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Xu L, Lien J, Li H, Gillian N, Nongpiur R, Li J, Zhang Q, Cui J, Jorgensen D, Bernstein A, Bedal L, Hayashi E, Yamanaka J, Lee A, Wang J, Shin D, Poupyrev I, Thormundsson T, Pathak A, Patel S. Soli-enabled noncontact heart rate detection for sleep and meditation tracking. Sci Rep 2023; 13:18008. [PMID: 37865634 PMCID: PMC10590449 DOI: 10.1038/s41598-023-44714-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 10/11/2023] [Indexed: 10/23/2023] Open
Abstract
Heart rate (HR) is a crucial physiological signal that can be used to monitor health and fitness. Traditional methods for measuring HR require wearable devices, which can be inconvenient or uncomfortable, especially during sleep and meditation. Noncontact HR detection methods employing microwave radar can be a promising alternative. However, the existing approaches in the literature usually use high-gain antennas and require the sensor to face the user's chest or back, making them difficult to integrate into a portable device and unsuitable for sleep and meditation tracking applications. This study presents a novel approach for noncontact HR detection using a miniaturized Soli radar chip embedded in a portable device (Google Nest Hub). The chip has a [Formula: see text] dimension and can be easily integrated into various devices. The proposed approach utilizes advanced signal processing and machine learning techniques to extract HRs from radar signals. The approach is validated on a sleep dataset (62 users, 498 h) and a meditation dataset (114 users, 1131 min). The approach achieves a mean absolute error (MAE) of 1.69 bpm and a mean absolute percentage error (MAPE) of [Formula: see text] on the sleep dataset. On the meditation dataset, the approach achieves an MAE of 1.05 bpm and a MAPE of [Formula: see text]. The recall rates for the two datasets are [Formula: see text] and [Formula: see text], respectively. This study represents the first application of the noncontact HR detection technology to sleep and meditation tracking, offering a promising alternative to wearable devices for HR monitoring during sleep and meditation.
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Affiliation(s)
- Luzhou Xu
- Google LLC, 6420 Sequence Drive, San Diego, CA, 92121, USA.
| | - Jaime Lien
- Google LLC, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA
| | - Haiguang Li
- Google LLC, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA
| | - Nicholas Gillian
- Google LLC, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA
| | - Rajeev Nongpiur
- Google LLC, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA
| | - Jihan Li
- Google LLC, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA
| | - Qian Zhang
- Google LLC, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA
| | - Jian Cui
- Google LLC, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA
| | - David Jorgensen
- Google LLC, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA
| | - Adam Bernstein
- Google LLC, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA
| | - Lauren Bedal
- Google LLC, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA
| | - Eiji Hayashi
- Google LLC, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA
| | - Jin Yamanaka
- Google LLC, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA
| | - Alex Lee
- Google LLC, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA
| | - Jian Wang
- Google LLC, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA
| | - D Shin
- Google LLC, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA
| | - Ivan Poupyrev
- Google LLC, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA
| | | | - Anupam Pathak
- Google LLC, 19510 Jamboree Rd, Irvine, CA, 92612, USA
| | - Shwetak Patel
- Google LLC, 601 North 34st Street, Seattle, WA, 98103, USA
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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.
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SVR-EEMD: An Improved EEMD Method Based on Support Vector Regression Extension in PPG Signal Denoising. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2019; 2019:5363712. [PMID: 31915461 PMCID: PMC6935458 DOI: 10.1155/2019/5363712] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 11/09/2019] [Accepted: 11/25/2019] [Indexed: 11/30/2022]
Abstract
Photoplethysmography (PPG) has been widely used in noninvasive blood volume and blood flow detection since its first appearance. However, its noninvasiveness also makes the PPG signals vulnerable to noise interference and thus exhibits nonlinear and nonstationary characteristics, which have brought difficulties for the denoising of PPG signals. Ensemble empirical mode decomposition known as EEMD, which has made great progress in noise processing, is a noise-assisted nonlinear and nonstationary time series analysis method based on empirical mode decomposition (EMD). The EEMD method solves the “mode mixing” problem in EMD effectively, but it can do nothing about the “end effect,” another problem in the decomposition process. In response to this problem, an improved EEMD method based on support vector regression extension (SVR-EEMD) is proposed and verified by simulated data and real-world PPG data. Experiments show that the SVR-EEMD method can solve the “end effect” efficiently to get a better decomposition performance than the traditional EEMD method and bring more benefits to the noise processing of PPG signals.
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Motin MA, Karmakar CK, Palaniswami M. Robust Heart Rate Estimation During Physical Exercise Using Photoplethysmographic Signals. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:494-497. [PMID: 30440442 DOI: 10.1109/embc.2018.8512405] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
A method for estimating heart rate (HR) from photoplethysmographic (PPG) signal, during physical exercise, is presented in this paper. Accurate and reliable estimation of HR from PPG during intensive physical activity is challenging because intense motion artifacts can easily mask the true HR. If PPG signal is contaminated by intense motion artifacts, the highest peak of PPG spectrum is shifted from true HR due to motion artifacts. The proposed method employs a simple technique using spectral estimation and median filtering for HR estimation from intensely motion artifacts corrupted PPG signal. Experimental result for a database of 12 subjects recorded during fast running showed that the average absolute estimation error was 1.31 beats/minute.
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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. Modified thresholding technique of MMSPCA for extracting respiratory activity from short length 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; 2017:1804-1807. [PMID: 29060239 DOI: 10.1109/embc.2017.8037195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
In this paper, we propose an automatic threshold selection of modified multi scale principal component analysis (MMSPCA) for reliable extraction of respiratory activity (RA) from short length photoplethysmographic (PPG) signals. MMSPCA was applied to the PPG signal with a varying data length, from 30 seconds to 60 seconds, to extract the respiratory activity. To examine the performance, we used 100 epochs of simultaneously recorded PPG and respiratory signals extracted from the MIMIC database (Physionet ATM data bank). The respiratory signal used as the ground truth and several performance measurement metrics such as magnitude squared coherence (MSC), correlation coefficients (CC), and normalized root mean square error (NRMSE) were used to compare the performance of MMSPCA based PPG derived RA. At the data length of 30 seconds, MSC, CC and NRMSE for proposed thresholding were 0.65, 0.62 and -0.82 dB respectively where as they were 0.68, 0.47 and 0.25 dB respectively for existing thresholding. These results illustrated that the proposed threshold selection performs better than existing threshold selection for short length data.
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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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