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Davies HJ, Hammour G, Xiao H, Bachtiger P, Larionov A, Molyneaux PL, Peters NS, Mandic DP. Physically Meaningful Surrogate Data for COPD. IEEE Open J Eng Med Biol 2024; 5:148-156. [PMID: 38487098 PMCID: PMC10939325 DOI: 10.1109/ojemb.2024.3360688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 11/23/2023] [Accepted: 01/26/2024] [Indexed: 03/17/2024] Open
Abstract
The rapidly increasing prevalence of debilitating breathing disorders, such as chronic obstructive pulmonary disease (COPD), calls for a meaningful integration of artificial intelligence (AI) into respiratory healthcare. Deep learning techniques are "data hungry" whilst patient-based data is invariably expensive and time consuming to record. To this end, we introduce a novel COPD-simulator, a physical apparatus with an easy to replicate design which enables rapid and effective generation of a wide range of COPD-like data from healthy subjects, for enhanced training of deep learning frameworks. To ensure the faithfulness of our domain-aware COPD surrogates, the generated waveforms are examined through both flow waveforms and photoplethysmography (PPG) waveforms (as a proxy for intrathoracic pressure) in terms of duty cycle, sample entropy, FEV1/FVC ratios and flow-volume loops. The proposed simulator operates on healthy subjects and is able to generate FEV1/FVC obstruction ratios ranging from greater than 0.8 to less than 0.2, mirroring values that can observed in the full spectrum of real-world COPD. As a final stage of verification, a simple convolutional neural network is trained on surrogate data alone, and is used to accurately detect COPD in real-world patients. When training solely on surrogate data, and testing on real-world data, a comparison of true positive rate against false positive rate yields an area under the curve of 0.75, compared with 0.63 when training solely on real-world data.
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Affiliation(s)
- Harry J. Davies
- Department of Electrical and Electronic EngineeringImperial College LondonSW7 2BXLondonU.K.
| | - Ghena Hammour
- Department of Electrical and Electronic EngineeringImperial College LondonSW7 2BXLondonU.K.
| | - Hongjian Xiao
- Department of Electrical and Electronic EngineeringImperial College LondonSW7 2BXLondonU.K.
| | - Patrik Bachtiger
- National Heart and Lung InstituteImperial College LondonSW7 2BXLondonU.K.
| | | | | | - Nicholas S. Peters
- National Heart and Lung InstituteImperial College LondonSW7 2BXLondonU.K.
| | - Danilo P. Mandic
- Department of Electrical and Electronic EngineeringImperial College LondonSW7 2BXLondonU.K.
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Brandner CF, Tinsley GM, Graybeal AJ. Smartwatch-based bioimpedance analysis for body composition estimation: precision and agreement with a 4-compartment model. Appl Physiol Nutr Metab 2023; 48:172-182. [PMID: 36462216 DOI: 10.1139/apnm-2022-0301] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
Given that the prevalence of smartwatches has allowed them to become a hallmark in health monitoring, they are primed to provide accessible body composition estimations. The purpose of this study was to evaluate the precision and agreement of smartwatch-based bioimpedance analysis (SW-BIA) and multifrequency bioimpedance analysis (MFBIA) against a 4-compartment (4C) model criterion. A total of 186 participants (114 females) underwent body composition assessments necessary for a 4C model and SW-BIA and MFBIA. Values of total body water (TBW) from each device were compared with those obtained from bioimpedance spectroscopy. Precision was adequate though slightly lower for the smartwatch compared with other methods. No device demonstrated equivalence with the 4C model. Specifically, the SW-BIA overestimated and MFBIA underestimated body fat. MFBIA, but not SW-BIA, demonstrated equivalence for TBW. Overall error was higher for males using the smartwatch compared with females. While these findings do not invalidate the use of smartwatch-based estimates, clinicians should consider that there may be large errors relative to clinical measures. If this wearable device is intended to be used to monitor body composition change over time, these findings demonstrate the need for future research to evaluate its accuracy during follow-up testing.
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Affiliation(s)
- Caleb F Brandner
- School of Kinesiology & Nutrition, College of Education and Human Sciences, University of Southern Mississippi, Hattiesburg, MS 39406, USA
| | - Grant M Tinsley
- Department of Kinesiology & Sport Management, Texas Tech University, Lubbock, TX 79409, USA
| | - Austin J Graybeal
- School of Kinesiology & Nutrition, College of Education and Human Sciences, University of Southern Mississippi, Hattiesburg, MS 39406, USA
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Davies HJ, Williams I, Peters NS, Mandic DP. In-Ear SpO 2: A Tool for Wearable, Unobtrusive Monitoring of Core Blood Oxygen Saturation. Sensors (Basel) 2020; 20:E4879. [PMID: 32872310 PMCID: PMC7506719 DOI: 10.3390/s20174879] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 08/24/2020] [Accepted: 08/24/2020] [Indexed: 02/06/2023]
Abstract
The non-invasive estimation of blood oxygen saturation (SpO2) by pulse oximetry is of vital importance clinically, from the detection of sleep apnea to the recent ambulatory monitoring of hypoxemia in the delayed post-infective phase of COVID-19. In this proof of concept study, we set out to establish the feasibility of SpO2 measurement from the ear canal as a convenient site for long term monitoring, and perform a comprehensive comparison with the right index finger-the conventional clinical measurement site. During resting blood oxygen saturation estimation, we found a root mean square difference of 1.47% between the two measurement sites, with a mean difference of 0.23% higher SpO2 in the right ear canal. Using breath holds, we observe the known phenomena of time delay between central circulation and peripheral circulation with a mean delay between the ear and finger of 12.4 s across all subjects. Furthermore, we document the lower photoplethysmogram amplitude from the ear canal and suggest ways to mitigate this issue. In conjunction with the well-known robustness to temperature induced vasoconstriction, this makes conclusive evidence for in-ear SpO2 monitoring being both convenient and superior to conventional finger measurement for continuous non-intrusive monitoring in both clinical and everyday-life settings.
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Affiliation(s)
- Harry J. Davies
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK; (I.W.); (D.P.M.)
- Imperial Centre for Cardiac Engineering, Imperial College London, London SW7 2AZ, UK;
| | - Ian Williams
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK; (I.W.); (D.P.M.)
- Imperial Centre for Cardiac Engineering, Imperial College London, London SW7 2AZ, UK;
| | - Nicholas S. Peters
- Imperial Centre for Cardiac Engineering, Imperial College London, London SW7 2AZ, UK;
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London SW3 6LY, UK
| | - Danilo P. Mandic
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK; (I.W.); (D.P.M.)
- Imperial Centre for Cardiac Engineering, Imperial College London, London SW7 2AZ, UK;
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Starliper N, Mohammadzadeh F, Songkakul T, Hernandez M, Bozkurt A, Lobaton E. Activity-Aware Wearable System for Power-Efficient Prediction of Physiological Responses. Sensors (Basel) 2019; 19:s19030441. [PMID: 30678188 PMCID: PMC6387359 DOI: 10.3390/s19030441] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2018] [Revised: 01/17/2019] [Accepted: 01/18/2019] [Indexed: 02/04/2023]
Abstract
Wearable health monitoring has emerged as a promising solution to the growing need for remote health assessment and growing demand for personalized preventative care and wellness management. Vital signs can be monitored and alerts can be made when anomalies are detected, potentially improving patient outcomes. One major challenge for the use of wearable health devices is their energy efficiency and battery-lifetime, which motivates the recent efforts towards the development of self-powered wearable devices. This article proposes a method for context aware dynamic sensor selection for power optimized physiological prediction using multi-modal wearable data streams. We first cluster the data by physical activity using the accelerometer data, and then fit a group lasso model to each activity cluster. We find the optimal reduced set of groups of sensor features, in turn reducing power usage by duty cycling these and optimizing prediction accuracy. We show that using activity state-based contextual information increases accuracy while decreasing power usage. We also show that the reduced feature set can be used in other regression models increasing accuracy and decreasing energy burden. We demonstrate the potential reduction in power usage using a custom-designed multi-modal wearable system prototype.
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Affiliation(s)
- Nathan Starliper
- Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC 27695, USA.
| | - Farrokh Mohammadzadeh
- Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC 27695, USA.
| | - Tanner Songkakul
- Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC 27695, USA.
| | - Michelle Hernandez
- Department of Pediatrics, University of North Carolina School of Medicine, Chapel Hill, NC 27516, USA.
| | - Alper Bozkurt
- Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC 27695, USA.
| | - Edgar Lobaton
- Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC 27695, USA.
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Banerjee T, Peterson M, Oliver Q, Froehle A, Lawhorne L. Validating a Commercial Device for Continuous Activity Measurement in the Older Adult Population for Dementia Management. ACTA ACUST UNITED AC 2017; 5-6:51-62. [PMID: 29915807 DOI: 10.1016/j.smhl.2017.11.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
With the introduction of the large number of fitness devices on the market, there are numerous possibilities for their use in managing chronic diseases in older adults. For example, monitoring people with dementia using commercially available devices that measure heart rate, breathing rate, lung volume, step count, and activity level could be used to predict episodic behavioral and psychological symptoms before they become distressing or disruptive. However, since these devices are designed primarily for fitness assessment, validation of the sensors in a controlled environment with the target cohort population is needed. In this study, we present validation results using a commercial fitness tracker, the Hexoskin sensor vest, with thirty-one participants aged 65 and older. Estimated physiological measures investigated in this study are heart rate, breathing rate, lung volume, step count, and activity level of the participants. Findings indicate that while the processed step count, heart rate, and breathing rate show strong correlations to the clinically accepted gold standard values, lung volume and activity level do not. This indicates the need to proceed cautiously when making clinical decisions using such sensors, and suggests that users should focus on the three strongly correlated parameters for further analysis, at least in the older population. The use of physiological measurement devices such as the Hexoskin may eventually become a non-intrusive way to continuously assess physiological measures in older adults with dementia who are at risk for distressing behavioral and psychological symptoms.
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Affiliation(s)
- Tanvi Banerjee
- Department of Computer Science and Engineering, Wright State University, 303 Russ Engineering Building, 3640 Colonel Glenn Highway, Dayton, Ohio 45435 USA
| | - Matthew Peterson
- Boonshoft School of Medicine, Wright State University, 303 Russ Engineering Building, 3640 Colonel Glenn Highway, Dayton, Ohio 45435 USA
| | - Quintin Oliver
- Department of Computer Science and Engineering, Wright State University, 303 Russ Engineering Building, 3640 Colonel Glenn Highway, Dayton, Ohio 45435 USA
| | - Andrew Froehle
- Boonshoft School of Medicine, Wright State University, 303 Russ Engineering Building, 3640 Colonel Glenn Highway, Dayton, Ohio 45435 USA.,Department of Kinesiology and Health,Wright State University, 303 Russ Engineering Building, 3640 Colonel Glenn Highway, Dayton, Ohio 45435 USA
| | - Larry Lawhorne
- Boonshoft School of Medicine, Wright State University, 303 Russ Engineering Building, 3640 Colonel Glenn Highway, Dayton, Ohio 45435 USA
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von Rosenberg W, Chanwimalueang T, Goverdovsky V, Peters NS, Papavassiliou C, Mandic DP. Hearables: feasibility of recording cardiac rhythms from head and in-ear locations. R Soc Open Sci 2017; 4:171214. [PMID: 29291107 PMCID: PMC5717682 DOI: 10.1098/rsos.171214] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2017] [Accepted: 10/23/2017] [Indexed: 06/07/2023]
Abstract
Mobile technologies for the recording of vital signs and neural signals are envisaged to underpin the operation of future health services. For practical purposes, unobtrusive devices are favoured, such as those embedded in a helmet or incorporated onto an earplug. However, these locations have so far been underexplored, as the comparably narrow neck impedes the propagation of vital signals from the torso to the head surface. To establish the principles behind electrocardiogram (ECG) recordings from head and ear locations, we first introduce a realistic three-dimensional biophysics model for the propagation of cardiac electric potentials to the head surface, which demonstrates the feasibility of head-ECG recordings. Next, the proposed biophysics propagation model is verified over comprehensive real-world experiments based on head- and in-ear-ECG measurements. It is shown both that the proposed model is an excellent match for the recordings, and that the quality of head- and ear-ECG is sufficient for a reliable identification of the timing and shape of the characteristic P-, Q-, R-, S- and T-waves within the cardiac cycle. This opens up a range of new possibilities in the identification and management of heart conditions, such as myocardial infarction and atrial fibrillation, based on 24/7 continuous in-ear measurements. The study therefore paves the way for the incorporation of the cardiac modality into future 'hearables', unobtrusive devices for health monitoring.
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Affiliation(s)
- Wilhelm von Rosenberg
- Department of Electrical and Electronic Engineering, Imperial College London, London, UK
| | | | - Valentin Goverdovsky
- Department of Electrical and Electronic Engineering, Imperial College London, London, UK
| | - Nicholas S. Peters
- ElectroCardioMaths Programme, Myocardial Function Section, Imperial College and Imperial NHS Trust, London, UK
| | - Christos Papavassiliou
- Department of Electrical and Electronic Engineering, Imperial College London, London, UK
| | - Danilo P. Mandic
- Department of Electrical and Electronic Engineering, Imperial College London, London, UK
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