |
Collapse
|
14
|
Liu H, Allen J, Zheng D, Chen F. Recent development of respiratory rate measurement technologies. Physiol Meas 2019; 40:07TR01. [PMID: 31195383 DOI: 10.1088/1361-6579/ab299e] [Citation(s) in RCA: 93] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Respiratory rate (RR) is an important physiological parameter whose abnormality has been regarded as an important indicator of serious illness. In order to make RR monitoring simple to perform, reliable and accurate, many different methods have been proposed for such automatic monitoring. According to the theory of respiratory rate extraction, methods are categorized into three modalities: extracting RR from other physiological signals, RR measurement based on respiratory movements, and RR measurement based on airflow. The merits and limitations of each method are highlighted and discussed. In addition, current works are summarized to suggest key directions for the development of future RR monitoring methodologies.
Collapse
Affiliation(s)
- Haipeng Liu
- Faculty of Health, Education, Medicine, and Social Care, Anglia Ruskin University, Chelmsford, CM1 1SQ, United Kingdom. Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen 518055, People's Republic of China
| | | | | | | |
Collapse
|
15
|
Jorge J, Villarroel M, Chaichulee S, Green G, McCormick K, Tarassenko L. Assessment of Signal Processing Methods for Measuring the Respiratory Rate in the Neonatal Intensive Care Unit. IEEE J Biomed Health Inform 2019; 23:2335-2346. [PMID: 30951480 DOI: 10.1109/jbhi.2019.2898273] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Knowledge of the pathological instabilities in the breathing pattern can provide valuable insights into the cardiorespiratory status of the critically-ill infant as well as their maturation level. This paper is concerned with the measurement of respiratory rate in premature infants. We compare the rates estimated from the chest impedance pneumogram, the ECG-derived respiratory rhythms, and the PPG-derived respiratory rhythms against those measured in the reference standard of breath detection provided by attending clinical staff during 165 manual breath counts. We demonstrate that accurate RR estimates can be produced from all sources for RR in the 40-80 bpm (breaths per min) range. We also conclude that the use of indirect methods based on the ECG or the PPG poses a fundamental challenge in this population due to their poor behavior at fast breathing rates (upward of 80 bpm).
Collapse
|
16
|
Jarchi D, Charlton P, Pimentel M, Casson A, Tarassenko L, Clifton DA. Estimation of respiratory rate from motion contaminated photoplethysmography signals incorporating accelerometry. Healthc Technol Lett 2019; 6:19-26. [PMID: 30881695 PMCID: PMC6407448 DOI: 10.1049/htl.2018.5019] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Revised: 10/04/2018] [Accepted: 11/20/2018] [Indexed: 12/02/2022] Open
Abstract
Estimation of respiratory rate (RR) from photoplethysmography (PPG) signals has important applications in the healthcare sector, from assisting doctors onwards to monitoring patients in their own homes. The problem is still very challenging, particularly during the motion for large segments of data, where results from different methods often do not agree. The authors aim to propose a new technique which performs motion reduction from PPG signals with the help of simultaneous acceleration signals where the PPG and accelerometer sensors need to be embedded in the same sensor unit. This method also reconstructs motion corrupted PPG signals in the Hilbert domain. An auto-regressive (AR) based technique has been used to estimate the RR from reconstructed PPGs. The proposed method has provided promising results for the estimation of RRs and their variations from PPG signals corrupted with motion artefact. The proposed platform is able to contribute to continuous in-hospital and home-based monitoring of patients using PPG signals under various conditions such as rest and motion states.
Collapse
Affiliation(s)
- Delaram Jarchi
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, UK
| | | | - Marco Pimentel
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, UK
| | - Alex Casson
- School of Electrical and Electronic Engineering, University of Manchester, Manchester, UK
| | - Lionel Tarassenko
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, UK
| | - David A Clifton
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, UK
| |
Collapse
|
17
|
Sharma H. Heart rate extraction from PPG signals using variational mode decomposition. Biocybern Biomed Eng 2019. [DOI: 10.1016/j.bbe.2018.11.001] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
|
18
|
Dehkordi P, Garde A, Molavi B, Ansermino JM, Dumont GA. Extracting Instantaneous Respiratory Rate From Multiple Photoplethysmogram Respiratory-Induced Variations. Front Physiol 2018; 9:948. [PMID: 30072918 PMCID: PMC6058306 DOI: 10.3389/fphys.2018.00948] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2018] [Accepted: 06/28/2018] [Indexed: 11/13/2022] Open
Abstract
In this study, we proposed a novel method for extracting the instantaneous respiratory rate (IRR) from the pulse oximeter photoplethysmogram (PPG). The method was performed in three main steps: (1) a time-frequency transform called synchrosqueezing transform (SST) was used to extract the respiratory-induced intensity, amplitude and frequency variation signals from PPG, (2) the second SST was applied to each extracted respiratory-induced variation signal to estimate the corresponding IRR, and (3) the proposed peak-conditioned fusion method then combined the IRR estimates to calculate the final IRR. We validated the implemented method with capnography and nasal/oral airflow as the reference RR using the limits of agreement (LOA) approach. Compared to simple fusion and single respiratory-induced variation estimations, peak-conditioned fusion shows better performance. It provided a bias of 0.28 bpm with the 95% LOAs ranging from −3.62 to 4.17, validated against capnography and a bias of 0.04 bpm with the 95% LOAs ranging from −5.74 to 5.82, validated against nasal/oral airflow. This algorithm would expand the functionality of a conventional pulse oximetry beyond the measurement of heart rate and oxygen saturation to measure the respiratory rate continuously and instantly.
Collapse
Affiliation(s)
- Parastoo Dehkordi
- Electrical and Computer Engineering, Faculty of Applied Science, The University of British Columbia, Vancouver, BC, Canada
| | - Ainara Garde
- Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede, Netherlands
| | | | - J Mark Ansermino
- Anesthesiology, Pharmacology and Therapeutics, The University of British Columbia, Vancouver, BC, Canada
| | - Guy A Dumont
- Electrical and Computer Engineering, Faculty of Applied Science, The University of British Columbia, Vancouver, BC, Canada
| |
Collapse
|
19
|
Prochazka A, Charvatova H, Vaseghi S, Vysata O. Machine Learning in Rehabilitation Assessment for Thermal and Heart Rate Data Processing. IEEE Trans Neural Syst Rehabil Eng 2018; 26:1209-1214. [DOI: 10.1109/tnsre.2018.2831444] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
|
20
|
Kim M, Collins SH. Step-to-Step Ankle Inversion/Eversion Torque Modulation Can Reduce Effort Associated with Balance. Front Neurorobot 2017; 11:62. [PMID: 29184493 PMCID: PMC5694462 DOI: 10.3389/fnbot.2017.00062] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2017] [Accepted: 10/19/2017] [Indexed: 11/25/2022] Open
Abstract
Below-knee amputation is associated with higher energy expenditure during walking, partially due to difficulty maintaining balance. We previously found that once-per-step push-off work control can reduce balance-related effort, both in simulation and in experiments with human participants. Simulations also suggested that changing ankle inversion/eversion torque on each step, in response to changes in body state, could assist with balance. In this study, we investigated the effects of ankle inversion/eversion torque modulation on balance-related effort among amputees (N = 5) using a multi-actuated ankle-foot prosthesis emulator. In stabilizing conditions, changes in ankle inversion/eversion torque were applied so as to counteract deviations in side-to-side center-of-mass acceleration at the moment of intact-limb toe off; higher acceleration toward the prosthetic limb resulted in a corrective ankle inversion torque during the ensuing stance phase. Destabilizing controllers had the opposite effect, and a zero gain controller made no changes to the nominal inversion/eversion torque. To separate the balance-related effects of step-to-step control from the potential effects of changes in average mechanics, average ankle inversion/eversion torque and prosthesis work were held constant across conditions. High-gain stabilizing control lowered metabolic cost by 13% compared to the zero gain controller (p = 0.05). We then investigated individual responses to subject-specific stabilizing controllers following an enforced exploration period. Four of five participants experienced reduced metabolic rate compared to the zero gain controller (−15, −14, −11, −6, and +4%) an average reduction of 9% (p = 0.05). Average prosthesis mechanics were unchanged across all conditions, suggesting that improvements in energy economy might have come from changes in step-to-step corrections related to balance. Step-to-step modulation of inversion/eversion torque could be used in new, active ankle-foot prostheses to reduce walking effort associated with maintaining balance.
Collapse
Affiliation(s)
- Myunghee Kim
- Experimental Biomechatronics Laboratory, Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, United States
| | - Steven H Collins
- Experimental Biomechatronics Laboratory, Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, United States.,Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, United States.,Department of Mechanical Engineering, Stanford University, Stanford, CA, United States
| |
Collapse
|
21
|
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.
Collapse
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
| |
Collapse
|
22
|
Cicone A, Wu HT. How Nonlinear-Type Time-Frequency Analysis Can Help in Sensing Instantaneous Heart Rate and Instantaneous Respiratory Rate from Photoplethysmography in a Reliable Way. Front Physiol 2017; 8:701. [PMID: 29018352 PMCID: PMC5615790 DOI: 10.3389/fphys.2017.00701] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2017] [Accepted: 08/30/2017] [Indexed: 11/13/2022] Open
Abstract
Despite the population of the noninvasive, economic, comfortable, and easy-to-install photoplethysmography (PPG), it is still lacking a mathematically rigorous and stable algorithm which is able to simultaneously extract from a single-channel PPG signal the instantaneous heart rate (IHR) and the instantaneous respiratory rate (IRR). In this paper, a novel algorithm called deppG is provided to tackle this challenge. deppG is composed of two theoretically solid nonlinear-type time-frequency analyses techniques, the de-shape short time Fourier transform and the synchrosqueezing transform, which allows us to extract the instantaneous physiological information from the PPG signal in a reliable way. To test its performance, in addition to validating the algorithm by a simulated signal and discussing the meaning of "instantaneous," the algorithm is applied to two publicly available batch databases, the Capnobase and the ICASSP 2015 signal processing cup. The former contains PPG signals relative to spontaneous or controlled breathing in static patients, and the latter is made up of PPG signals collected from subjects doing intense physical activities. The accuracies of the estimated IHR and IRR are compared with the ones obtained by other methods, and represent the state-of-the-art in this field of research. The results suggest the potential of deppG to extract instantaneous physiological information from a signal acquired from widely available wearable devices, even when a subject carries out intense physical activities.
Collapse
Affiliation(s)
- Antonio Cicone
- Department of Information Engineering, Computer Science and Mathematics, Universitá degli Studi dell'AquilaL'Aquila, Italy
| | - Hau-Tieng Wu
- Department of Mathematics and Statistical Science, Duke UniversityDurham, NC, United States.,Mathematics Division, National Center for Theoretical SciencesTaipei, Taiwan
| |
Collapse
|
23
|
Kim M, Ding Y, Malcolm P, Speeckaert J, Siviy CJ, Walsh CJ, Kuindersma S. Human-in-the-loop Bayesian optimization of wearable device parameters. PLoS One 2017; 12:e0184054. [PMID: 28926613 PMCID: PMC5604949 DOI: 10.1371/journal.pone.0184054] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2017] [Accepted: 08/17/2017] [Indexed: 11/19/2022] Open
Abstract
The increasing capabilities of exoskeletons and powered prosthetics for walking assistance have paved the way for more sophisticated and individualized control strategies. In response to this opportunity, recent work on human-in-the-loop optimization has considered the problem of automatically tuning control parameters based on realtime physiological measurements. However, the common use of metabolic cost as a performance metric creates significant experimental challenges due to its long measurement times and low signal-to-noise ratio. We evaluate the use of Bayesian optimization-a family of sample-efficient, noise-tolerant, and global optimization methods-for quickly identifying near-optimal control parameters. To manage experimental complexity and provide comparisons against related work, we consider the task of minimizing metabolic cost by optimizing walking step frequencies in unaided human subjects. Compared to an existing approach based on gradient descent, Bayesian optimization identified a near-optimal step frequency with a faster time to convergence (12 minutes, p < 0.01), smaller inter-subject variability in convergence time (± 2 minutes, p < 0.01), and lower overall energy expenditure (p < 0.01).
Collapse
Affiliation(s)
- Myunghee Kim
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, United States of America
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, MA, United States of America
| | - Ye Ding
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, United States of America
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, MA, United States of America
| | - Philippe Malcolm
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, United States of America
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, MA, United States of America
- Department of Biomechanics and Center for Research in Human Movement Variability, University of Nebraska Omaha, Omaha, NE, United States of America
| | - Jozefien Speeckaert
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, United States of America
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, MA, United States of America
| | - Christoper J. Siviy
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, United States of America
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, MA, United States of America
| | - Conor J. Walsh
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, United States of America
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, MA, United States of America
| | - Scott Kuindersma
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, United States of America
| |
Collapse
|
24
|
Pereira I, Silveira LF, Gonçalves L. Video Synchronization With Bit-Rate Signals and Correntropy Function. SENSORS 2017; 17:s17092021. [PMID: 28869536 PMCID: PMC5620601 DOI: 10.3390/s17092021] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2017] [Revised: 08/25/2017] [Accepted: 08/30/2017] [Indexed: 11/16/2022]
Abstract
We propose an approach for the synchronization of video streams using correntropy. Essentially, the time offset is calculated on the basis of the instantaneous transfer rates of the video streams that are extracted in the form of a univariate signal known as variable bit-rate (VBR). The state-of-the-art approach uses a window segmentation strategy that is based on consensual zero-mean normalized cross-correlation (ZNCC). This strategy has an elevated computational complexity, making its application to synchronizing online data streaming difficult. Hence, our proposal uses a different window strategy that, together with the correntropy function, allows the synchronization to be performed for online applications. This provides equivalent synchronization scores with a rapid offset determination as the streams come into the system. The efficiency of our approach has been verified through experiments that demonstrate its viability with values that are as precise as those obtained by ZNCC. The proposed approach scored 81 % in time reference classification against the equivalent 81 % of the state-of-the-art approach, requiring much less computational power.
Collapse
Affiliation(s)
- Igor Pereira
- Department of Computer Engineering and Automation, University of Rio Grande do Norte, Rio Grande do Norte 59078-970, Brazil.
| | - Luiz F Silveira
- Department of Computer Engineering and Automation, University of Rio Grande do Norte, Rio Grande do Norte 59078-970, Brazil.
| | - Luiz Gonçalves
- Department of Computer Engineering and Automation, University of Rio Grande do Norte, Rio Grande do Norte 59078-970, Brazil.
| |
Collapse
|
25
|
Zhang X, Ding Q. Respiratory rate estimation from the photoplethysmogram via joint sparse signal reconstruction and spectra fusion. Biomed Signal Process Control 2017. [DOI: 10.1016/j.bspc.2017.02.003] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
26
|
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.
Collapse
|
27
|
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.
Collapse
|
28
|
Choi A, Shin H. Photoplethysmography sampling frequency: pilot assessment of how low can we go to analyze pulse rate variability with reliability? Physiol Meas 2017; 38:586-600. [PMID: 28169836 DOI: 10.1088/1361-6579/aa5efa] [Citation(s) in RCA: 59] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Pulse rate variability (PRV) analysis appears as the first alternative to heart rate variability analysis for wearable devices; however, there is a constraint on computational load and energy consumption for the limited system resources available to the devices. Considering that adjustment of the sampling frequency is one of the strategies for reducing computational load and power consumption, this study aimed to investigate the influence of sampling frequency (f s) on PRV analysis and to find the minimum sampling frequency while maintaining reliability. We generated 5000, 2500, 1000, 500, 250, 100, 50, 25, 20, 15, 10, 5 Hz down-sampled photoplethysmograms from 10 kHz-sampled PPGs and derived time- and frequency-domain variables of the PRV. These included AVNN, SDNN, SDSD, RMSSD, NN50, pNN50, total power, VLF, LF, HF, LF/HF, nLF and nHF for each down-sampled signal. Derived variables were compared with heart rate variability of the 10 kHz-sampled electrocardiograms, and then statistically investigated using one-way ANOVA test and Bland-Altman analysis. As a result, significant differences (P < 0.05) were found for SDNN, SDSD, RMSSD, NN50, pNN50, TP, HF, LF/HF, nLF and nHF, but not for AVNN, VLF and LF. Based on the post hoc tests, it was found that the NN50 and pNN50, SDSD and RMSSD, LF/HF and nHF, SDNN, TP and nLF analysis had significant differences at f s ⩽ 20 Hz, f s ⩽ 15 Hz, f s ⩽10 Hz; f s = 5 Hz, respectively. In other words, a significant difference was not seen for any variable if the f s was greater than 25 Hz. Consequently, our pilot study suggests that analysis of variability in the time and frequency domain from pulse rate obtained through PPG may be potentially as reliable as that derived from the analysis of the electrocardiogram, provided that f s ⩾25 Hz sampling frequency is used.
Collapse
Affiliation(s)
- A Choi
- Department of Software, Gachon University, Seongnam, Republic of Korea
| | | |
Collapse
|
29
|
Pimentel MAF, Johnson AEW, Charlton PH, Birrenkott D, Watkinson PJ, Tarassenko L, Clifton DA. Toward a Robust Estimation of Respiratory Rate From Pulse Oximeters. IEEE Trans Biomed Eng 2016; 64:1914-1923. [PMID: 27875128 PMCID: PMC6051482 DOI: 10.1109/tbme.2016.2613124] [Citation(s) in RCA: 79] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Goal: Current methods for estimating respiratory rate (RR) from the photoplethysmogram (PPG)
typically fail to distinguish between periods of high- and low-quality input data, and fail to perform well on
independent “validation” datasets. The lack of robustness of existing methods directly results in a lack
of penetration of such systems into clinical practice. The present work proposes an alternative method to improve the
robustness of the estimation of RR from the PPG. Methods: The proposed algorithm is based on the use
of multiple autoregressive models of different orders for determining the dominant respiratory frequency in the three
respiratory-induced variations (frequency, amplitude, and intensity) derived from the PPG. The algorithm was tested on
two different datasets comprising 95 eight-minute PPG recordings (in total) acquired from both children and adults in
different clinical settings, and its performance using two window sizes (32 and 64 seconds) was compared with that of
existing methods in the literature. Results: The proposed method achieved comparable accuracy to
existing methods in the literature, with mean absolute errors (median, 25\documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{upgreek}
\usepackage{mathrsfs}
\setlength{\oddsidemargin}{-69pt}
\begin{document}
}{}
$\text {th}$\end{document}–75\documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{upgreek}
\usepackage{mathrsfs}
\setlength{\oddsidemargin}{-69pt}
\begin{document}
}{}$\text {th}$\end{document} percentiles for a window size of 32 seconds) of 1.5 (0.3–3.3) and 4.0 (1.8–5.5) breaths
per minute (for each dataset respectively), whilst providing RR estimates for a greater proportion of windows (over
90% of the input data are kept). Conclusion: Increased robustness of RR estimation by the
proposed method was demonstrated. Significance: This work demonstrates that the use of large publicly
available datasets is essential for improving the robustness of wearable-monitoring algorithms for use in clinical
practice.
Collapse
Affiliation(s)
- Marco A F Pimentel
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, U.K
| | - Alistair E W Johnson
- Institute for Medical Engineering & ScienceMassachusetts Institute of Technology
| | | | - Drew Birrenkott
- Department of Engineering ScienceInstitute of Biomedical EngineeringUniversity of Oxford
| | | | - Lionel Tarassenko
- Department of Engineering ScienceInstitute of Biomedical EngineeringUniversity of Oxford
| | - David A Clifton
- Department of Engineering ScienceInstitute of Biomedical EngineeringUniversity of Oxford
| |
Collapse
|
30
|
Harju J, Vehkaoja A, Lindroos V, Kumpulainen P, Liuhanen S, Yli-Hankala A, Oksala N. Determination of saturation, heart rate, and respiratory rate at forearm using a Nellcor™ forehead SpO 2-saturation sensor. J Clin Monit Comput 2016; 31:1019-1026. [PMID: 27752932 DOI: 10.1007/s10877-016-9940-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2016] [Accepted: 10/07/2016] [Indexed: 11/29/2022]
Abstract
Alterations in arterial blood oxygen saturation, heart rate (HR), and respiratory rate (RR) are strongly associated with intra-hospital cardiac arrests and resuscitations. A wireless, easy-to-use, and comfortable method for monitoring these important clinical signs would be highly useful. We investigated whether the Nellcor™ OxiMask MAX-FAST forehead sensor could provide data for vital sign measurements when located at the distal forearm instead of its intended location at the forehead to provide improved comfortability and easy placement. In a prospective setting, we recruited 30 patients undergoing surgery requiring postoperative care. At the postoperative care unit, patients were monitored for two hours using a standard patient monitor and with a study device equipped with a Nellcor™ Forehead SpO2 sensor. The readings were electronically recorded and compared in post hoc analysis using Bland-Altman plots, Spearman's correlation, and root-mean-square error (RMSE). Bland-Altman plot showed that saturation (SpO2) differed by a mean of -0.2 % points (SD, 4.6), with a patient-weighted Spearman's correlation (r) of 0.142, and an RMSE of 4.2 points. For HR measurements, the mean difference was 0.6 bpm (SD, 2.5), r = 0.997, and RMSE = 1.8. For RR, the mean difference was -0.5 1/min (4.1), r = 0.586, and RMSE = 4.0. The SpO2 readings showed a low mean difference, but also a low correlation and high RMSE, indicating that the Nellcor™ saturation sensor cannot reliably assess oxygen saturation at the forearm when compared to finger PPG measurements.
Collapse
Affiliation(s)
- Jarkko Harju
- Department of Anesthesia, Tampere University Hospital, PL2000, 33521, Tampere, Finland.
| | | | | | | | - Sasu Liuhanen
- Department of Anesthesia, Helsinki University Hospital, Helsinki, Finland
| | - Arvi Yli-Hankala
- Department of Anesthesia, Tampere University Hospital, PL2000, 33521, Tampere, Finland.,Medical School, University of Tampere, Tampere, Finland
| | - Niku Oksala
- Medical School, University of Tampere, Tampere, Finland.,Department of Surgery, Tampere University Hospital, Tampere, Finland
| |
Collapse
|
31
|
Estrada L, Torres A, Sarlabous L, Jané R. EMG-Derived Respiration Signal Using the Fixed Sample Entropy during an Inspiratory Load Protocol. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2015:1703-6. [PMID: 26736605 DOI: 10.1109/embc.2015.7318705] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Extracting clinical information from one single measurement represents a step forward in the assessment of the respiratory muscle function. This attracting idea entails the reduction of the instrumentation and fosters to develop new medical integrated technologies. We present the use of the fixed sample entropy (fSampEn) as a more direct method to non-invasively derive the breathing activity from the diaphragm electromyographic (EMGdi) signal, and thus to extract the respiratory rate, an important vital sign which is cumbersome and time-consuming to be measured by clinicians. fSampEn is a method to evaluate the EMGdi activity that is less sensitive to the cardiac activity (ECG) and its application has proven to be useful to evaluate the load of the respiratory muscles. The behavior of the proposed method was tested in signals from two subjects that performed an inspiratory load protocol, which consists of increments in the inspiratory mouth pressure (P mouth). Two respiratory signals were derived and compared to the P mouth signal: the ECG-derived respiration (EDR) signal from the lead-I configuration, and the EMG-derived respiration (EMGDR) signal by applying the fSampEn method over the EMGdi signal. The similitude and the lag between signals were calculated through the cross-correlation between each derived respiratory signal and the P mouth. The EMGDR signal showed higher correlation and lower lag values (≥ 0.91 and ≤ 0.70 s, respectively) than the EDR signal (≥ 0.83 and ≤ 0.99 s, respectively). Additionally, the respiratory rate was estimated with the P mouth, EDR and EMGDR signals showing very similar values. The results from this preliminary work suggest that the fSampEn method can be used to derive the respiration waveform from the respiratory muscle electrical activity.
Collapse
|
32
|
Cherif S, Pastor D, L'Her E. Detection of artifacts on photoplethysmography signals using random distortion testing. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2016:6214-6217. [PMID: 28269671 DOI: 10.1109/embc.2016.7592148] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
In this work, we describe a novel method based on waveform morphology for detecting artifacts in photoplethysmography (PPG) signals and, thus, improve reliability of PPG. By considering inter-individual and measure condition variability, specific parameters are estimated for each record. We introduce a novel metric for comparing pulses, which is the derivative of the correlation coefficient. Then, we propose a detection method based on Random Distortion Testing (RDT), to perform adaptive threasholding for each record. The results show that the proposed method provides pertinent detection of pulses with artifacts. Tested on 104 PPG records, the mean of sensitivity, specificity and accuracy were 84 ± 16%, 83 ± 12% and 83 ± 8%, respectively.
Collapse
|
33
|
Zhang X, Ding Q. Respiratory rate monitoring from the photoplethysmogram via sparse signal reconstruction. Physiol Meas 2016; 37:1105-19. [DOI: 10.1088/0967-3334/37/7/1105] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
|
34
|
Charlton PH, Bonnici T, Tarassenko L, Clifton DA, Beale R, Watkinson PJ. An assessment of algorithms to estimate respiratory rate from the electrocardiogram and photoplethysmogram. Physiol Meas 2016; 37:610-26. [PMID: 27027672 PMCID: PMC5390977 DOI: 10.1088/0967-3334/37/4/610] [Citation(s) in RCA: 135] [Impact Index Per Article: 16.9] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Over 100 algorithms have been proposed to estimate respiratory rate (RR) from the electrocardiogram (ECG) and photoplethysmogram (PPG). As they have never been compared systematically it is unclear which algorithm performs the best. Our primary aim was to determine how closely algorithms agreed with a gold standard RR measure when operating under ideal conditions. Secondary aims were: (i) to compare algorithm performance with IP, the clinical standard for continuous respiratory rate measurement in spontaneously breathing patients; (ii) to compare algorithm performance when using ECG and PPG; and (iii) to provide a toolbox of algorithms and data to allow future researchers to conduct reproducible comparisons of algorithms. Algorithms were divided into three stages: extraction of respiratory signals, estimation of RR, and fusion of estimates. Several interchangeable techniques were implemented for each stage. Algorithms were assembled using all possible combinations of techniques, many of which were novel. After verification on simulated data, algorithms were tested on data from healthy participants. RRs derived from ECG, PPG and IP were compared to reference RRs obtained using a nasal-oral pressure sensor using the limits of agreement (LOA) technique. 314 algorithms were assessed. Of these, 270 could operate on either ECG or PPG, and 44 on only ECG. The best algorithm had 95% LOAs of -4.7 to 4.7 bpm and a bias of 0.0 bpm when using the ECG, and -5.1 to 7.2 bpm and 1.0 bpm when using PPG. IP had 95% LOAs of -5.6 to 5.2 bpm and a bias of -0.2 bpm. Four algorithms operating on ECG performed better than IP. All high-performing algorithms consisted of novel combinations of time domain RR estimation and modulation fusion techniques. Algorithms performed better when using ECG than PPG. The toolbox of algorithms and data used in this study are publicly available.
Collapse
Affiliation(s)
- Peter H Charlton
- School of Medicine, King's College London, UK. Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, UK
| | | | | | | | | | | |
Collapse
|
35
|
Görges M, West NC, Christopher NA, Koch JL, Brodie SM, Lowlaavar N, Lauder GR, Ansermino JM. An Ethnographic Observational Study to Evaluate and Optimize the Use of Respiratory Acoustic Monitoring in Children Receiving Postoperative Opioid Infusions. Anesth Analg 2016; 122:1132-40. [PMID: 26745756 DOI: 10.1213/ane.0000000000001127] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
BACKGROUND Respiratory depression in children receiving postoperative opioid infusions is a significant risk because of the interindividual variability in analgesic requirement. Detection of respiratory depression (or apnea) in these children may be improved with the introduction of automated acoustic respiratory rate (RR) monitoring. However, early detection of adverse events must be balanced with the risk of alarm fatigue. Our objective was to evaluate the use of acoustic RR monitoring in children receiving opioid infusions on a postsurgical ward and identify the causes of false alarm and optimal alarm thresholds. METHODS A video ethnographic study was performed using an observational, mixed methods approach. After surgery, an acoustic RR sensor was placed on the participant's neck and attached to a Rad87 monitor. The monitor was networked with paging for alarms. Vital signs data and paging notification logs were obtained from the central monitoring system. Webcam videos of the participant, infusion pump, and Rad87 monitor were recorded, stored on a secure server, and subsequently analyzed by 2 research nurses to identify the cause of the alarm, response, and effectiveness. Alarms occurring within a 90-second window were grouped into a single-alarm response opportunity. RESULTS Data from 49 patients (30 females) with median age 14 (range, 4.4-18.8) years were analyzed. The 896 bedside vital sign threshold alarms resulted in 160 alarm response opportunities (44 low RR, 74 high RR, and 42 low SpO2). In 141 periods (88% of total), for which video was available, 65% of alarms were deemed effective (followed by an alarm-related action within 10 minutes). Nurses were the sole responders in 55% of effective alarms and the patient or parent in 20%. Episodes of desaturation (SpO2 < 90%) were observed in 9 patients: At the time of the SpO2 paging trigger, the RR was >10 bpm in 6 of 9 patients. Based on all RR samples observed, the default alarm thresholds, to serve as a starting point for each patient, would be a low RR of 6 (>10 years of age) and 10 (4-9 years of age). CONCLUSIONS In this study, the use of RR monitoring did not improve the detection of respiratory depression. An RR threshold, which would have been predictive of desaturations, would have resulted in an unacceptably high false alarm rate. Future research using a combination of variables (e.g., SpO2 and RR), or the measurement of tidal volumes, may be needed to improve patient safety in the postoperative ward.
Collapse
Affiliation(s)
- Matthias Görges
- From the Departments of *Electrical and Computer Engineering and †Anesthesiology, Pharmacology, and Therapeutics, University of British Columbia, Vancouver, British Columbia, Canada; and ‡Department of Neurosciences and Surgery, BC Children's Hospital, Vancouver, British Columbia, Canada
| | | | | | | | | | | | | | | |
Collapse
|
36
|
Mollakazemi MJ, Atyabi SA, Ghaffari A. Heart beat detection using a multimodal data coupling method. Physiol Meas 2015. [DOI: 10.1088/0967-3334/36/8/1729] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
|
37
|
Karlen W, Garde A, Myers D, Scheffer C, Ansermino JM, Dumont GA. Estimation of respiratory rate from photoplethysmographic imaging videos compared to pulse oximetry. IEEE J Biomed Health Inform 2015; 19:1331-8. [PMID: 25955999 DOI: 10.1109/jbhi.2015.2429746] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
We present a study evaluating two respiratory rate estimation algorithms using videos obtained from placing a finger on the camera lens of a mobile phone. The two algorithms, based on Smart Fusion and empirical mode decomposition (EMD), consist of previously developed signal processing methods to detect features and extract respiratory induced variations in photoplethysmographic signals to estimate respiratory rate. With custom-built software on an Android phone, photoplethysmographic imaging videos were recorded from 19 healthy adults while breathing spontaneously at respiratory rates between 6 to 32 breaths/min. Signals from two pulse oximeters were simultaneously recorded to compare the algorithms' performance using mobile phone data and clinical data. Capnometry was recorded to obtain reference respiratory rates. Two hundred seventy-two recordings were analyzed. The Smart Fusion algorithm reported 39 recordings with insufficient respiratory information from the photoplethysmographic imaging data. Of the 232 remaining recordings, a root mean square error (RMSE) of 6 breaths/min was obtained. The RMSE for the pulse oximeter data was lower at 2.3 breaths/min. RMSE for the EMD method was higher throughout all data sources as, unlike the Smart Fusion, the EMD method did not screen for inconsistent results. The study showed that it is feasible to estimate respiratory rates by placing a finger on a mobile phone camera, but that it becomes increasingly challenging at respiratory rates greater than 20 breaths/min, independent of data source or algorithm tested.
Collapse
|
38
|
Discrimination between different degrees of coronary artery disease using time-domain features of the finger photoplethysmogram in response to reactive hyperemia. Biomed Signal Process Control 2015. [DOI: 10.1016/j.bspc.2014.12.011] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
|
39
|
Karlen W, Garde A, Myers D, Scheffer C, Ansermino JM, Dumont GA. Respiratory rate assessment from photoplethysmographic imaging. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2014:5397-400. [PMID: 25571214 DOI: 10.1109/embc.2014.6944846] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
We present a study investigating the suitability of a respiratory rate estimation algorithm applied to photoplethysmographic imaging on a mobile phone. The algorithm consists of a cascade of previously developed signal processing methods to detect features and extract respiratory induced variations in photoplethysmogram signals to estimate respiratory rate. With custom-built software on an Android phone (Camera Oximeter), contact photoplethysmographic imaging videos were recorded using the integrated camera from 19 healthy adults breathing spontaneously at respiratory rates between 6 and 40 breaths/min. Capnometry was simultaneously recorded to obtain reference respiratory rates. Two hundred and ninety-eight Camera Oximeter recordings were available for analysis. The algorithm detected 22 recordings with poor photoplethysmogram quality and 46 recordings with insufficient respiratory information. Of the 232 remaining recordings, a root mean square error of 5.9 breaths/min and a median absolute error of 2.3 breaths/min was obtained. The study showed that it is feasible to estimate respiratory rates by placing a finger on a mobile phone camera, but that it becomes increasingly challenging at respiratory rates higher than 20 breaths/min.
Collapse
|
40
|
Probabilistic Estimation of Respiratory Rate from Wearable Sensors. WEARABLE ELECTRONICS SENSORS 2015. [DOI: 10.1007/978-3-319-18191-2_10] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
|
41
|
Development of a screening tool for sleep disordered breathing in children using the phone Oximeter™. PLoS One 2014; 9:e112959. [PMID: 25401696 PMCID: PMC4234680 DOI: 10.1371/journal.pone.0112959] [Citation(s) in RCA: 79] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2014] [Accepted: 10/13/2014] [Indexed: 11/24/2022] Open
Abstract
Background Sleep disordered breathing (SDB) can lead to daytime sleepiness, growth failure and developmental delay in children. Polysomnography (PSG), the gold standard to diagnose SDB, is a highly resource-intensive test, confined to the sleep laboratory. Aim To combine the blood oxygen saturation (SpO2) characterization and cardiac modulation, quantified by pulse rate variability (PRV), to identify children with SDB using the Phone Oximeter, a device integrating a pulse oximeter with a smartphone. Methods Following ethics approval and informed consent, 160 children referred to British Columbia Children's Hospital for overnight PSG were recruited. A second pulse oximeter sensor applied to the finger adjacent to the one used for standard PSG was attached to the Phone Oximeter to record overnight pulse oximetry (SpO2 and photoplethysmogram (PPG)) alongside the PSG. Results We studied 146 children through the analysis of the SpO2 pattern, and PRV as an estimate of heart rate variability calculated from the PPG. SpO2 variability and SpO2 spectral power at low frequency, was significantly higher in children with SDB due to the modulation provoked by airway obstruction during sleep (p-value ). PRV analysis reflected a significant augmentation of sympathetic activity provoked by intermittent hypoxia in SDB children. A linear classifier was trained with the most discriminating features to identify children with SDB. The classifier was validated with internal and external cross-validation, providing a high negative predictive value (92.6%) and a good balance between sensitivity (88.4%) and specificity (83.6%). Combining SpO2 and PRV analysis improved the classification performance, providing an area under the receiver operating characteristic curve of 88%, beyond the 82% achieved using SpO2 analysis alone. Conclusions These results demonstrate that the implementation of this algorithm in the Phone Oximeter will provide an improved portable, at-home screening tool, with the capability of monitoring patients over multiple nights.
Collapse
|
42
|
Yousefi R, Nourani M. Separating arterial and venous-related components of photoplethysmographic signals for accurate extraction of oxygen saturation and respiratory rate. IEEE J Biomed Health Inform 2014; 19:848-57. [PMID: 25055387 DOI: 10.1109/jbhi.2014.2334697] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
We propose an algorithm for separating arterial and venous-related signals using second-order statistics of red and infrared signals in a blind source separation technique. The separated arterial signal is used to compute accurate arterial oxygen saturation. We have also introduced an algorithm for extracting the respiratory pattern from the extracted venous-related signal. In addition to real-time monitoring, respiratory rate is also extracted. Our experimental results from multiple subjects show that the proposed separation technique is extremely useful for extracting accurate arterial oxygen saturation and respiratory rate. Specifically, the breathing rate is extracted with average root mean square deviation of 1.89 and average mean difference of -0.69.
Collapse
|