Evaluation of a wearable device to determine cardiorespiratory parameters from surface diaphragm electromyography

Using wearable devices in clinical routines could reduce healthcare costs and improve the quality of assessment in patients with chronic respiratory diseases. The purpose of this study is to evaluate the capability of a Shimmer3 wearable device device to extract reliable cardiorespiratory parameters from surface diaphragm electromyography (EMGdi). Twenty healthy volunteers underwent an incremental load respiratory test whilst EMGdi was recorded with a Shimmer3 wearable device (EMGdiW). Simultaneously, a second EMGdi (EMGdiL), the inspiratory mouth pressure (Pmouth) and the lead-I electrocardiogram (ECG) were recorded via a standard wired laboratory acquisition system. Different cardiorespiratory parameters have been extracted from both EMGdiW and EMGdiL signals.: heart rate, respiratory rate, respiratory muscle activity and mean frequency of EMGdi signals. Alongside these, similar parameters were also extracted from reference signals (Pmouth and ECG). High correlations were found between the data extracted from the EMGdiW and the reference signal data: heart rate (R = 0.947), respiratory rate (R = 0.940), respiratory muscle activity (R = 0.877), and mean frequency (R = 0.895). Moreover, similar increments in EMGdiW and EMGdiL activity were observed when Pmouth was raised, enabling the study of respiratory muscle activation. In summary, the Shimmer3 device is a promising and cost-effective solution for ambulatory monitoring of respiratory muscle function in chronic respiratory diseases.Postprint (author's final draft

Evaluation of Respiratory Muscle Activity by Means of Concentric Ring Electrodes

© 2021 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] Surface electromyography (sEMG) can be used for the evaluation of respiratory muscle activity. Recording sEMG involves the use of surface electrodes in a bipolar configuration. However, electrocardiographic (ECG) interference and electrode orientation represent considerable drawbacks to bipolar acquisition. As an alternative, concentric ring electrodes (CREs) can be used for sEMG acquisition and offer great potential for the evaluation of respiratory muscle activity due to their enhanced spatial resolution and simple placement protocol, which does not depend on muscle fiber orientation. The aim of this work was to analyze the performance of CREs during respiratory sEMG acquisitions. Respiratory muscle sEMG was applied to the diaphragm and sternocleidomastoid muscles using a bipolar and a CRE configuration. Thirty-two subjects underwent four inspiratory load spontaneous breathing tests which was repeated after interchanging the electrode positions. We calculated parameters such as (1) spectral power and (2) median frequency during inspiration, and power ratios of inspiratory sEMG without ECG in relation to (3) basal sEMG without ECG (R-ins/noise), (4) basal sEMG with ECG (R-ins/cardio) and (5) expiratory sEMG without ECG (R-ins/exp). Spectral power, R-ins/noise and R-ins/cardio increased with the inspiratory load. Significantly higher values (p < 0.05) of R-ins/cardio and significantly higher median frequencies were obtained for CREs. R-ins/noise and R-ins/exp were higher for the bipolar configuration only in diaphragm sEMG recordings, whereas no significant differences were found in the sternocleidomastoid recordings. Our results suggest that the evaluation of respiratory muscle activity by means of sEMG can benefit from the remarkably reduced influence of cardiac activity, the enhanced detection of the shift in frequency content and the axial isotropy of CREs which facilitates its placement.This work was supported in part by the CERCA Program/Generalitat de Catalunya, in part by the Secretaria d'Universitats i Recerca de la Generalitat de Catalunya under Grant GRC 2017 SGR 01770, in part by the Spanish Grants RTI2018-098472-B-I00, RTI2018-094449-A-I00-AR (MCIU/AEI/FEDER, UE) and DPI2015-68397-R (MINECO/FEDER), and in part by the Biomedical Research Networking Centre in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN, Instituto de Salud Carlos III/FEDER). The first author was supported by the IFARHU-SENACYT Scholarship Program from the Panama Government under Grant 270-2012-273.Estrada-Petrocelli, L.; Torres, A.; Sarlabous, L.; Ràfols-De-Urquía, M.; Ye Lin, Y.; Prats-Boluda, G.; Jané, R.... (2021). Evaluation of Respiratory Muscle Activity by Means of Concentric Ring Electrodes. IEEE Transactions on Biomedical Engineering. 68(3):1005-1014. https://doi.org/10.1109/TBME.2020.3012385S1005101468

Breathing Signature as Vitality Score Index Created by Exercises of Qigong: Implications of Artificial Intelligence Tools Used in Traditional Chinese Medicine.

Rising concerns about the short- and long-term detrimental consequences of administration of conventional pharmacopeia are fueling the search for alternative, complementary, personalized, and comprehensive approaches to human healthcare. Qigong, a form of Traditional Chinese Medicine, represents a viable alternative approach. Here, we started with the practical, philosophical, and psychological background of Ki (in Japanese) or Qi (in Chinese) and their relationship to Qigong theory and clinical application. Noting the drawbacks of the current state of Qigong clinic, herein we propose that to manage the unique aspects of the Eastern 'non-linearity' and 'holistic' approach, it needs to be integrated with the Western "linearity" "one-direction" approach. This is done through developing the concepts of "Qigong breathing signatures," which can define our life breathing patterns associated with diseases using machine learning technology. We predict that this can be achieved by establishing an artificial intelligence (AI)-Medicine training camp of databases, which will integrate Qigong-like breathing patterns with different pathologies unique to individuals. Such an integrated connection will allow the AI-Medicine algorithm to identify breathing patterns and guide medical intervention. This unique view of potentially connecting Eastern Medicine and Western Technology can further add a novel insight to our current understanding of both Western and Eastern medicine, thereby establishing a vitality score index (VSI) that can predict the outcomes of lifestyle behaviors and medical conditions

ESTIMATING THE VALUE OF THE VOLUME FROM ACCELERATION ON THE DIAPHRAGM MOVEMENTS DURING BREATHING

Information related to the movements of the diaphragm is very important and it is used in the detection of some respiratory diseases, which are common in all over the world, such as chronic obstructive pulmonary disease (COPD), asthma, and bronchitis. This article describes a practical method for estimating the value of the volume using the acceleration information on the diaphragm movements. The main goal of this paper is to develop a data collection system that measures acceleration values and to estimate the acceleration-volume relationship by examining the obtained data. Thus, two important parameters (TVC and FVC) in the diagnosis of COPD are measured in a more practical way. In the present case, these two parameters can be measured in a hospital environment by an expensive medical device called “spirometry”. For this purpose, our device is placed on the abdomen region of the patient, diaphragm movements are examined and values of the volume are estimated from acceleration data (total 416 accelerometric data). Measurements are performed simultaneously by the spirometry and the developed device. Pearson coefficient (p<0.01) is calculated to determine the correlation between the measured data by using devices. Results show us that there is a positive correlation between measured values of the two devices (accelerometric and spirometric). It can be concluded that there is an acceptable correlation (91.4%) between accelerometric and spirometric results and the estimate error margin is quite low (0.08). In this respect, this study is considered to be an alternative method to spirometry tests, which is used in diagnosing COPD

Telemedicine in home NIV: developing Health Informatics, assessing Physiological response, and Improving Patient Outcomes (THE HIPPO study)

The landscape of digital technology innovations which can assist healthcare provision has expanded rapidly over the past decade. With the adoption of consumer and healthcare-based technologies including mobile device and network access, the use of tele-monitoring in the management of chronic medical conditions will be incorporated into routine clinical care within this generation. Remote patient monitoring has an established role in the management of patients with obstructive sleep apnoea syndrome who require positive airway pressure support. However, the use of two-way remote monitoring via a cloud-based platform to initiate and optimise home non-invasive ventilation (NIV) is novel. Rising obesity rates and new evidence supporting the use of home NIV in patients with severe chronic obstructive pulmonary disease (COPD) and chronic hypercapnic respiratory failure has resulted in increased referrals for breathing support assessment and treatment. Chronic hypercapnic respiratory failure develops as a consequence of imbalance in the respiratory load capacity drive relationship and is associated with high morbidity and mortality. Advanced physiological measurements such as parasternal electromyography (EMG) to quantify neural respiratory drive and forced oscillometry technique to quantify airway resistance and reactance are well established in research but evidence for their clinical application in disease monitoring in patients with sleep disordered breathing and chronic hypercapnic respiratory failure is lacking. The anticipation is that big data from remote monitoring of home breathing support therapies and serial advanced physiological measurements will provide mechanistic insights of chronic respiratory failure, facilitate early optimisation of treatment, prompt early recognition of treatment failure and prioritise at risk patients to provide a personalised approach to the management of chronic respiratory disease. The aim of this thesis was to evaluate the adoption of two-way remote monitoring in patients with sleep disordered breathing and hypercapnic severe COPD and determine the feasibility of serial advanced physiological measurements in chronic respiratory disease. Methods A summary of the evolution of clinical pathways for two-way remote monitored breathing support and home ventilation in NHS Greater Glasgow and Clyde are detailed. A retrospective review of the clinical outcomes in observational cohorts of patients who were managed with two-way remote monitored home NIV for hypercapnic severe COPD and obesity related respiratory failure were evaluated. Clinical outcomes were compared to those of patients who survived a life-threatening exacerbation of COPD with persistent hypercapnic failure who were not referred for breathing support assessment (controls). Four physiological studies were performed. Firstly, the optimisation of parasternal EMG signals using different skin preparation and electrodes was explored. Secondly, inter-observer variability of parasternal EMG analysis between two UK based respiratory physiology research centres was assessed. Thirdly, the simplification of neural respiratory drive index analysis by using EMG signals to estimate respiratory rate to determine the feasibility of future omission of additional sensors improving accessibility. The fourth study explored the feasibility of serial advanced physiological measurements alongside standard care in a wide range of respiratory diseases. Results Clinical pathways for remote management of breathing support patients are now routine clinical care within NHS Greater Glasgow and Clyde. It is feasible and safe to use remote monitored home ventilation in patients with hypercapnic severe COPD. Continued use of two-way remote monitored home NIV prolonged time to re-admission or death in patients with hypercapnic severe COPD when compared to the control cohort. Continued use of remote monitored home NIV in hypercapnic severe COPD resulted in a median reduction of 14 occupied bed days per annum. Continued use of remote NIV prolonged time to re-admission or death in patients with obesity related respiratory failure compared to those non-adherent or discontinued NIV. Two-way remote home NIV can facilitate safe day case initiation of home NIV in patients with stable hypercapnic respiratory failure. It is feasible to use long term cardiac electrodes for parasternal electromyography measurements. Acceptable reproducibility of parasternal EMG analysis between two UK research centres has been demonstrated. The derivation of respiratory rate from parasternal EMG signals is feasible. Serial advanced physiological measurements can be incorporated into standard care in a wide range of respiratory diseases. Serial oscillometry measurements in patients with obstructive sleep apnoea syndrome has provided novel insight into the role a small airways disease. Conclusion The work undertaken in this thesis enabled significant service improvement within NHS Greater Glasgow and Clyde. The utilisation of remote monitoring in disease management provides realistic service provision with tangible service and cost efficiencies, addressing increased service demands and justifying future cost-effective analysis. This work has been a catalyst for ongoing digital innovation projects incorporating EHRs, ambulatory physiological monitoring and home device data into a multi-media multi-disciplinary platform for high-risk COPD patients. Serial advanced physiology data has advocated ongoing studies in acute respiratory failure secondary to COVID-19 infection. Adoption of these new technologies into routine clinical care will address increasing service demands, improve patient outcomes, and provide physiological insights into chronic respiratory failure and COVID-19 related respiratory failure

Cardiorespiratory Function in Young Adults With a History of Covid-19 Infection

Objective. Respiratory complications may persist several months into the recovery period following COVID-19 infection. This study evaluated respiratory function and oxygen saturation variability between young adults with a history of COVID-19 infection and controls. Associations between cardiorespiratory function with potential biobehavioral correlates of COVID-19 infection were also explored.Methods. 57 adults ages 18 to 65 participated in this study (24 COVID+, 33 Control). Spirometry was used to assess pulmonary function volumes of forced vital capacity (FVC), forced expiratory volume in 1 second (FEV1), FEV1/FVC and peak expiratory flow (PEF). Exhaled nitric oxide (FeNO) was measured using the NiOX VERO, a handheld electrochemical nitric oxide analyzer and taken as a proxy of airway inflammation. Systemic inflammation levels were assessed using salivary concentrations of inflammatory biomarkers. Oxygen saturation variability was quantified via extended continuous oxygen saturation (SpO2) monitoring using linear and nonlinear analyses. Network physiology analysis was conducted to evaluate cardiorespiratory control between SpO2, heart rate (HR), respiratory rate and skin temperature signals measured by continuous ambulatory monitoring with an Equivital EQO2 LifeMonitor. Physical activity levels and sedentary time were assessed using 9-day accelerometry. COVID-19 symptom severity was assessed by participant self-report via questionnaires. Results. No group differences were observed for pulmonary function of FVC (COVID+: 4.22±1.01, C: 4.43±1.06 L, p=.663), FEV1 (COVID+: 3.45±0.72, C: 3.57±0.92 L, p=.865), PEF (COVID+: 349.63±105.54, C: 373.73±140.61 L/min, p=.370), or FeNO (COVID+: 16.61±13.04, C: 20.03±20.11 ppb, p=.285). Linear and nonlinear oxygen saturation variability did not differ between adults with a history of COVID-19 infection and controls with no history of infection (p\u3e0.05). Cardiorespiratory function measured using network analysis of did not differ between recovering COVID-19 individuals and controls (p\u3e0.05). Sedentary time was inversely associated with FEV1 (r=-.392, p=.040), PEF (r=-.579, p=.003), and IL-6 concentrations (r=- .370, p=.049). COVID-19 disease severity was inversely associated with FVC (r=-.461, p=.012) and FEV1 (r=-.365, p=.040). Number of symptoms was inversely associated with FVC (r=-.404, p=.025). Conclusions. Pulmonary function, inflammation levels and oxygen saturation variability were similar between individuals with a history of COVID-19 infection and controls without a history of COVID-19 infection. Network interactions between regulatory components of the cardiorespiratory system were also similar between recovering COVID-19 individuals and controls. Findings suggest that cardiorespiratory function and dynamic control of SpO2 may not be impaired following COVID-19 infection in young adults. Moreover, increased sedentary time and disease severity may have negative effects on pulmonary function in individuals recovering from COVID-19

Smart Sensors for Healthcare and Medical Applications

This book focuses on new sensing technologies, measurement techniques, and their applications in medicine and healthcare. Specifically, the book briefly describes the potential of smart sensors in the aforementioned applications, collecting 24 articles selected and published in the Special Issue “Smart Sensors for Healthcare and Medical Applications”. We proposed this topic, being aware of the pivotal role that smart sensors can play in the improvement of healthcare services in both acute and chronic conditions as well as in prevention for a healthy life and active aging. The articles selected in this book cover a variety of topics related to the design, validation, and application of smart sensors to healthcare