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Gazi AH, Sanchez-Perez JA, Saks GL, Alday EAP, Haffar A, Ahmed H, Herraka D, Tarlapally N, Smith NL, Bremner JD, Shah AJ, Inan OT, Vaccarino V. Quantifying Posttraumatic Stress Disorder Symptoms During Traumatic Memories Using Interpretable Markers of Respiratory Variability. IEEE J Biomed Health Inform 2024; 28:4912-4924. [PMID: 38713564 PMCID: PMC11364449 DOI: 10.1109/jbhi.2024.3397589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/09/2024]
Abstract
BACKGROUND Posttraumatic stress disorder (PTSD) causes heightened fight-or-flight responses to traumatic memories (i.e., hyperarousal). Although hyperarousal is hypothesized to cause irregular breathing (i.e., respiratory variability), no quantitative markers of respiratory variability have been shown to correspond with PTSD symptoms in humans. OBJECTIVE In this study, we define interpretable markers of respiration pattern variability (RPV) and investigate whether these markers respond during traumatic memories, correlate with PTSD symptoms, and differ in patients with PTSD. METHODS We recruited 156 veterans from the Vietnam-Era Twin Registry to participate in a trauma recall protocol. From respiratory effort and electrocardiogram measurements, we extracted respiratory timings and rate using a robust quality assessment and fusion approach. We then quantified RPV using the interquartile range and compared RPV between baseline and trauma recall conditions, correlated PTSD symptoms to the difference between trauma recall and baseline RPV (i.e., ∆RPV), and compared ∆RPV between patients with PTSD and trauma-exposed controls. Leveraging a subset of 116 paired twins, we then uniquely controlled for factors shared by co-twins via within-pair analysis for further validation. RESULTS We found RPV was increased during traumatic memories (p .001), ∆ RPV was positively correlated with PTSD symptoms (p .05), and patients with PTSD exhibited higher ∆ RPV than trauma-exposed controls (p . 05). CONCLUSIONS This paper is the first to elucidate RPV markers that respond during traumatic memories, especially in patients with PTSD, and correlate with PTSD symptoms. SIGNIFICANCE These findings encourage future studies outside the clinic, where interpretable markers of respiratory variability are used to track hyperarousal.
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Bhavani T, VamseeKrishna P, Chakraborty C, Dwivedi P. Stress Classification and Vital Signs Forecasting for IoT-Health Monitoring. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024; 21:652-659. [PMID: 35921342 DOI: 10.1109/tcbb.2022.3196151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Health monitoring embedded with intelligence is the demand of the day. In this era of a large population with the emergence of a variety of diseases, the demand for healthcare facilities is high. Yet there is scarcity of medical experts, technicians for providing healthcare to the people affected with some medical problem. This article presents an Internet of Things (IoT) system architecture for health monitoring and how data analytics can be applied in the health sector. IoT is employed to integrate the sensor information, data analytics, machine intelligence and user interface to continuously track and monitor the health condition of the patient. Considering data analytics as the major part, we focused on the implementation of stress classification and forecasted the future values from the recorded data using sensors. Physiological vitals like Pulse, oxygen level percentage (SpO2), temperature, arterial blood pressure along with the patients age, height, weight and movement are considered. Various traditional and ensemble machine learning methods are applied to stress classification data. The experimental results have shown that a hypertuned random forest algorithm has given a better performance with an accuracy of 94.3%. In a view that knowing the future values in prior helps in quick decision making, critical vitals like pulse, oxygen level percentage and blood pressure have been forecasted. The data is trained with ML and neural network models. GRU model has given better performance with lower error rates of 1.76, 0.27, 5.62 RMSE values and 0.845, 0.13, 2.01 MAE values for pulse, SpO2 and blood pressure respectively.
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Castro Ribeiro T, García Pagès E, Ballester L, Vilagut G, García Mieres H, Suárez Aragonès V, Amigo F, Bailón R, Mortier P, Pérez Sola V, Serrano-Blanco A, Alonso J, Aguiló J. Design of a Remote Multiparametric Tool to Assess Mental Well-Being and Distress in Young People (mHealth Methods in Mental Health Research Project): Protocol for an Observational Study. JMIR Res Protoc 2024; 13:e51298. [PMID: 38551647 PMCID: PMC11015365 DOI: 10.2196/51298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 12/22/2023] [Accepted: 01/11/2024] [Indexed: 04/15/2024] Open
Abstract
BACKGROUND Mental health conditions have become a substantial cause of disability worldwide, resulting in economic burden and strain on the public health system. Incorporating cognitive and physiological biomarkers using noninvasive sensors combined with self-reported questionnaires can provide a more accurate characterization of the individual's well-being. Biomarkers such as heart rate variability or those extracted from the electrodermal activity signal are commonly considered as indices of autonomic nervous system functioning, providing objective indicators of stress response. A model combining a set of these biomarkers can constitute a comprehensive tool to remotely assess mental well-being and distress. OBJECTIVE This study aims to design and validate a remote multiparametric tool, including physiological and cognitive variables, to objectively assess mental well-being and distress. METHODS This ongoing observational study pursues to enroll 60 young participants (aged 18-34 years) in 3 groups, including participants with high mental well-being, participants with mild to moderate psychological distress, and participants diagnosed with depression or anxiety disorder. The inclusion and exclusion criteria are being evaluated through a web-based questionnaire, and for those with a mental health condition, the criteria are identified by psychologists. The assessment consists of collecting mental health self-reported measures and physiological data during a baseline state, the Stroop Color and Word Test as a stress-inducing stage, and a final recovery period. Several variables related to heart rate variability, pulse arrival time, breathing, electrodermal activity, and peripheral temperature are collected using medical and wearable devices. A second assessment is carried out after 1 month. The assessment tool will be developed using self-reported questionnaires assessing well-being (short version of Warwick-Edinburgh Mental Well-being Scale), anxiety (Generalized Anxiety Disorder-7), and depression (Patient Health Questionnaire-9) as the reference. We will perform correlation and principal component analysis to reduce the number of variables, followed by the calculation of multiple regression models. Test-retest reliability, known-group validity, and predictive validity will be assessed. RESULTS Participant recruitment is being carried out on a university campus and in mental health services. Recruitment commenced in October 2022 and is expected to be completed by June 2024. As of July 2023, we have recruited 41 participants. Most participants correspond to the group with mild to moderate psychological distress (n=20, 49%), followed by the high mental well-being group (n=13, 32%) and those diagnosed with a mental health condition (n=8, 20%). Data preprocessing is currently ongoing, and publication of the first results is expected by September 2024. CONCLUSIONS This study will establish an initial framework for a comprehensive mental health assessment tool, taking measurements from sophisticated devices, with the goal of progressing toward a remotely accessible and objectively measured approach that maintains an acceptable level of accuracy in clinical practice and epidemiological studies. TRIAL REGISTRATION OSF Registries N3GCH; https://doi.org/10.17605/OSF.IO/N3GCH. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/51298.
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Affiliation(s)
- Thais Castro Ribeiro
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
- Departament of Microelectronics and Electronic Systems, Autonomous University of Barcelona, Bellaterra, Spain
| | - Esther García Pagès
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
- Departament of Microelectronics and Electronic Systems, Autonomous University of Barcelona, Bellaterra, Spain
| | - Laura Ballester
- CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
- Health Services Research Group, Hospital del Mar Research Institute, Barcelona, Spain
| | - Gemma Vilagut
- CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
- Health Services Research Group, Hospital del Mar Research Institute, Barcelona, Spain
| | - Helena García Mieres
- CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
- Health Services Research Group, Hospital del Mar Research Institute, Barcelona, Spain
| | - Víctor Suárez Aragonès
- Department of Clinical Psychology and Psychobiology, University of Barcelona, Barcelona, Spain
| | - Franco Amigo
- CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
- Health Services Research Group, Hospital del Mar Research Institute, Barcelona, Spain
| | - Raquel Bailón
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
- Aragón Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain
| | - Philippe Mortier
- CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
- Health Services Research Group, Hospital del Mar Research Institute, Barcelona, Spain
| | - Víctor Pérez Sola
- CIBER en Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Institute of Neuropsychiatry and Addictions (INAD), Parc de Salut Mar (PSMAR), Barcelona, Spain
- Neurosciences Research Group, Hospital del Mar Research Institute, Barcelona, Spain
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Antoni Serrano-Blanco
- CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
- Institut de Recerca Sant Joan de Déu, Parc Sanitari Sant Joan de Déu, Barcelona, Spain
| | - Jordi Alonso
- CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
- Health Services Research Group, Hospital del Mar Research Institute, Barcelona, Spain
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Jordi Aguiló
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
- Departament of Microelectronics and Electronic Systems, Autonomous University of Barcelona, Bellaterra, Spain
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Catrambone V, Zallocco L, Ramoretti E, Mazzoni MR, Sebastiani L, Valenza G. Integrative neuro-cardiovascular dynamics in response to test anxiety: A brain-heart axis study. Physiol Behav 2024; 276:114460. [PMID: 38215864 DOI: 10.1016/j.physbeh.2024.114460] [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: 10/19/2023] [Revised: 12/08/2023] [Accepted: 01/08/2024] [Indexed: 01/14/2024]
Abstract
Test anxiety (TA), a recognized form of social anxiety, is the most prominent cause of anxiety among students and, if left unmanaged, can escalate to psychiatric disorders. TA profoundly impacts both central and autonomic nervous systems, presenting as a dual manifestation of cognitive and autonomic components. While limited studies have explored the physiological underpinnings of TA, none have directly investigated the intricate interplay between the CNS and ANS in this context. In this study, we introduce a non-invasive, integrated neuro-cardiovascular approach to comprehensively characterize the physiological responses of 27 healthy subjects subjected to test anxiety induced via a simulated exam scenario. Our experimental findings highlight that an isolated analysis of electroencephalographic and heart rate variability data fails to capture the intricate information provided by a brain-heart axis assessment, which incorporates an analysis of the dynamic interaction between the brain and heart. With respect to resting state, the simulated examination induced a decrease in the neural control onto heartbeat dynamics at all frequencies, while the studying condition induced a decrease in the ascending heart-to-brain interplay at EEG oscillations up to 12Hz. This underscores the significance of adopting a multisystem perspective in understanding the complex and especially functional directional mechanisms underlying test anxiety.
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Affiliation(s)
- Vincenzo Catrambone
- Neurocardiovascular Intelligence Laboratory, Department of Information Engineering & Bioengineering and Robotics Research Center E. Piaggio, School of Engineering, University of Pisa, Pisa, Italy.
| | - Lorenzo Zallocco
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Eleonora Ramoretti
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Maria Rosa Mazzoni
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Laura Sebastiani
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy; Institute of Information Science and Technologies A. Faedo, ISTI-CNR, Pisa, Italy
| | - Gaetano Valenza
- Neurocardiovascular Intelligence Laboratory, Department of Information Engineering & Bioengineering and Robotics Research Center E. Piaggio, School of Engineering, University of Pisa, Pisa, Italy
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Peláez-Coca MD, Hernando A, Lozano MT, Bolea J, Izquierdo D, Sánchez C. Heart Rate Variability to Automatically Identify Hyperbaric States Considering Respiratory Component. SENSORS (BASEL, SWITZERLAND) 2024; 24:447. [PMID: 38257541 PMCID: PMC11154234 DOI: 10.3390/s24020447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 01/04/2024] [Accepted: 01/08/2024] [Indexed: 01/24/2024]
Abstract
This study's primary objective was to identify individuals whose physiological responses deviated from the rest of the study population by automatically monitoring atmospheric pressure levels to which they are exposed and using parameters derived from their heart rate variability (HRV). To achieve this, 28 volunteers were placed in a dry hyperbaric chamber, where they experienced varying pressures from 1 to 5 atmospheres, with five sequential stops lasting five minutes each at different atmospheric pressures. The HRV was dissected into two components: the respiratory component, which is linked to respiration; and the residual component, which is influenced by factors beyond respiration. Nine parameters were assessed, including the respiratory rate, four classic HRV temporal parameters, and four frequency parameters. A k-nearest neighbors classifier based on cosine distance successfully identified the atmospheric pressures to which the subjects were exposed to. The classifier achieved an 88.5% accuracy rate in distinguishing between the 5 atm and 3 atm stages using only four features: respiratory rate, heart rate, and two frequency parameters associated with the subjects' sympathetic responses. Furthermore, the study identified 6 out of 28 subjects as having atypical responses across all pressure levels when compared to the majority. Interestingly, two of these subjects stood out in terms of gender and having less prior diving experience, but they still exhibited normal responses to immersion. This suggests the potential for establishing distinct safety protocols for divers based on their previous experience and gender.
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Affiliation(s)
- María Dolores Peláez-Coca
- Centro Universitario de la Defensa de Zaragoza, 50090 Zaragoza, Spain; (M.T.L.); (J.B.)
- BSICoS Group, I3A Institute, University of Zaragoza, IIS Aragón, 50009 Zaragoza, Spain; (A.H.); (C.S.)
| | - Alberto Hernando
- BSICoS Group, I3A Institute, University of Zaragoza, IIS Aragón, 50009 Zaragoza, Spain; (A.H.); (C.S.)
| | - María Teresa Lozano
- Centro Universitario de la Defensa de Zaragoza, 50090 Zaragoza, Spain; (M.T.L.); (J.B.)
- BSICoS Group, I3A Institute, University of Zaragoza, IIS Aragón, 50009 Zaragoza, Spain; (A.H.); (C.S.)
| | - Juan Bolea
- Centro Universitario de la Defensa de Zaragoza, 50090 Zaragoza, Spain; (M.T.L.); (J.B.)
| | - David Izquierdo
- GTF Group, I3A Institute, University of Zaragoza, 50009 Zaragoza, Spain;
| | - Carlos Sánchez
- BSICoS Group, I3A Institute, University of Zaragoza, IIS Aragón, 50009 Zaragoza, Spain; (A.H.); (C.S.)
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Zhang J, Li WC, Braithwaite G, Blundell J. Practice effects of a breathing technique on pilots' cognitive and stress associated heart rate variability during flight operations. Stress 2024; 27:2361253. [PMID: 38859613 DOI: 10.1080/10253890.2024.2361253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 05/22/2024] [Indexed: 06/12/2024] Open
Abstract
Commercial pilots endure multiple stressors in their daily and occupational lives which are detrimental to psychological well-being and cognitive functioning. The Quick coherence technique (QCT) is an effective intervention tool to improve stress resilience and psychophysiological balance based on a five-minute paced breathing exercise with heart rate variability (HRV) biofeedback. The current research reports on the application of QCT training within an international airline to improve commercial pilots' psychological health and support cognitive functions. Forty-four commercial pilots volunteered in a one-month training programme to practise self-regulated QCT in day-to-day life and flight operations. Pilots' stress index, HRV time-domain and frequency-domain parameters were collected to examine the influence of QCT practice on the stress resilience process. The results demonstrated that the QCT improved psychophysiological indicators associated with stress resilience and cognitive functions, in both day-to-day life and flight operation settings. HRV fluctuations, as measured through changes in RMSSD and LF/HF, revealed that the resilience processes were primarily controlled by the sympathetic nervous system activities that are important in promoting pilots' energy mobilization and cognitive functions, thus QCT has huge potential in facilitating flight performance and aviation safety. These findings provide scientific evidence for implementing QCT as an effective mental support programme and controlled rest strategy to improve pilots' psychological health, stress management, and operational performance.
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Affiliation(s)
- Jingyi Zhang
- Safety and Accident Investigation Centre, Cranfield University, Bedfordshire, UK
| | - Wen-Chin Li
- Safety and Accident Investigation Centre, Cranfield University, Bedfordshire, UK
| | - Graham Braithwaite
- Safety and Accident Investigation Centre, Cranfield University, Bedfordshire, UK
| | - James Blundell
- Safety and Accident Investigation Centre, Cranfield University, Bedfordshire, UK
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Fan L, Baharum MR. The effect of exposure to natural sounds on stress reduction: a systematic review and meta-analysis. Stress 2024; 27:2402519. [PMID: 39285764 DOI: 10.1080/10253890.2024.2402519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 09/03/2024] [Indexed: 10/08/2024] Open
Abstract
The main aim of this review is to compare whether natural sounds or a quiet environment is more beneficial for alleviating stress. The results showed that there is a statistically significant difference between exposure to natural sounds and a quiet environment in terms of their effect on heart rate (p = 0.006), blood pressure (p = 0.001), and respiratory rate (p = 0.032). However, no significant difference was found between exposure to natural sounds and a quiet environment in terms of their effect on MAP (p = 0.407), perceived stress, and SPO2 (p = 0.251). Although the evidence was slightly inconsistent, overall, natural sounds were found more beneficial for stress reduction than quiet environments.
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Affiliation(s)
- Luyao Fan
- Faculty of Built Environment, Universiti Malaya, Kuala Lumpur, Malaysia
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Yao Q, Gu H, Wang S, Liang G, Zhao X, Li X. Exploring EEG characteristics of multi-level mental stress based on human-machine system. J Neural Eng 2023; 20:056023. [PMID: 37729925 DOI: 10.1088/1741-2552/acfbba] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 09/20/2023] [Indexed: 09/22/2023]
Abstract
Objective.The understanding of cognitive states is important for the development of human-machine systems (HMSs), and one of the fundamental but challenging issues is the understanding and assessment of the operator's mental stress state in real task scenarios.Approach.In this paper, a virtual unmanned vehicle (UAV) driving task with multi-challenge-level was created to explore the operator's mental stress, and the human brain activity during the task was tracked in real time via electroencephalography (EEG). A mental stress analysis dataset for the virtual UAV task was then developed and used to explore the neural activation patterns associated with mental stress activity. Finally, a multiple attention-based convolutional neural network (MACN) was constructed for automatic stress assessment using the extracted stress-sensitive neural activation features.Main Results.The statistical results of EEG power spectral density (PSD) showed that frontal theta-PSD decreased with increasing task difficulty, and central beta-PSD increased with increasing task difficulty, indicating that neural patterns showed different trends under different levels of mental stress. The performance of the proposed MACN was evaluated based on the dimensional model, and results showed that average three-class classification accuracies of 89.49%/89.88% were respectively achieved for arousal/valence.Significance.The results of this paper suggest that objective assessment of mental stress in a HMS based on a virtual UAV scenario is feasible, and the proposed method provides a promising solution for cognitive computing and applications in human-machine tasks.
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Affiliation(s)
- Qunli Yao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, People's Republic of China
| | - Heng Gu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, People's Republic of China
| | - Shaodi Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, People's Republic of China
| | - Guanhao Liang
- Center for Cognition and Neuroergonomics, Beijing Normal University, Zhuhai 519087, People's Republic of China
| | - Xiaochuan Zhao
- Institute of Computer Applied Technology of China North Industries Group Corporation Limited, Beijing 100821, People's Republic of China
| | - Xiaoli Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, People's Republic of China
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Dillard CC, Martaindale H, Hunter SD, McAllister MJ. Slow Breathing Reduces Biomarkers of Stress in Response to a Virtual Reality Active Shooter Training Drill. Healthcare (Basel) 2023; 11:2351. [PMID: 37628548 PMCID: PMC10454504 DOI: 10.3390/healthcare11162351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 08/08/2023] [Accepted: 08/19/2023] [Indexed: 08/27/2023] Open
Abstract
Tactical occupations regularly encounter life-threatening situations while on duty. Although these occupations are often trained to utilize slow breathing (SB) during intense stress, there is no evidence supporting the effects on markers of stress in response to a virtual reality active shooter training drill (VR-ASD). The purpose of the study was to determine the impact of acute SB on biomarkers of stress in response to a VR-ASD. Seventy-nine (n = 79) subjects performed either slow breathing method 1 (SB1), slow breathing method 2 (SB2), or normal breathing (control) for five minutes, both pre- and post-VR-ASD. Saliva samples were analyzed for stress markers, including α-amylase (sAA) and secretory immunoglobulin-A (SIgA). Both methods of SB resulted in significantly lower sAA concentrations at 5 (p < 0.001) and 30 min post-VR-ASD (SB1: p = 0.008; SB2: p < 0.001) compared to the control. In the control condition, the sAA concentrations were significantly elevated 5 min post-VR-ASD (p < 0.001) but did not change across time in SB1 or SB2 (p > 0.05). Thus, both SB1 and SB2 reduced the sAA response and resulted in lower concentrations post-VR-ASD. This study was pre-registered as a clinical trial ("Impact of Breathing Interventions on Stress Markers"; NCT05825846).
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Affiliation(s)
- Courtney C. Dillard
- Metabolic & Applied Physiology Lab, Texas State University, San Marcos, TX 78666, USA
| | | | - Stacy D. Hunter
- Metabolic & Applied Physiology Lab, Texas State University, San Marcos, TX 78666, USA
| | - Matthew J. McAllister
- Metabolic & Applied Physiology Lab, Texas State University, San Marcos, TX 78666, USA
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Choi SO, Choi JG, Yun JY. A Study of Brain Function Characteristics of Service Members at High Risk for Accidents in the Military. Brain Sci 2023; 13:1157. [PMID: 37626513 PMCID: PMC10452066 DOI: 10.3390/brainsci13081157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 07/23/2023] [Accepted: 07/29/2023] [Indexed: 08/27/2023] Open
Abstract
Military accidents are often associated with stress and depressive psychological conditions among soldiers, and they often fail to adapt to military life. Therefore, this study analyzes whether there are differences in EEG and pulse wave indices between general soldiers and three groups of soldiers who have not adapted to military life and are at risk of accidents. Data collection was carried out using a questionnaire and a device that can measure EEG and pulse waves, and data analysis was performed using SPSS. The results showed that the concentration level and brain activity indices were higher in the general soldiers and the soldiers in the first stage of accident risk. The body stress index was higher for each stage of accident risk, and the physical vitality index was higher for general soldiers. Therefore, it can be seen that soldiers who have not adapted to military life and are at risk of accidents have somewhat lower concentration and brain activity than general soldiers, and have symptoms of stress and lethargy. The results of this study will contribute to reducing human accidents through EEG and pulse wave measurements not only in the military but also in occupations with a high risk of accidents such as construction.
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Affiliation(s)
| | | | - Jong-Yong Yun
- Department of Protection and Safety Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea
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Sánchez C, Hernando A, Bolea J, Izquierdo D, Rodríguez G, Olea A, Lozano MT, Peláez-Coca MD. Enhancing Safety in Hyperbaric Environments through Analysis of Autonomic Nervous System Responses: A Comparison of Dry and Humid Conditions. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115289. [PMID: 37300016 DOI: 10.3390/s23115289] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 05/26/2023] [Accepted: 05/31/2023] [Indexed: 06/12/2023]
Abstract
Diving can have significant cardiovascular effects on the human body and increase the risk of developing cardiac health issues. This study aimed to investigate the autonomic nervous system (ANS) responses of healthy individuals during simulated dives in hyperbaric chambers and explore the effects of the humid environment on these responses. Electrocardiographic- and heart-rate-variability (HRV)-derived indices were analyzed, and their statistical ranges were compared at different depths during simulated immersions under dry and humid conditions. The results showed that humidity significantly affected the ANS responses of the subjects, leading to reduced parasympathetic activity and increased sympathetic dominance. The power of the high-frequency band of the HRV after removing the influence of respiration, PHF⟂¯, and the number of pairs of successive normal-to-normal intervals that differ by more than 50 ms divided by the total number of normal-to-normal intervals, pNN50¯, indices were found to be the most informative in distinguishing the ANS responses of subjects between the two datasets. Additionally, the statistical ranges of the HRV indices were calculated, and the classification of subjects as "normal" or "abnormal" was determined based on these ranges. The results showed that the ranges were effective at identifying abnormal ANS responses, indicating the potential use of these ranges as a reference for monitoring the activity of divers and avoiding future immersions if many indices are out of the normal ranges. The bagging method was also used to include some variability in the datasets' ranges, and the classification results showed that the ranges computed without proper bagging represent reality and its associated variability. Overall, this study provides valuable insights into the ANS responses of healthy individuals during simulated dives in hyperbaric chambers and the effects of humidity on these responses.
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Affiliation(s)
- Carlos Sánchez
- BSICoS Group, I3A Institute, IIS Aragón, University of Zaragoza, 50018 Zaragoza, Spain
| | - Alberto Hernando
- BSICoS Group, I3A Institute, IIS Aragón, University of Zaragoza, 50018 Zaragoza, Spain
| | - Juan Bolea
- Departamento de Física, Centro Universitario de la Defensa de Zaragoza, Academia General Militar, 50090 Zaragoza, Spain
| | - David Izquierdo
- GTF Group, I3A Institute, University of Zaragoza, 50018 Zaragoza, Spain
| | - Germán Rodríguez
- Departamento de Ingeniería y Técnicas Aplicadas, Centro Universitario de la Defensa de San Javier, Academia General del Aire, 30729 Murcia, Spain
| | - Agustín Olea
- Centro de Buceo de la Armada de Cartagena, 30205 Murcia, Spain
| | - María Teresa Lozano
- BSICoS Group, I3A Institute, IIS Aragón, University of Zaragoza, 50018 Zaragoza, Spain
- Departamento de Física, Centro Universitario de la Defensa de Zaragoza, Academia General Militar, 50090 Zaragoza, Spain
| | - María Dolores Peláez-Coca
- BSICoS Group, I3A Institute, IIS Aragón, University of Zaragoza, 50018 Zaragoza, Spain
- Departamento de Física, Centro Universitario de la Defensa de Zaragoza, Academia General Militar, 50090 Zaragoza, Spain
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12
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Castro Ribeiro T, Sobregrau Sangrà P, García Pagès E, Badiella L, López-Barbeito B, Aguiló S, Aguiló J. Assessing effectiveness of heart rate variability biofeedback to mitigate mental health symptoms: a pilot study. Front Physiol 2023; 14:1147260. [PMID: 37234414 PMCID: PMC10206049 DOI: 10.3389/fphys.2023.1147260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 04/24/2023] [Indexed: 05/28/2023] Open
Abstract
Introduction: The increasing burden on mental health has become a worldwide concern especially due to its substantial negative social and economic impact. The implementation of prevention actions and psychological interventions is crucial to mitigate these consequences, and evidence supporting its effectiveness would facilitate a more assertive response. Heart rate variability biofeedback (HRV-BF) has been proposed as a potential intervention to improve mental wellbeing through mechanisms in autonomic functioning. The aim of this study is to propose and evaluate the validity of an objective procedure to assess the effectiveness of a HRV-BF protocol in mitigating mental health symptoms in a sample of frontline HCWs (healthcare workers) who worked in the COVID-19 pandemic. Methods: A prospective experimental study applying a HRV-BF protocol was conducted with 21 frontline healthcare workers in 5 weekly sessions. For PRE-POST intervention comparisons, two different approaches were used to evaluate mental health status: applying (a) gold-standard psychometric questionnaires and (b) electrophysiological multiparametric models for chronic and acute stress assessment. Results: After HRV-BF intervention, psychometric questionnaires showed a reduction in mental health symptoms and stress perception. The electrophysiological multiparametric also showed a reduction in chronic stress levels, while the acute stress levels were similar in PRE and POST conditions. A significant reduction in respiratory rate and an increase in some heart rate variability parameters, such as SDNN, LFn, and LF/HF ratio, were also observed after intervention. Conclusion: Our findings suggest that a 5-session HRV-BF protocol is an effective intervention for reducing stress and other mental health symptoms among frontline HCWs who worked during the COVID-19 pandemic. The electrophysiological multiparametric models provide relevant information about the current mental health state, being useful for objectively evaluating the effectiveness of stress-reducing interventions. Further research could replicate the proposed procedure to confirm its feasibility for different samples and specific interventions.
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Affiliation(s)
- Thais Castro Ribeiro
- Biomedical Research Network Center in Biogineering, Biomaterial and Nanomedicine (CIBER-BBN), Madrid, Spain
- Department of Microelectronics and Electronic Systems, Autonomous University of Barcelona, Barcelona, Spain
| | - Pau Sobregrau Sangrà
- Clínic Foundation for Biomedical Research, Hospital Clínic of Barcelona, Barcelona, Spain
| | - Esther García Pagès
- Department of Microelectronics and Electronic Systems, Autonomous University of Barcelona, Barcelona, Spain
| | - Llorenç Badiella
- Applied Statistics Service, Autonomous University of Barcelona, Barcelona, Spain
| | | | - Sira Aguiló
- Emergency Department, Hospital Clínic of Barcelona, Barcelona, Spain
| | - Jordi Aguiló
- Biomedical Research Network Center in Biogineering, Biomaterial and Nanomedicine (CIBER-BBN), Madrid, Spain
- Department of Microelectronics and Electronic Systems, Autonomous University of Barcelona, Barcelona, Spain
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13
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Kaplan J, Colgan DD, Klee D, Hanes D, Oken BS. Patterns of Respiration Rate Reactivity in Response to a Cognitive Stressor Associate With Self-Reported Mental Health Outcomes. Psychol Rep 2023:332941231171887. [PMID: 37083201 DOI: 10.1177/00332941231171887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/22/2023]
Abstract
Many studies have examined physiological responses to acute stress in healthy and clinical populations. Some have documented exaggerated physiological stress reactivity in response to acute stress, while others have reported blunted physiological stress reactivity. Although the literature is conflicted, the relationship between abnormal physiological stress reactivity and negative outcomes is well-established. However, past research has neglected a critical aspect of physiological stress response - respiration - and it is unclear whether differences in respiration rate responses to acute stress are related to health outcomes. This secondary cross-sectional analysis explored differences in outcomes between three subgroups: blunted, moderate, and exaggerated respiration rate reactivity to an acute stress task. In a sample of at least mildly-stressed older adults (n = 55), we found that perceived stress (b = -7.63; p = .004) and depression (b = -9.13; p = .007) were significantly lower in the moderate reactivity group compared to the high reactivity group, and that self-reported mindfulness (b = 10.96; p = .008) was significantly lower in the moderate reactivity group as compared to the low reactivity group. Across outcomes, participants in the moderate range of physiological reactivity showed less negative and more positive psychological attributes and better health outcomes, while the blunted subgroup demonstrated more negative and less positive psychological attributes and worse health outcomes overall, when compared to the exaggerated and moderate groups. This study extends the literature by adding respiration to markers of acute physiological stress reactivity and demonstrating the effects of blunted respiration reactivity on negative psychological attributes and health outcomes.
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Affiliation(s)
- Josh Kaplan
- Oregon Health and Science University, Department of Neurology, Portland, OR, USA
| | | | - Daniel Klee
- Oregon Health and Science University, Department of Neurology, Portland, OR, USA
| | - Douglas Hanes
- Providence Center for Cardiovascular Analytics, Research + Data Science
| | - Barry S Oken
- Oregon Health and Science University, Departments of Behavioral Neuroscience and Biomedical Engineering, Portland, OR, USA
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14
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Boccignone G, D’Amelio A, Ghezzi O, Grossi G, Lanzarotti R. An Evaluation of Non-Contact Photoplethysmography-Based Methods for Remote Respiratory Rate Estimation. SENSORS (BASEL, SWITZERLAND) 2023; 23:3387. [PMID: 37050444 PMCID: PMC10098914 DOI: 10.3390/s23073387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Revised: 03/16/2023] [Accepted: 03/21/2023] [Indexed: 06/19/2023]
Abstract
The respiration rate (RR) is one of the physiological signals deserving monitoring for assessing human health and emotional states. However, traditional devices, such as the respiration belt to be worn around the chest, are not always a feasible solution (e.g., telemedicine, device discomfort). Recently, novel approaches have been proposed aiming at estimating RR in a less invasive yet reliable way, requiring the acquisition and processing of contact or remote Photoplethysmography (contact reference and remote-PPG, respectively). The aim of this paper is to address the lack of systematic evaluation of proposed methods on publicly available datasets, which currently impedes a fair comparison among them. In particular, we evaluate two prominent families of PPG processing methods estimating Respiratory Induced Variations (RIVs): the first encompasses methods based on the direct extraction of morphological features concerning the RR; and the second group includes methods modeling respiratory artifacts adopting, in the most promising cases, single-channel blind source separation. Extensive experiments have been carried out on the public BP4D+ dataset, showing that the morphological estimation of RIVs is more reliable than those produced by a single-channel blind source separation method (both in contact and remote testing phases), as well as in comparison with a representative state-of-the-art Deep Learning-based approach for remote respiratory information estimation.
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Affiliation(s)
| | | | | | | | - Raffaella Lanzarotti
- PHuSe Laboratory—Dipartimento di Informatica, Università degli Studi di Milano, Via Celoria 18, 20133 Milano, Italy
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15
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Li X, Deng X, Huang Z, Kowark A, Coburn M, Zhang G, Duan X. Relationship between Heart Rate Variability and Postoperative Cognitive Dysfunction in Elderly Patients. Am J Health Behav 2023; 47:65-74. [PMID: 36945090 DOI: 10.5993/ajhb.47.1.8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
Objectives: Postoperative cognitive dysfunction (POCD) is objectively measurable after anesthesia and surgery. Lower heart rate variability (HRV) is associated with poorer cognitive performance, but the relationship between HRV and POCD remains unclear. Methods: Elderly patients who underwent total hip replacement under general anesthesia from the Department of Bone and Joint Surgery, Affiliated Hospital of Southwest Medical University were enrolled. Neuropsychological tests, standard deviation of the interbeat interval (SDNN, a parameter of HRV), and plasma concentrations of glial cell line-derived neurotrophic factors (GDNF) were performed one day before (T-1) and 7 days after (T7) surgery. Results: POCD occurred in 35% of patients on 7 days after surgery. Lower SDNN(T7) (OR=.91) and longer surgery time (OR=1.33) were associated with POCD. Compared with patients without POCD, there was higher variation SDNN (Δ SDNN) and plasma GDNF (ΔGDNF) in those with POCD from T-1 to T7 period. ΔGDNF is positively correlated with ΔSDNN (r = .61, p<.001). Conclusions: Lower SDNN (T7) was associated with POCD and might be used as a warning indicator for the risk of POCD.
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Affiliation(s)
- Xuelian Li
- Associate Professor, Department of Anesthesiology, The Affiliated Hospital of Southwest Medical University, Anesthesiology and Critical Care Medicine Key Laboratory of Luzhou, Southwest Medical University, Department of Anesthesiology, Southwest Medical University, Luzhou, China
| | - Xiren Deng
- Associate Professor, Department of Anesthesiology, The Affiliated Hospital of Southwest Medical University, Anesthesiology and Critical Care Medicine Key Laboratory of Luzhou, Southwest Medical University, Department of Anesthesiology, Southwest Medical University, Luzhou, China
| | - Zhiwei Huang
- Professor, School of Medical Information and Engineering, Southwest Medical University, Luzhou, China
| | - Ana Kowark
- Department of Anesthesiology and Intensive Care Medicine, University Hospital Bonn, Bonn, Germany
| | - Mark Coburn
- Department of Anesthesiology and Intensive Care Medicine, University Hospital Bonn, Bonn, Germany
| | - Guanpeng Zhang
- Department of Electrocardiogram, The Affiliated Hospital of Southwest Medical University, Luzhou, China;,
| | - Xiaoxia Duan
- Associate Professor, Department of Anesthesiology, The Affiliated Hospital of Southwest Medical University, Anesthesiology and Critical Care Medicine Key Laboratory of Luzhou, Southwest Medical University, Department of Anesthesiology, Southwest Medical University, Luzhou, China;,
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16
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Bouchal SM, Meyer JH, Bendok BR. Commentary: Physiological Responses and Training Satisfaction During National Rollout of a Neurosurgical Intraoperative Catastrophe Simulator for Resident Training. Oper Neurosurg (Hagerstown) 2023; 24:e139-e141. [PMID: 36637327 DOI: 10.1227/ons.0000000000000548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 09/27/2022] [Indexed: 01/14/2023] Open
Affiliation(s)
| | - Jenna H Meyer
- Neurosurgery Simulation and Innovation Lab, Department of Neurologic Surgery, Mayo Clinic, Phoenix, Arizona, USA
| | - Bernard R Bendok
- Neurosurgery Simulation and Innovation Lab, Department of Neurologic Surgery, Mayo Clinic, Phoenix, Arizona, USA
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17
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García Pagès E, Arza A, Lazaro J, Puig C, Castro T, Ottaviano M, Arredondo MT, Bernal ML, López-Antón R, Cámara CDL, Gil E, Laguna P, Bailón R, Aguiló J, Garzón-Rey JM. Psychosomatic response to acute emotional stress in healthy students. Front Physiol 2023; 13:960118. [PMID: 36699693 PMCID: PMC9870289 DOI: 10.3389/fphys.2022.960118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 11/29/2022] [Indexed: 01/11/2023] Open
Abstract
The multidimensionality of the stress response has shown the complexity of this phenomenon and therefore the impossibility of finding a unique biomarker among the physiological variables related to stress. An experimental study was designed and performed to guarantee the correct synchronous and concurrent measure of psychometric tests, biochemical variables and physiological features related to acute emotional stress. The population studied corresponds to a group of 120 university students between 20 and 30 years of age, with healthy habits and without a diagnosis of chronic or psychiatric illnesses. Following the protocol of the experimental pilot, each participant reached a relaxing state and a stress state in two sessions of measurement for equivalent periods. Both states are correctly achieved evidenced by the psychometric test results and the biochemical variables. A Stress Reference Scale is proposed based on these two sets of variables. Then, aiming for a non-invasive and continuous approach, the Acute Stress Model correlated to the previous scale is also proposed, supported only by physiological signals. Preliminary results support the feasibility of measuring/quantifying the stress level. Although the results are limited to the population and stimulus type, the procedure and methodological analysis used for the assessment of acute stress in young people can be extrapolated to other populations and types of stress.
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Affiliation(s)
- Esther García Pagès
- Universitat Autònoma de Barcelona, UAB, Barcelona, Spain,*Correspondence: Esther García Pagès,
| | - Adriana Arza
- École Polytechnique Fédérale de Lausanne, EPFL, Lausanne, Switzerland
| | | | - Carlos Puig
- Universitat Autònoma de Barcelona, UAB, Barcelona, Spain
| | - Thais Castro
- Universitat Autònoma de Barcelona, UAB, Barcelona, Spain,Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, CIBER-BBN, Madrid, Spain
| | - Manuel Ottaviano
- Life Supporting Technologies, Universidad Politécnica de Madrid, UPM, Madrid, Spain
| | | | | | | | | | - Eduardo Gil
- Universidad de Zaragoza, UZ, Zaragoza, Spain
| | - Pablo Laguna
- Universidad de Zaragoza, UZ, Zaragoza, Spain,Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, CIBER-BBN, Madrid, Spain
| | - Raquel Bailón
- Universidad de Zaragoza, UZ, Zaragoza, Spain,Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, CIBER-BBN, Madrid, Spain
| | - Jordi Aguiló
- Universidad de Zaragoza, UZ, Zaragoza, Spain,Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, CIBER-BBN, Madrid, Spain
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18
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Feasibility study for detection of mental stress and depression using pulse rate variability metrics via various durations. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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19
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Park C, Youn I, Han S. Single-lead ECG based autonomic nervous system assessment for meditation monitoring. Sci Rep 2022; 12:22513. [PMID: 36581715 PMCID: PMC9800362 DOI: 10.1038/s41598-022-27121-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 12/26/2022] [Indexed: 12/30/2022] Open
Abstract
We propose a single-lead ECG-based heart rate variability (HRV) analysis algorithm to quantify autonomic nervous system activity during meditation. Respiratory sinus arrhythmia (RSA) induced by breathing is a dominant component of HRV, but its frequency depends on an individual's breathing speed. To address this RSA issue, we designed a novel HRV tachogram decomposition algorithm and new HRV indices. The proposed method was validated by using a simulation, and applied to our experimental (mindfulness meditation) data and the WESAD open-source data. During meditation, our proposed HRV indices related to vagal and sympathetic tones were significantly increased (p < 0.000005) and decreased (p < 0.000005), respectively. These results were consistent with self-reports and experimental protocols, and identified parasympathetic activation and sympathetic inhibition during meditation. In conclusion, the proposed method successfully assessed autonomic nervous system activity during meditation when respiration influences disrupted classical HRV. The proposed method can be considered a reliable approach to quantify autonomic nervous system activity.
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Affiliation(s)
- Chanki Park
- grid.36303.350000 0000 9148 4899Future and Basic Technology Research Division, ICT Creative Research Laboratory, Electronics and Telecommunications Research Institute, CybreBrain Research Section, Daejeon, 34129 Republic of Korea
| | - Inchan Youn
- grid.35541.360000000121053345Bionics Research Center, Biomedical Research Division, Korea Institute of Science and Technology, Seoul, 02792 Republic of Korea ,grid.35541.360000000121053345Division of Bio‑Medical Science and Technology, Korea Institute of Science and Technology School, Seoul, 02792 Republic of Korea ,grid.289247.20000 0001 2171 7818KHU-KIST Department of Converging Science and Technology, Kyung Hee University, Seoul, Seongbuk-gu 02447 Republic of Korea
| | - Sungmin Han
- grid.35541.360000000121053345Bionics Research Center, Biomedical Research Division, Korea Institute of Science and Technology, Seoul, 02792 Republic of Korea ,grid.35541.360000000121053345Division of Bio‑Medical Science and Technology, Korea Institute of Science and Technology School, Seoul, 02792 Republic of Korea
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20
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Taoum A, Bisiaux A, Tilquin F, Le Guillou Y, Carrault G. Validity of Ultra-Short-Term HRV Analysis Using PPG-A Preliminary Study. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22207995. [PMID: 36298346 PMCID: PMC9611389 DOI: 10.3390/s22207995] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 10/17/2022] [Accepted: 10/18/2022] [Indexed: 05/26/2023]
Abstract
Continuous measurement of heart rate variability (HRV) in the short and ultra-short-term using wearable devices allows monitoring of physiological status and prevention of diseases. This study aims to evaluate the agreement of HRV features between a commercial device (Bora Band, Biosency) measuring photoplethysmography (PPG) and reference electrocardiography (ECG) and to assess the validity of ultra-short-term HRV as a surrogate for short-term HRV features. PPG and ECG recordings were acquired from 5 healthy subjects over 18 nights in total. HRV features include time-domain, frequency-domain, nonlinear, and visibility graph features and are extracted from 5 min 30 s and 1 min 30 s duration PPG recordings. The extracted features are compared with reference features of 5 min 30 s duration ECG recordings using repeated-measures correlation, Bland-Altman plots with 95% limits of agreements, Cliff's delta, and an equivalence test. Results showed agreement between PPG recordings and ECG reference recordings for 37 out of 48 HRV features in short-term durations. Sixteen of the forty-eight HRV features were valid and retained very strong correlations, negligible to small bias, with statistical equivalence in the ultra-short recordings (1 min 30 s). The current study concludes that the Bora Band provides valid and reliable measurement of HRV features in short and ultra-short duration recordings.
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Affiliation(s)
- Aline Taoum
- Laboratoire Traitement du Signal et de l’Image (LTSI-Inserm UMR 1099), Université de Rennes 1, 35042 Rennes, France
| | | | | | | | - Guy Carrault
- Laboratoire Traitement du Signal et de l’Image (LTSI-Inserm UMR 1099), Université de Rennes 1, 35042 Rennes, France
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21
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Kaklauskas A, Abraham A, Ubarte I, Kliukas R, Luksaite V, Binkyte-Veliene A, Vetloviene I, Kaklauskiene L. A Review of AI Cloud and Edge Sensors, Methods, and Applications for the Recognition of Emotional, Affective and Physiological States. SENSORS (BASEL, SWITZERLAND) 2022; 22:7824. [PMID: 36298176 PMCID: PMC9611164 DOI: 10.3390/s22207824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 09/28/2022] [Accepted: 10/12/2022] [Indexed: 06/16/2023]
Abstract
Affective, emotional, and physiological states (AFFECT) detection and recognition by capturing human signals is a fast-growing area, which has been applied across numerous domains. The research aim is to review publications on how techniques that use brain and biometric sensors can be used for AFFECT recognition, consolidate the findings, provide a rationale for the current methods, compare the effectiveness of existing methods, and quantify how likely they are to address the issues/challenges in the field. In efforts to achieve the key goals of Society 5.0, Industry 5.0, and human-centered design better, the recognition of emotional, affective, and physiological states is progressively becoming an important matter and offers tremendous growth of knowledge and progress in these and other related fields. In this research, a review of AFFECT recognition brain and biometric sensors, methods, and applications was performed, based on Plutchik's wheel of emotions. Due to the immense variety of existing sensors and sensing systems, this study aimed to provide an analysis of the available sensors that can be used to define human AFFECT, and to classify them based on the type of sensing area and their efficiency in real implementations. Based on statistical and multiple criteria analysis across 169 nations, our outcomes introduce a connection between a nation's success, its number of Web of Science articles published, and its frequency of citation on AFFECT recognition. The principal conclusions present how this research contributes to the big picture in the field under analysis and explore forthcoming study trends.
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Affiliation(s)
- Arturas Kaklauskas
- Department of Construction Management and Real Estate, Vilnius Gediminas Technical University, Sauletekio Ave. 11, LT-10223 Vilnius, Lithuania
| | - Ajith Abraham
- Machine Intelligence Research Labs, Scientific Network for Innovation and Research Excellence, Auburn, WA 98071, USA
| | - Ieva Ubarte
- Institute of Sustainable Construction, Vilnius Gediminas Technical University, Sauletekio Ave. 11, LT-10223 Vilnius, Lithuania
| | - Romualdas Kliukas
- Department of Applied Mechanics, Vilnius Gediminas Technical University, Sauletekio Ave. 11, LT-10223 Vilnius, Lithuania
| | - Vaida Luksaite
- Department of Construction Management and Real Estate, Vilnius Gediminas Technical University, Sauletekio Ave. 11, LT-10223 Vilnius, Lithuania
| | - Arune Binkyte-Veliene
- Institute of Sustainable Construction, Vilnius Gediminas Technical University, Sauletekio Ave. 11, LT-10223 Vilnius, Lithuania
| | - Ingrida Vetloviene
- Department of Construction Management and Real Estate, Vilnius Gediminas Technical University, Sauletekio Ave. 11, LT-10223 Vilnius, Lithuania
| | - Loreta Kaklauskiene
- Department of Construction Management and Real Estate, Vilnius Gediminas Technical University, Sauletekio Ave. 11, LT-10223 Vilnius, Lithuania
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22
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Armañac-Julián P, Kontaxis S, Rapalis A, Marozas V, Laguna P, Bailón R, Gil E, Lázaro J. Reliability of pulse photoplethysmography sensors: Coverage using different setups and body locations. FRONTIERS IN ELECTRONICS 2022. [DOI: 10.3389/felec.2022.906324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Pulse photoplethysmography (PPG) is a simple and economical technique for obtaining cardiovascular information. In fact, PPG has become a very popular technology among wearable devices. However, the PPG signal is well-known to be very vulnerable to artifacts, and a good quality signal cannot be expected for most of the time in daily life. The percentage of time that a given measurement can be estimated (e.g., pulse rate) is denoted coverage (C), and it is highly dependent on the subject activity and on the configuration of the sensor, location, and stability of contact. This work aims to quantify the coverage of PPG sensors, using the simultaneously recorded electrocardiogram as a reference, with the PPG recorded at different places in the body and under different stress conditions. While many previous works analyzed the feasibility of PPG as a surrogate for heart rate variability analysis, there exists no previous work studying coverage to derive other cardiovascular indices. We report the coverage not only for estimating pulse rate (PR) but also for estimating pulse arrival time (PAT) and pulse amplitude variability (PAV). Three different datasets are analyzed for this purpose, consisting of a tilt-table test, an acute emotional stress test, and a heat stress test. The datasets include 19, 120, and 51 subjects, respectively, with PPG at the finger and at the forehead for the first two datasets and at the earlobe, in addition, for the latter. C ranges from 70% to 90% for estimating PR. Regarding the estimation of PAT, C ranges from 50% to 90%, and this is very dependent on the PPG sensor location, PPG quality, and the fiducial point (FP) chosen for the delineation of PPG. In fact, the delineation of the FP is critical in time for estimating derived series such as PAT due to the small dynamic range of these series. For the estimation of PAV, the C rates are between 70% and 90%. In general, lower C rates have been obtained for the PPG at the forehead. No difference in C has been observed between using PPG at the finger or at the earlobe. Then, the benefits of using either will depend on the application. However, different C rates are obtained using the same PPG signal, depending on the FP chosen for delineation. Lower C is reported when using the apex point of the PPG instead of the maximum flow velocity or the basal point, with a difference from 1% to even 10%. For further studies, each setup should first be analyzed and validated, taking the results and guidelines presented in this work into account, to study the feasibility of its recording devices with respect to each specific application.
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23
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Effects of Missing Data on Heart Rate Variability Metrics. SENSORS 2022; 22:s22155774. [PMID: 35957328 PMCID: PMC9371086 DOI: 10.3390/s22155774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 07/29/2022] [Accepted: 07/30/2022] [Indexed: 02/05/2023]
Abstract
Heart rate variability (HRV) has been studied for decades in clinical environments. Currently, the exponential growth of wearable devices in health monitoring is leading to new challenges that need to be solved. These devices have relatively poor signal quality and are affected by numerous motion artifacts, with data loss being the main stumbling block for their use in HRV analysis. In the present paper, it is shown how data loss affects HRV metrics in the time domain and frequency domain and Poincaré plots. A gap-filling method is proposed and compared to other existing approaches to alleviate these effects, both with simulated (16 subjects) and real (20 subjects) missing data. Two different data loss scenarios have been simulated: (i) scattered missing beats, related to a low signal to noise ratio; and (ii) bursts of missing beats, with the most common due to motion artifacts. In addition, a real database of photoplethysmography-derived pulse detection series provided by Apple Watch during a protocol including relax and stress stages is analyzed. The best correction method and maximum acceptable missing beats are given. Results suggest that correction without gap filling is the best option for the standard deviation of the normal-to-normal intervals (SDNN), root mean square of successive differences (RMSSD) and Poincaré plot metrics in datasets with bursts of missing beats predominance (p<0.05), whereas they benefit from gap-filling approaches in the case of scattered missing beats (p<0.05). Gap-filling approaches are also the best for frequency-domain metrics (p<0.05). The findings of this work are useful for the design of robust HRV applications depending on missing data tolerance and the desired HRV metrics.
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24
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Sarkar S, Bhattacherjee S, Bhattacharyya P, Mitra M, Pal S. Automatic identification of asthma from ECG derived respiration using complete ensemble empirical mode decomposition with adaptive noise and principal component analysis. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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25
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Masci F, Spatari G, Bortolotti S, Giorgianni CM, Antonangeli LM, Rosecrance J, Colosio C. Assessing the Impact of Work Activities on the Physiological Load in a Sample of Loggers in Sicily (Italy). INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19137695. [PMID: 35805360 PMCID: PMC9265621 DOI: 10.3390/ijerph19137695] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 06/15/2022] [Accepted: 06/18/2022] [Indexed: 11/16/2022]
Abstract
Occupational logging activities expose workers to a wide range of risk factors, such as lifting heavy loads, prolonged, awkward positioning of the lower back, repetitive movements, and insufficient work pauses. Body posture has an important impact on the level of physiological load. The present study involved a group of 40 loggers in the province of Enna (Sicily, Italy) with the aim of defining the impact of logging activities on the workers’ physiological strain during the three primary work tasks of felling, delimbing, and bucking. The Zephyr Bioharness measurement system was used to record trunk posture and heart rate data during work tasks. The NASA TLX questionnaire was used to explore workers’ effort perception of the work tasks. Based on our results, the most demanding work task was tree felling, which requires a higher level of cardiac cost and longer periods spent in awkward trunk postures. The perceived physiological workload was consistently underestimated, especially by the more experienced loggers. Lastly, as the weight of the chainsaw increased, the cardiac load increased.
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Affiliation(s)
- Federica Masci
- Department of Health Sciences, International Centre for Rural Health of the Santi Paolo e Carlo ASST of Milan, University of Milan, 20142 Milano, Italy;
- Department of Environmental and Radiological Health Sciences, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, CO 80523, USA;
- Correspondence:
| | - Giovanna Spatari
- Department of Biomedical, Dental and Morphological and Functional Imaging, University of Messina, 98122 Messina, Italy; (G.S.); (C.M.G.)
| | | | - Concetto Mario Giorgianni
- Department of Biomedical, Dental and Morphological and Functional Imaging, University of Messina, 98122 Messina, Italy; (G.S.); (C.M.G.)
| | | | - John Rosecrance
- Department of Environmental and Radiological Health Sciences, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, CO 80523, USA;
| | - Claudio Colosio
- Department of Health Sciences, International Centre for Rural Health of the Santi Paolo e Carlo ASST of Milan, University of Milan, 20142 Milano, Italy;
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Seipäjärvi S, Tuomola A, Juurakko J, Rottensteiner M, Rissanen APE, Kurkela J, Kujala U, Laukkanen J, Wikgren J. Measuring psychosocial stress with heart rate variability-based methods in different health and age groups. Physiol Meas 2022; 43. [PMID: 35483348 DOI: 10.1088/1361-6579/ac6b7c] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 04/28/2022] [Indexed: 11/12/2022]
Abstract
Autonomic nervous system function and thereby bodily stress and recovery reactions may be assessed by wearable devices measuring heart rate (HR) and its variability (HRV). So far, the validity of HRV-based stress assessments has been mainly studied in healthy populations. In this study, we determined how psychosocial stress affects physiological and psychological stress responses in both young (18-30 yrs) and middle-aged (45-64 yrs) healthy individuals as well as in patients with arterial hypertension and/or either prior evidence of prediabetes or type 2 diabetes. We also studied how an HRV-based stress index (Relax-Stress Intensity, RSI) relates to perceived stress (PS) and cortisol (CRT) responses during psychosocial stress. APPROACH A total of 197 participants were divided into three groups: 1) healthy young (HY, N=63), 2) healthy middle-aged (HM, N=61) and 3) patients with cardiometabolic risk factors (Pts, N=73, 32-65 yrs). The participants underwent a group version of Trier Social Stress Test (TSST-G). HR, HRV (quantified as root mean square of successive differences of R-R intervals, RMSSD), RSI, PS, and salivary CRT were measured regularly during TSST-G and a subsequent recovery period. MAIN RESULTS All groups showed significant stress reactions during TSST-G as indicated by significant responses of HR, RMSSD, RSI, PS, and salivary CRT. Between-group differences were also observed in all measures. Correlation and regression analyses implied RSI being the strongest predictor of CRT response, while HR was more closely associated with PS. SIGNIFICANCE The HRV-based stress index mirrors responses of CRT, which is an independent marker for physiological stress, around TSST-G. Thus, the HRV-based stress index may be used to quantify physiological responses to psychosocial stress across various health and age groups.
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Affiliation(s)
- Santtu Seipäjärvi
- Department of Psychology, University of Jyväskylä, Kärki, Mattilanniemi, Jyvaskyla, 40100, FINLAND
| | - Anniina Tuomola
- Department of Psychology, University of Jyväskylä, Kärki, Mattilanniemi, Jyvaskyla, Keski-Suomi, 40100, FINLAND
| | - Joona Juurakko
- Department of Psychology, University of Jyväskylä, Kärki, Mattilanniemi, Jyvaskyla, Keski-Suomi, 40100, FINLAND
| | - Mirva Rottensteiner
- Central Finland Health Care District, Keskussairaalantie 19, Jyvaskyla, Central Finland, 40620, FINLAND
| | - Antti-Pekka E Rissanen
- Central Finland Health Care District, Keskussairaalantie, Jyvaskyla, Central Finland, 40620, FINLAND
| | - Jari Kurkela
- Department of Psychology, University of Jyväskylä, Kärki, Mattilanniemi, Jyvaskyla, Keski-Suomi, 40100, FINLAND
| | - Urho Kujala
- University of Jyväskylä Faculty of Sports and Health Sciences, Rautpohjankatu 8, Jyvaskyla, Keski-Suomi, 40700, FINLAND
| | - Jari Laukkanen
- Central Finland Health Care District, Keskussairaalantie, Jyvaskyla, Central Finland, 40620, FINLAND
| | - Jan Wikgren
- Department of Psychology, University of Jyväskylä, Kärki, Mattilanniemi, Jyvaskyla, Keski-Suomi, 40100, FINLAND
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Information Retrieval from Photoplethysmographic Sensors: A Comprehensive Comparison of Practical Interpolation and Breath-Extraction Techniques at Different Sampling Rates. SENSORS 2022; 22:s22041428. [PMID: 35214329 PMCID: PMC8877143 DOI: 10.3390/s22041428] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 02/07/2022] [Accepted: 02/08/2022] [Indexed: 11/17/2022]
Abstract
The increasingly widespread diffusion of wearable devices makes possible the continuous monitoring of vital signs, such as heart rate (HR), heart rate variability (HRV), and breath signal. However, these devices usually do not record the “gold-standard” signals, namely the electrocardiography (ECG) and respiratory activity, but a single photoplethysmographic (PPG) signal, which can be exploited to estimate HR and respiratory activity. In addition, these devices employ low sampling rates to limit power consumption. Hence, proper methods should be adopted to compensate for the resulting increased discretization error, while diverse breath-extraction algorithms may be differently sensitive to PPG sampling rate. Here, we assessed the efficacy of parabola interpolation, cubic-spline, and linear regression methods to improve the accuracy of the inter-beat intervals (IBIs) extracted from PPG sampled at decreasing rates from 64 to 8 Hz. PPG-derived IBIs and HRV indices were compared with those extracted from a standard ECG. In addition, breath signals extracted from PPG using three different techniques were compared with the gold-standard signal from a thoracic belt. Signals were recorded from eight healthy volunteers during an experimental protocol comprising sitting and standing postures and a controlled respiration task. Parabola and cubic-spline interpolation significantly increased IBIs accuracy at 32, 16, and 8 Hz sampling rates. Concerning breath signal extraction, the method holding higher accuracy was based on PPG bandpass filtering. Our results support the efficacy of parabola and spline interpolations to improve the accuracy of the IBIs obtained from low-sampling rate PPG signals, and also indicate a robust method for breath signal extraction.
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Schitter AM, Frei P, Elfering A, Kurpiers N, Radlinger L. Evaluation of short-term effects of three passive aquatic interventions on chronic non-specific low back pain: Study protocol for a randomized cross-over clinical trial. Contemp Clin Trials Commun 2022; 26:100904. [PMID: 35243125 PMCID: PMC8886016 DOI: 10.1016/j.conctc.2022.100904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Revised: 02/07/2022] [Accepted: 02/10/2022] [Indexed: 11/17/2022] Open
Abstract
Background Low back pain (LBP) is among the most common physical ailments and its chronic manifestation is a leading cause for disability worldwide. LBP is not attributable to a known diagnosis in 85% of the cases and therefore called chronic non-specific LBP (cnLBP). Passive immersion in warm water is commonly claimed to reduce muscular tension and pain, but not yet sufficiently investigated with regard to cnLBP. The current study compares three passive aquatic interventions regarding their effects on cnLBP: floating (resting in a supine immersed position on flotation devices), WATSU (a passive hands-on treatment, in which a practitioner stands in warm water, gently moving and massaging the client), and a Spa session. Methods In this randomized cross-over clinical trial, all 24 adult participants with cnLBP will undergo the three interventions in balanced order with a washout-period of at least two weeks in between. Assessments will take place at baseline and follow-up of study and immediately before and after each intervention. Assessments cover the primary outcome self-reported current pain (Visual Analog Scale, range: 0–100 mm), other self-report questionnaires (addressing, e.g., personality traits or -states), and physiological parameters (e.g., measurement of spinal range of motion). Discussion The study adds estimates of intervention-specific effect-sizes of widespread passive aquatic interventions to cnLBP. The study also points to potential underlying pain-reducing mechanisms. Trial registration The protocol was approved by the Ethics Committee of the Canton Bern (ProjectID: 2018–00461). Trial registration is intended at ClinicalTrials.gov.
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Hernando A, Posada-Quintero H, Peláez-Coca MD, Gil E, Chon KH. Autonomic Nervous System characterization in hyperbaric environments considering respiratory component and non-linear analysis of Heart Rate Variability. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 214:106527. [PMID: 34879328 DOI: 10.1016/j.cmpb.2021.106527] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 11/05/2021] [Indexed: 06/13/2023]
Abstract
OBJECTIVES an evaluation of Principal Dynamic Mode (PDM) and Orthogonal Subspace Projection (OSP) methods to characterize the Autonomic Nervous System (ANS) response in three different hyperbaric environments was performed. METHODS ECG signals were recorded in two different stages (baseline and immersion) in three different hyperbaric environments: (a) inside a hyperbaric chamber, (b) in a controlled sea immersion, (c) in a real reservoir immersion. Time-domain parameters were extracted from the RR series of the ECG. From the Heart Rate Variability signal (HRV), classic Power Spectral Density (PSD), PDM (a non-linear analysis of HRV which is able to separate sympathetic and parasympathetic activities) and OSP (an analysis of HRV which is able to extract the respiratory component) methods were used to assess the ANS response. RESULTS PDM and OSP parameters follows the same trend when compared to the PSD ones for the hyperbaric chamber dataset. Comparing the three hyperbaric scenarios, significant differences were found: i) heart rate decreased and RMSSD increased in the hyperbaric chamber and the controlled dive, but they had the opposite behavior during the uncontrolled dive; ii) power in the OSP respiratory component was lower than power in the OSP residual component in cases a and c; iii) PDM and OSP methods showed a significant increase in sympathetic activity during both dives, but parasympathetic activity increased only during the uncontrolled dive. CONCLUSIONS PDM and OSP methods could be used as an alternative measurement of ANS response instead of the PSD method. OSP results indicate that most of the variation in the heart rate variability cannot be described by changes in the respiration, so changes in ANS response can be assigned to other factors. Time-domain parameters reflect vagal activation in the hyperbaric chamber and in the controlled dive because of the effect of pressure. In the uncontrolled dive, sympathetic activity seems to be dominant, due to the effects of other factors such as physical activity, the challenging environment, and the influence of breathing through the scuba mask during immersion. In sum, a careful description of the changes in all the possible factors that could affect the ANS response between baseline and immersion stages in hyperbaric environments is needed for better interpretation of the results.
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Affiliation(s)
- Alberto Hernando
- Centro Universitario de Defensa (CUD), Academia General Militar (AGM), Zaragoza, Spain; BSICoS Group, Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain.
| | | | - María Dolores Peláez-Coca
- Centro Universitario de Defensa (CUD), Academia General Militar (AGM), Zaragoza, Spain; BSICoS Group, Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain
| | - Eduardo Gil
- Centro de Investigación Biomédica en Red Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain; BSICoS Group, Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain
| | - Ki H Chon
- Department of Biomedical Engineering, University of Connecticut, Storrs CT, USA
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Iranfar A, Arza A, Atienza D. ReLearn: A Robust Machine Learning Framework in Presence of Missing Data for Multimodal Stress Detection from Physiological Signals . ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:535-541. [PMID: 34891350 DOI: 10.1109/embc46164.2021.9630040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Continuous and multimodal stress detection has been performed recently through wearable devices and machine learning algorithms. However, a well-known and important challenge of working on physiological signals recorded by conventional monitoring devices is missing data due to sensors insufficient contact and interference by other equipment. This challenge becomes more problematic when the user/patient is mentally or physically active or stressed because of more frequent conscious or subconscious movements. In this paper, we propose ReLearn, a robust machine learning framework for stress detection from biomarkers extracted from multimodal physiological signals. ReLearn effectively copes with missing data and outliers both at training and inference phases. ReLearn, composed of machine learning models for feature selection, outlier detection, data imputation, and classification, allows us to classify all samples, including those with missing values at inference. In particular, according to our experiments and stress database, while by discarding all missing data, as a simplistic yet common approach, no prediction can be made for 34% of the data at inference, our approach can achieve accurate predictions, as high as 78%, for missing samples. Also, our experiments show that the proposed framework obtains a cross-validation accuracy of 86.8% even if more than 50% of samples within the features are missing.
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Applicability of Physiological Monitoring Systems within Occupational Groups: A Systematic Review. SENSORS 2021; 21:s21217249. [PMID: 34770556 PMCID: PMC8587311 DOI: 10.3390/s21217249] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 10/24/2021] [Accepted: 10/25/2021] [Indexed: 12/17/2022]
Abstract
The emergence of physiological monitoring technologies has produced exceptional opportunities for real-time collection and analysis of workers' physiological information. To benefit from these safety and health prognostic opportunities, research efforts have explored the applicability of these devices to control workers' wellbeing levels during occupational activities. A systematic review is proposed to summarise up-to-date progress in applying physiological monitoring systems for occupational groups. Adhering with the PRISMA Statement, five databases were searched from 2014 to 2021, and 12 keywords were combined, concluding with the selection of 38 articles. Sources of risk of bias were assessed regarding randomisation procedures, selective outcome reporting and generalisability of results. Assessment procedures involving non-invasive methods applied with health and safety-related goals were filtered. Working-age participants from homogeneous occupational groups were selected, with these groups primarily including firefighters and construction workers. Research objectives were mainly directed to assess heat stress and physiological workload demands. Heart rate related variables, thermal responses and motion tracking through accelerometry were the most common approaches. Overall, wearable sensors proved to be valid tools for assessing physiological status in working environments. Future research should focus on conducting sensor fusion assessments, engaging wearables in real-time evaluation methods and giving continuous feedback to workers and practitioners.
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Wu S, Chen M, Wang J, Shi B, Zhou Y. Association of Short-Term Heart Rate Variability With Breast Tumor Stage. Front Physiol 2021; 12:678428. [PMID: 34566672 PMCID: PMC8461241 DOI: 10.3389/fphys.2021.678428] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 08/18/2021] [Indexed: 11/13/2022] Open
Abstract
Cardiac autonomic modulation, assessed by heart rate variability (HRV), is associated with tumor pathogenesis and development as well as invasion and metastasis. This study aimed to examine this association in breast cancer (BC) patients. A total of 133 patients (average age 49.2years) with BC or benign breast tumors were divided into three groups: benign group, early-stage group, and advanced-stage group. About 5-min resting ECG was collected for the analysis of linear and nonlinear HRV parameters. Multiple logistic regression models were performed to test the independent contribution of HRV to breast tumor stage. The advanced-stage group had significantly reduced HRV compared to the benign and early-stage groups. In particular, for each 1-SD increase in SD2, SD of normal-to-normal intervals, very-low frequency, total power, and low frequency, the odds of having advanced staging decreased by 69.3, 64.3, 58.3, 53.3, and 65.9%, respectively. These associations were independent of age, body mass index, mean heart rate (HR), and respiratory rate (RR). These findings suggest an association between HRV and breast tumor stage, and HRV parameters may help construct an effective early diagnostic and clinical prognostic model.
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Affiliation(s)
- Shuang Wu
- Department of Radiation Oncology, First Affiliated Hospital, Bengbu Medical College, Bengbu, China
| | - Man Chen
- Department of Radiation Oncology, First Affiliated Hospital, Bengbu Medical College, Bengbu, China
| | - Jingfeng Wang
- School of Medical Imaging, Bengbu Medical College, Bengbu, China.,Anhui Key Laboratory of Computational Medicine and Intelligent Health, Bengbu Medical College, Bengbu, China
| | - Bo Shi
- School of Medical Imaging, Bengbu Medical College, Bengbu, China.,Anhui Key Laboratory of Computational Medicine and Intelligent Health, Bengbu Medical College, Bengbu, China
| | - Yufu Zhou
- Department of Radiation Oncology, First Affiliated Hospital, Bengbu Medical College, Bengbu, China
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Slade K, Kramer SE, Fairclough S, Richter M. Effortful listening: Sympathetic activity varies as a function of listening demand but parasympathetic activity does not. Hear Res 2021; 410:108348. [PMID: 34543837 DOI: 10.1016/j.heares.2021.108348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 08/16/2021] [Accepted: 08/30/2021] [Indexed: 11/27/2022]
Abstract
Research on listening effort has used various physiological measures to examine the biological correlates of listening effort but a systematic examination of the impact of listening demand on cardiac autonomic nervous system activity is still lacking. The presented study aimed to close this gap by assessing cardiac sympathetic and parasympathetic responses to variations in listening demand. For this purpose, 45 participants performed four speech-in-noise tasks differing in listening demand-manipulated as signal-to-noise ratio varying between +23 dB and -16 dB-while their pre-ejection period and respiratory sinus arrythmia responses were assessed. Cardiac responses showed the expected effect of listening demand on sympathetic activity, but failed to provide evidence for the expected listening demand impact on parasympathetic activity: Pre-ejection period reactivity increased with increasing listening demand across the three possible listening conditions and was low in the very high (impossible) demand condition, whereas respiratory sinus arrythmia did not show this pattern. These findings have two main implications. First, cardiac sympathetic responses seem to be the more sensitive correlate of the impact of task demand on listening effort compared to cardiac parasympathetic responses. Second, very high listening demand may lead to disengagement and correspondingly low effort and reduced cardiac sympathetic response.
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Affiliation(s)
- Kate Slade
- Department of Psychology, Lancaster University, LA1 4YF Lancaster, United Kingdom.
| | - Sophia E Kramer
- Amsterdam UMC, Vrije Universiteit Amsterdam, Otolaryngology-Head and Neck Surgery, Ear and Hearing, Amsterdam Public Health Research Institute, De Boelelaan, Amsterdam, the Netherlands
| | - Stephen Fairclough
- School of Psychology, Liverpool John Moores University, Byrom Street, L3 3AF, Liverpool, United Kingdom
| | - Michael Richter
- School of Psychology, Liverpool John Moores University, Byrom Street, L3 3AF, Liverpool, United Kingdom.
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Momeni N, Valdes AA, Rodrigues J, Sandi C, Atienza D. CAFS: Cost-Aware Features Selection Method for Multimodal Stress Monitoring on Wearable Devices. IEEE Trans Biomed Eng 2021; 69:1072-1084. [PMID: 34543185 DOI: 10.1109/tbme.2021.3113593] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE Today, stress monitoring on wearable devices is challenged by the tension between high-detection accuracy and battery lifetime driven by multimodal data acquisition and processing. Limited research has addressed the classification cost on multimodal wearable sensors, particularly when the features are cost-dependent. Thus, we design a Cost-Aware Feature Selection (CAFS) methodology that trades-off between prediction-power and energy-cost for multimodal stress monitoring. METHODS CAFS selects the most important features under different energy-constraints, which allows us to obtain energy-scalable stress monitoring models. We further propose a self-aware stress monitoring method that intelligently switches among the energy-scalable models, reducing energy consumption. RESULTS Using CAFS methodology on experimental data and simulation, we reduce the energy-cost of the stress model designed without energy constrains up to 94.37%. We obtain 90.98% and 95.74% as the best accuracy and confidence values, respectively, on unseen data, outperforming state-of-the-art studies. Analyzing our interpretable and energy-scalable models, we showed that simple models that use only heart rate (HR) or skin conductance level (SCL), confidently predict stress for HR >93.30 BPM and non-stress for SCL <6.42S, but, outside these values, a multimodal model using respiration and pulse waves features is needed for confident stress classification. Our self-aware stress monitoring proposal saves10x energy and provides 88.72% of ac-curacy on unseen data. CONCLUSION We propose a comprehensive solution for the design of cost-aware stress monitoring addressing the problem of selecting an optimal feature subset considering their cost-dependency and cost-constrains. Significant: Our design framework enables long-term, confident, and accurate stress monitoring on wearable devices.
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Dai R, Lu C, Yun L, Lenze E, Avidan M, Kannampallil T. Comparing stress prediction models using smartwatch physiological signals and participant self-reports. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 208:106207. [PMID: 34161847 DOI: 10.1016/j.cmpb.2021.106207] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 05/24/2021] [Indexed: 06/13/2023]
Abstract
Recent advances in wearable technology have facilitated the non-obtrusive monitoring of physiological signals, creating opportunities to monitor and predict stress. Researchers have utilized machine learning methods using these physiological signals to develop stress prediction models. Many of these prediction models have utilized objective stressor tasks (e.g., a public speaking task or solving math problems). Alternatively, the subjective user responses with self-reports have also been used for measuring stress. In this paper, we describe a methodological approach (a) to compare the prediction performance of models developed using objective markers of stress using participant-reported subjective markers of stress from self-reports; and (b) to develop personalized stress models by accounting for inter-individual differences. Towards this end, we conducted a laboratory-based study with 32 healthy volunteers. Participants completed a series of stressor tasks-social, cognitive and physical-wearing an instrumented commercial smartwatch that collected physiological signals and participant responses using timed self-reports. After extensive data preprocessing using a combination of signal processing techniques, we developed two types of models: objective stress models using the stressor tasks as labels; and subjective stress models using participant responses to each task as the label for that stress task. We trained and tested several machine learning algorithms-support vector machine (SVM), random forest (RF), gradient boosted trees (GBT), AdaBoost, and Logistic Regression (LR)-and evaluated their performance. SVM had the best performance for the models using the objective stressor (i.e., stressor tasks) with an AUROC of 0.790 and an F-1 score of 0.623. SVM also had the highest performance for the models using the subjective stress (i.e., participant self-reports) with an AUROC of 0.719 and an F-1 score of 0.520. Model performance improved with a personalized threshold model to an AUROC of 0.751 and an F-1 score of 0.599. The performance of the stress models using an instrumented commercial smartwatch was comparable to similar models from other state-of-the-art laboratory-based studies. However, the subjective stress models had a lower performance, indicating the need for further research on the use of self-reports for stress-related studies. The improvement in performance with the personalized threshold-based models provide new directions for building stress prediction models.
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Affiliation(s)
- Ruixuan Dai
- Department of Computer Science, McKelvey School of Engineering, USA
| | - Chenyang Lu
- Department of Computer Science, McKelvey School of Engineering, USA
| | | | | | | | - Thomas Kannampallil
- Department of Anesthesiology, USA; Institute for Informatics, School of Medicine, Washington University in St. Louis, St Louis, MO, USA.
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Reali P, Piazza C, Tacchino G, Songia L, Nazzari S, Reni G, Frigerio A, Bianchi AM. Assessing stress variations in children during the strange situation procedure: comparison of three widely used respiratory sinus arrhythmia estimation methods. Physiol Meas 2021; 42. [PMID: 34325412 DOI: 10.1088/1361-6579/ac18ff] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 07/29/2021] [Indexed: 01/01/2023]
Abstract
Objective.The respiratory sinus arrhythmia (RSA) is a well-known marker of vagal activity that can be exploited to measure stress changes. RSA is usually estimated from heart rate variability (HRV). This study aims to compare the RSA obtained with three widely adopted methods showing their strengths and potential pitfalls.Approach.The three methods are tested on 69 healthy preschoolers undergoing a stressful protocol, the strange situation procedure (SSP). We compare the RSA estimated by the Porges method, the univariate autoregressive (AR) spectral analysis of the HRV signal, and the bivariate AR spectral analysis of HRV and respirogram signals. We examine RSA differences detected across the SSP episodes and correlation between the estimates provided by each method.Main results.The Porges and the bivariate AR approaches both detected significant differences (i.e. stress variations) in the RSA measured across the SSP. However, the latter method showed higher sensitivity to stress changes induced by the procedure, with the mean RSA variation between baseline and first separation from the mother (the most stressful condition) being significantly different among methods: Porges, -17.5%; univariate AR, -18.3%; bivariate AR, -23.7%. Moreover, the performances of the Porges algorithm were found strictly dependent on the applied preprocessing.Significance.Our findings confirm the bivariate AR analysis of the HRV and respiratory signals as a robust stress assessment tool that does not require any population-specific preprocessing of the signals and warn about using RSA estimates that neglect breath information in more natural experiments, such as those involving children, in which respiratory frequency changes are extremely likely.
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Affiliation(s)
- Pierluigi Reali
- Electronics Information and Bioengineering Department, Politecnico di Milano, Milano, Italy
| | - Caterina Piazza
- Bioengineering Laboratory, Scientific Institute IRCCS E. Medea, Bosisio Parini, LC, Italy
| | - Giulia Tacchino
- Electronics Information and Bioengineering Department, Politecnico di Milano, Milano, Italy
| | - Letizia Songia
- Electronics Information and Bioengineering Department, Politecnico di Milano, Milano, Italy
| | - Sarah Nazzari
- Child Psychopathology Unit, Scientific Institute IRCCS E. Medea, Bosisio Parini, LC, Italy
| | - Gianluigi Reni
- Bioengineering Laboratory, Scientific Institute IRCCS E. Medea, Bosisio Parini, LC, Italy
| | - Alessandra Frigerio
- Child Psychopathology Unit, Scientific Institute IRCCS E. Medea, Bosisio Parini, LC, Italy
| | - Anna Maria Bianchi
- Electronics Information and Bioengineering Department, Politecnico di Milano, Milano, Italy
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Armañac-Julián P, Hernando D, Lázaro J, de Haro C, Magrans R, Morales J, Moeyersons J, Sarlabous L, López-Aguilar J, Subirà C, Fernández R, Orini M, Laguna P, Varon C, Gil E, Bailón R, Blanch L. Cardiopulmonary coupling indices to assess weaning readiness from mechanical ventilation. Sci Rep 2021; 11:16014. [PMID: 34362950 PMCID: PMC8346488 DOI: 10.1038/s41598-021-95282-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 07/20/2021] [Indexed: 02/07/2023] Open
Abstract
The ideal moment to withdraw respiratory supply of patients under Mechanical Ventilation at Intensive Care Units (ICU), is not easy to be determined for clinicians. Although the Spontaneous Breathing Trial (SBT) provides a measure of the patients' readiness, there is still around 15-20% of predictive failure rate. This work is a proof of concept focused on adding new value to the prediction of the weaning outcome. Heart Rate Variability (HRV) and Cardiopulmonary Coupling (CPC) methods are evaluated as new complementary estimates to assess weaning readiness. The CPC is related to how the mechanisms regulating respiration and cardiac pumping are working simultaneously, and it is defined from HRV in combination with respiratory information. Three different techniques are used to estimate the CPC, including Time-Frequency Coherence, Dynamic Mutual Information and Orthogonal Subspace Projections. The cohort study includes 22 patients in pressure support ventilation, ready to undergo the SBT, analysed in the 24 h previous to the SBT. Of these, 13 had a successful weaning and 9 failed the SBT or needed reintubation -being both considered as failed weaning. Results illustrate that traditional variables such as heart rate, respiratory frequency, and the parameters derived from HRV do not differ in patients with successful or failed weaning. Results revealed that HRV parameters can vary considerably depending on the time at which they are measured. This fact could be attributed to circadian rhythms, having a strong influence on HRV values. On the contrary, significant statistical differences are found in the proposed CPC parameters when comparing the values of the two groups, and throughout the whole recordings. In addition, differences are greater at night, probably because patients with failed weaning might be experiencing more respiratory episodes, e.g. apneas during the night, which is directly related to a reduced respiratory sinus arrhythmia. Therefore, results suggest that the traditional measures could be used in combination with the proposed CPC biomarkers to improve weaning readiness.
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Affiliation(s)
- Pablo Armañac-Julián
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) group at the Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain.
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain.
| | - David Hernando
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) group at the Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Jesús Lázaro
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) group at the Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Candelaria de Haro
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació Parc Taulí I3PT, Universitat Autónoma de Barcelona, Sabadell, Spain
- CIBER de Enfermedades Respiratorias (CIBER-ES), Instituto de Salud Carlos III, Madrid, Spain
| | | | - John Morales
- Department of Electrical Engineering-ESAT, STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
| | - Jonathan Moeyersons
- Department of Electrical Engineering-ESAT, STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
| | - Leonardo Sarlabous
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació Parc Taulí I3PT, Universitat Autónoma de Barcelona, Sabadell, Spain
| | - Josefina López-Aguilar
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació Parc Taulí I3PT, Universitat Autónoma de Barcelona, Sabadell, Spain
- CIBER de Enfermedades Respiratorias (CIBER-ES), Instituto de Salud Carlos III, Madrid, Spain
| | - Carles Subirà
- Department of Intensive Care, Fundació Althaia, Universitat Internacional de Catalunya, Manresa, Spain
| | - Rafael Fernández
- CIBER de Enfermedades Respiratorias (CIBER-ES), Instituto de Salud Carlos III, Madrid, Spain
- Department of Intensive Care, Fundació Althaia, Universitat Internacional de Catalunya, Manresa, Spain
| | - Michele Orini
- Institute of Cardiovascular Science, University College London, London, UK
- Barts Heart Centre, St Bartholomews Hospital, University College London, London, UK
| | - Pablo Laguna
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) group at the Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Carolina Varon
- Department of Electrical Engineering-ESAT, STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
- Circuits and Systems (CAS) group, Delft University of Technology, Delft, The Netherlands
| | - Eduardo Gil
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) group at the Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Raquel Bailón
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) group at the Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Lluís Blanch
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació Parc Taulí I3PT, Universitat Autónoma de Barcelona, Sabadell, Spain
- CIBER de Enfermedades Respiratorias (CIBER-ES), Instituto de Salud Carlos III, Madrid, Spain
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Cakmak AS, Alday EAP, Da Poian G, Rad AB, Metzler TJ, Neylan TC, House SL, Beaudoin FL, An X, Stevens JS, Zeng D, Linnstaedt SD, Jovanovic T, Germine LT, Bollen KA, Rauch SL, Lewandowski CA, Hendry PL, Sheikh S, Storrow AB, Musey PI, Haran JP, Jones CW, Punches BE, Swor RA, Gentile NT, McGrath ME, Seamon MJ, Mohiuddin K, Chang AM, Pearson C, Domeier RM, Bruce SE, O'Neil BJ, Rathlev NK, Sanchez LD, Pietrzak RH, Joormann J, Barch DM, Pizzagalli DA, Harte SE, Elliott JM, Kessler RC, Koenen KC, Ressler KJ, Mclean SA, Li Q, Clifford GD. Classification and Prediction of Post-Trauma Outcomes Related to PTSD Using Circadian Rhythm Changes Measured via Wrist-Worn Research Watch in a Large Longitudinal Cohort. IEEE J Biomed Health Inform 2021; 25:2866-2876. [PMID: 33481725 PMCID: PMC8395207 DOI: 10.1109/jbhi.2021.3053909] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Post-Traumatic Stress Disorder (PTSD) is a psychiatric condition resulting from threatening or horrifying events. We hypothesized that circadian rhythm changes, measured by a wrist-worn research watch are predictive of post-trauma outcomes. APPROACH 1618 post-trauma patients were enrolled after admission to emergency departments (ED). Three standardized questionnaires were administered at week eight to measure post-trauma outcomes related to PTSD, sleep disturbance, and pain interference with daily life. Pulse activity and movement data were captured from a research watch for eight weeks. Standard and novel movement and cardiovascular metrics that reflect circadian rhythms were derived using this data. These features were used to train different classifiers to predict the three outcomes derived from week-eight surveys. Clinical surveys administered at ED were also used as features in the baseline models. RESULTS The highest cross-validated performance of research watch-based features was achieved for classifying participants with pain interference by a logistic regression model, with an area under the receiver operating characteristic curve (AUC) of 0.70. The ED survey-based model achieved an AUC of 0.77, and the fusion of research watch and ED survey metrics improved the AUC to 0.79. SIGNIFICANCE This work represents the first attempt to predict and classify post-trauma symptoms from passive wearable data using machine learning approaches that leverage the circadian desynchrony in a potential PTSD population.
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Rozo A, Morales J, Moeyersons J, Joshi R, Caiani EG, Borzée P, Buyse B, Testelmans D, Van Huffel S, Varon C. Benchmarking Transfer Entropy Methods for the Study of Linear and Nonlinear Cardio-Respiratory Interactions. ENTROPY 2021; 23:e23080939. [PMID: 34441079 PMCID: PMC8394114 DOI: 10.3390/e23080939] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 07/13/2021] [Accepted: 07/20/2021] [Indexed: 11/16/2022]
Abstract
Transfer entropy (TE) has been used to identify and quantify interactions between physiological systems. Different methods exist to estimate TE, but there is no consensus about which one performs best in specific applications. In this study, five methods (linear, k-nearest neighbors, fixed-binning with ranking, kernel density estimation and adaptive partitioning) were compared. The comparison was made on three simulation models (linear, nonlinear and linear + nonlinear dynamics). From the simulations, it was found that the best method to quantify the different interactions was adaptive partitioning. This method was then applied on data from a polysomnography study, specifically on the ECG and the respiratory signals (nasal airflow and respiratory effort around the thorax). The hypothesis that the linear and nonlinear components of cardio-respiratory interactions during light and deep sleep change with the sleep stage, was tested. Significant differences, after performing surrogate analysis, indicate an increased TE during deep sleep. However, these differences were found to be dependent on the type of respiratory signal and sampling frequency. These results highlight the importance of selecting the appropriate signals, estimation method and surrogate analysis for the study of linear and nonlinear cardio-respiratory interactions.
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Affiliation(s)
- Andrea Rozo
- STADIUS, Center of Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, 3001 Leuven, Belgium; (J.M.); (J.M.); (S.V.H.); (C.V.)
- Correspondence:
| | - John Morales
- STADIUS, Center of Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, 3001 Leuven, Belgium; (J.M.); (J.M.); (S.V.H.); (C.V.)
| | - Jonathan Moeyersons
- STADIUS, Center of Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, 3001 Leuven, Belgium; (J.M.); (J.M.); (S.V.H.); (C.V.)
| | - Rohan Joshi
- Department of Patient Care and Monitoring, Philips Research, 5656 AE Eindhoven, The Netherlands;
| | - Enrico G. Caiani
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy;
| | - Pascal Borzée
- Department of Pneumology, Leuven University Centre for Sleep and Wake Disorders, UZ Leuven, 3000 Leuven, Belgium; (P.B.); (B.B.); (D.T.)
| | - Bertien Buyse
- Department of Pneumology, Leuven University Centre for Sleep and Wake Disorders, UZ Leuven, 3000 Leuven, Belgium; (P.B.); (B.B.); (D.T.)
| | - Dries Testelmans
- Department of Pneumology, Leuven University Centre for Sleep and Wake Disorders, UZ Leuven, 3000 Leuven, Belgium; (P.B.); (B.B.); (D.T.)
| | - Sabine Van Huffel
- STADIUS, Center of Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, 3001 Leuven, Belgium; (J.M.); (J.M.); (S.V.H.); (C.V.)
| | - Carolina Varon
- STADIUS, Center of Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, 3001 Leuven, Belgium; (J.M.); (J.M.); (S.V.H.); (C.V.)
- Service de Chimie-Physique E.P., Université libre de Bruxelles, B-1050 Brussels, Belgium
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Pamplin JC, Veazey SR, De Howitt J, Cohen K, Barczak S, Espinoza M, Luellen D, Ross K, Serio-Melvin M, McCarthy M, Colombo CJ. Prolonged, High-Fidelity Simulation for Study of Patient Care in Resource-Limited Medical Contexts and for Technology Comparative Effectiveness Testing. Crit Care Explor 2021; 3:e0477. [PMID: 34250500 PMCID: PMC8263321 DOI: 10.1097/cce.0000000000000477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Most high-fidelity medical simulation is of limited duration, used for education and training, and rarely intended to study medical technology. U.S. caregivers working in prehospital, resource-limited settings may need to manage patients for extended periods (hours to days). This "prolonged casualty care" occurs during military, wilderness, humanitarian, disaster, and space medicine. We sought to develop a standardized simulation model that accurately reflects prolonged casualty care in order to study caregiver decision-making and performance, training requirements, and technology use in prolonged casualty care. DESIGN Model development. SETTING High-fidelity simulation laboratory. SUBJECTS None. INTERVENTIONS We interviewed subject matter experts to identify relevant prolonged casualty care medical challenges and selected two casualty types to further develop our model: a large thermal burn model and a severe hypoxia model. We met with a multidisciplinary group of experts in prolonged casualty care, nursing, and critical care to describe how these problems could evolve over time and how to contextualize the problems with a background story and clinical environment with expected resource availability. Following initial scenario drafting, we tested the models with expert clinicians. After multiple tests, we selected the hypoxia model for refinement and testing with inexperienced providers. We tested and refined this model until two research teams could proctor the scenario consistently despite subject performance variability. MEASUREMENTS AND MAIN RESULTS We developed a 6-8-hour simulation model that represented a 14-hour scenario. This model of pneumonia evolved from presentation to severe hypoxia necessitating advanced interventions including airway, breathing, and shock management. The model included: context description, caregiver orientation scripts, hourly progressive physiology tracks corresponding to caregiver interventions, intervention/procedure-specific physiology tracks, intervention checklists, equipment lists, prestudy checklists, photographs of setups, procedure, telementor, and role player scripts, business rules, and data collection methods. CONCLUSIONS This is the first standardized, high-fidelity simulation model of prolonged casualty care described in the literature. It may be used to assess caregiver performance and patient outcomes resulting from that performance during a complex, 14-hour prolonged casualty care scenario. Because it is standardized, the model may be used to compare differences in the impact of new technologies upon caregiver performance and simulated patient outcomes..
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Affiliation(s)
- Jeremy C Pamplin
- Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fredrick, MD
- Department of Medicine, Uniformed Services University, Bethesda, MD
| | - Sena R Veazey
- U.S. Army Institute of Surgical Research, U.S. Army Medical Research and Development Command, San Antonio, TX
| | - Joanne De Howitt
- Department of Virtual Health, Madigan Army Medical Center, Tacoma, WA
- The Geneva Foundation, Tacoma, WA
| | - Katy Cohen
- Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fredrick, MD
- Department of Medicine, Uniformed Services University, Bethesda, MD
- U.S. Army Institute of Surgical Research, U.S. Army Medical Research and Development Command, San Antonio, TX
- Department of Virtual Health, Madigan Army Medical Center, Tacoma, WA
- The Geneva Foundation, Tacoma, WA
- DocBox, Waltham, MA
- Center for Nursing Science and Clinical Inquiry, Madigan Army Medical Center, Tacoma, WA
| | - Stacie Barczak
- Department of Virtual Health, Madigan Army Medical Center, Tacoma, WA
- The Geneva Foundation, Tacoma, WA
| | - Mark Espinoza
- U.S. Army Institute of Surgical Research, U.S. Army Medical Research and Development Command, San Antonio, TX
- The Geneva Foundation, Tacoma, WA
| | - Dave Luellen
- U.S. Army Institute of Surgical Research, U.S. Army Medical Research and Development Command, San Antonio, TX
| | | | - Maria Serio-Melvin
- U.S. Army Institute of Surgical Research, U.S. Army Medical Research and Development Command, San Antonio, TX
| | - Mary McCarthy
- Center for Nursing Science and Clinical Inquiry, Madigan Army Medical Center, Tacoma, WA
| | - Christopher J Colombo
- Department of Medicine, Uniformed Services University, Bethesda, MD
- Department of Virtual Health, Madigan Army Medical Center, Tacoma, WA
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Vavrinsky E, Stopjakova V, Kopani M, Kosnacova H. The Concept of Advanced Multi-Sensor Monitoring of Human Stress. SENSORS (BASEL, SWITZERLAND) 2021; 21:3499. [PMID: 34067895 PMCID: PMC8157129 DOI: 10.3390/s21103499] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 05/10/2021] [Accepted: 05/12/2021] [Indexed: 12/23/2022]
Abstract
Many people live under stressful conditions which has an adverse effect on their health. Human stress, especially long-term one, can lead to a serious illness. Therefore, monitoring of human stress influence can be very useful. We can monitor stress in strictly controlled laboratory conditions, but it is time-consuming and does not capture reactions, on everyday stressors or in natural environment using wearable sensors, but with limited accuracy. Therefore, we began to analyze the current state of promising wearable stress-meters and the latest advances in the record of related physiological variables. Based on these results, we present the concept of an accurate, reliable and easier to use telemedicine device for long-term monitoring of people in a real life. In our concept, we ratify with two synchronized devices, one on the finger and the second on the chest. The results will be obtained from several physiological variables including electrodermal activity, heart rate and respiration, body temperature, blood pressure and others. All these variables will be measured using a coherent multi-sensors device. Our goal is to show possibilities and trends towards the production of new telemedicine equipment and thus, opening the door to a widespread application of human stress-meters.
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Affiliation(s)
- Erik Vavrinsky
- Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, 81219 Bratislava, Slovakia;
- Institute of Medical Physics, Biophysics, Informatics and Telemedicine, Faculty of Medicine, Comenius University, Sasinkova 2, 81272 Bratislava, Slovakia;
| | - Viera Stopjakova
- Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, 81219 Bratislava, Slovakia;
| | - Martin Kopani
- Institute of Medical Physics, Biophysics, Informatics and Telemedicine, Faculty of Medicine, Comenius University, Sasinkova 2, 81272 Bratislava, Slovakia;
| | - Helena Kosnacova
- Department of Simulation and Virtual Medical Education, Faculty of Medicine, Comenius University, Sasinkova 4, 81272 Bratislava, Slovakia
- Department of Molecular Oncology, Cancer Research Institute, Biomedical Research Center of the Slovak Academy of Sciences, Dúbravská Cesta 9, 84505 Bratislava, Slovakia
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Pelaez-Coca MD, Hernando A, Lozano MT, Sanchez C, Izquierdo D, Gil E. Photoplethysmographic Waveform and Pulse Rate Variability Analysis in Hyperbaric Environments. IEEE J Biomed Health Inform 2021; 25:1550-1560. [PMID: 32870804 DOI: 10.1109/jbhi.2020.3020743] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The main aim of this work is to identify alterations in the morphology of the pulse photoplethysmogram (PPG) signal, due to the exposure of the subjects to a hyperbaric environment. Additionally, their Pulse Rate Variability (PRV) is analysed to characterise the response of their Autonomic Nervous System (ANS). To do that, 28 volunteers are introduced into a hyperbaric chamber and five sequential stages with different atmospheric pressures from 1 atm to 5 atm are performed. In this work, nineteen morphological parameters of the PPG signal are analysed: the pulse amplitude; eight parameters related to pulse width; eight parameters related to pulse area; and the two two pulse slopes. Also, classical time and frequency parameters of PRV are computed. Notable widening of the pulses width is observed in the stages analysed. The PPG area increases with pressure, with no significant changes when the initial pressure is recovered. These changes in PPG waveform may be caused by an increase in the systemic vascular resistance as a consequence of of vasoconstriction in the extremities, suggesting a sympathetic activation. However, the PRV results show an augmented parasympathetic activity and a reduction in the parameters that characterise the sympathetic response. So, only a sympathetic activation is detected in the peripheral region, as reflected by PPG morphology. The information regarding the ANS and the cardiovascular response that can be extracted from the PPG signal, as well as its compatibility with wet conditions make this signal the most suitable for studying the physiological response in hyperbaric environments.
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Cardiorespiratory synchronisation and systolic blood pressure correlation of peripheral arterial stiffness during endoscopic thoracic sympathectomy. Sci Rep 2021; 11:5966. [PMID: 33727620 PMCID: PMC7966741 DOI: 10.1038/s41598-021-85299-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 02/23/2021] [Indexed: 11/08/2022] Open
Abstract
Muscle sympathetic nerve activity (MSNA) is known as an effective measure to evaluate peripheral sympathetic activity; however, it requires invasive measurement with the microneurography method. In contrast, peripheral arterial stiffness affected by MSNA is a measure that allows non-invasive evaluation of mechanical changes of arterial elasticity. This paper aims to clarify the features of peripheral arterial stiffness to determine whether it inherits MSNA features towards non-invasive evaluation of its activity. To this end, we propose a method to estimate peripheral arterial stiffness [Formula: see text] at a high sampling rate. Power spectral analysis of the estimated [Formula: see text] was then performed on data acquired from 15 patients ([Formula: see text] years) who underwent endoscopic thoracic sympathectomy. We examined whether [Formula: see text] exhibited the features of MSNA where its frequency components synchronise with heart and respiration rates and correlates with the low-frequency component of systolic blood pressure. Regression analysis revealed that the local peak frequency in the range of heartbeat frequency highly correlate with the heart rate ([Formula: see text], [Formula: see text]) where the regression slope was approximately 1 and intercept was approximately 0. Frequency analysis then found spectral peaks of [Formula: see text] approximately 0.2 Hz that correspond to the respiratory cycle. Finally, cross power spectral analysis showed a significant magnitude squared coherence between [Formula: see text] and systolic blood pressure in the frequency band from 0.04 to 0.2 Hz. These results indicate that [Formula: see text] inherits the features observed in MSNA that require invasive measurements, and thus [Formula: see text] can be an effective non-invasive substitution for MSNA measure.
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Bhoja R, Guttman OT, Fox AA, Melikman E, Kosemund M, Gingrich KJ. Psychophysiological Stress Indicators of Heart Rate Variability and Electrodermal Activity With Application in Healthcare Simulation Research. Simul Healthc 2020; 15:39-45. [PMID: 32028446 DOI: 10.1097/sih.0000000000000402] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
STATEMENT Psychological stress arises from a stressor placed on an individual that leads to both emotional and physiological responses. The latter is referred to as psychophysiological stress. Healthcare simulation provides a platform to investigate stress psychobiology and its effects on learning and performance. However, psychophysiological stress measures may be underused in healthcare simulation research. The inclusion of such measures with subjective measures of stress in healthcare simulation research provides a more complete picture of the stress response, thereby furthering the understanding of stress and its impact on learning and performance. The goals of this article were to review 2 commonly used psychophysiological stress measures involving heart rate variability and electrodermal activity reflecting sweat gland activity and to demonstrate their utility in an example pilot study in healthcare simulation research.
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Affiliation(s)
- Ravi Bhoja
- From the Departments of Anesthesiology and Pain Medicine (R.B., O.T.G., A.A.F., E.M., K.J.G.), and Center for Minimally Invasive Surgery (M.K.), UT Southwestern Medical Center, Dallas, TX
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Zontone P, Affanni A, Bernardini R, Piras A, Rinaldo R, Formaggia F, Minen D, Minen M, Savorgnan C. Car Driver's Sympathetic Reaction Detection Through Electrodermal Activity and Electrocardiogram Measurements. IEEE Trans Biomed Eng 2020; 67:3413-3424. [PMID: 32305889 DOI: 10.1109/tbme.2020.2987168] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE in this paper we propose a system to detect a subject's sympathetic reaction, which is related to unexpected or challenging events during a car drive. METHODS we use the Electrocardiogram (ECG) signal and the Skin Potential Response (SPR) signal, which has several advantages with respect to other Electrodermal (EDA) signals. We record one SPR signal for each hand, and use an algorithm that, selecting the smoother signal, is able to remove motion artifacts. We extract statistical features from the ECG and SPR signals in order to classify signal segments and identify the presence or absence of emotional events via a Supervised Learning Algorithm. The experiments were carried out in a company which specializes in driving simulator equipment, using a motorized platform and a driving simulator. Different subjects were tested with this setup, with different challenging events happening on predetermined locations on the track. RESULTS we obtain an Accuracy as high as 79.10% for signal blocks and as high as 91.27% for events. CONCLUSION results demonstrate the good performance of the presented system in detecting sympathetic reactions, and the effectiveness of the motion artifact removal procedure. SIGNIFICANCE our work demonstrates the possibility to classify the emotional state of the driver, using the ECG and EDA signals and a slightly invasive setup. In particular, the proposed use of SPR and of the motion artifact removal procedure are crucial for the effectiveness of the system.
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Performance Evaluation of an Integrated Fuzzy-Based Driving-Support System for Real-Time Risk Management in VANETs. SENSORS 2020; 20:s20226537. [PMID: 33207609 PMCID: PMC7697935 DOI: 10.3390/s20226537] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 11/12/2020] [Accepted: 11/13/2020] [Indexed: 11/16/2022]
Abstract
The highly competitive and rapidly advancing autonomous vehicle race has been on for several years now, and it has made the driver-assistance systems a shadow of their former self. Nevertheless, automated vehicles have many obstacles on the way, and until we have them on the roads, promising solutions that can be achievable in the near future should be sought-after. Driving-support technologies have proven themselves to be effective in the battle against car crashes, and with Vehicular Ad hoc Networks (VANETs) supporting them, their efficiency is expected to rise steeply. In this work, we propose and implement a driving-support system which, on the one hand, could immensely benefit from major advancement of VANETs, but on the other hand can effectively be implemented as a stand-alone system. The proposed system consists of a non-intrusive integrated fuzzy-based system able to detect a risky situation in real time and alert the driver about the danger. It makes use of the information acquired from various in-car sensors as well as from communications with other vehicles and infrastructure to evaluate the condition of the considered parameters. The parameters include factors that affect the driver’s ability to drive, such as his/her current health condition and the inside environment in which he/she is driving, the vehicle speed, and factors related to the outside environment such as the weather and road condition. We show the effect of these parameters on the determination of the driving risk level through simulations and experiments and explain how these risk levels are translated into actions that can help the driver to manage certain risky situations, thus improving the driving safety.
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Lazaro J, Reljin N, Bailon R, Gil E, Noh Y, Laguna P, Chon KH. Electrocardiogram Derived Respiratory Rate Using a Wearable Armband. IEEE Trans Biomed Eng 2020; 68:1056-1065. [PMID: 32746038 DOI: 10.1109/tbme.2020.3004730] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
A method for deriving respiratory rate from an armband, which records three-channel electrocardiogram (ECG) using three pairs of dry (no hydrogel) electrodes, is presented. The armband device is especially convenient for long-term (months-years) monitoring because it does not use obstructive leads nor hydrogels/adhesives, which cause skin irritation even after few days. An ECG-derived respiration (EDR) based on respiration-related modulation of QRS slopes and R-wave angle approach was used. Moreover, we modified the EDR algorithm to lower the computational cost. Respiratory rates were estimated with the armband-ECG and the reference plethysmography-based respiration signals from 15 subjects who underwent breathing experiment consisting of five stages of controlled breathing (at 0.1, 0.2, 0.3, 0.4, and 0.5 Hz) and one stage of spontaneous breathing. The respiratory rates from the armband obtained a relative error with respect to the reference (respiratory rate estimated from the plethysmography-based respiration signal) that was not higher than 2.26% in median nor interquartile range (IQR) for all stages of fixed and spontaneous breathing, and not higher than 3.57% in median nor IQR in the case when the low computational cost algorithm was applied. These results demonstrate that respiration-related modulation of the ECG morphology are also present in the armband ECG device. Furthermore, these results suggest that respiration-related modulation can be exploited by the EDR method based on QRS slopes and R-wave angles to obtain respiratory rate, which may have a wide range of applications including monitoring patients with chronic respiratory diseases, epileptic seizures detection, stress assessment, and sleep studies, among others.
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Nicolini P, Mari D, Abbate C, Inglese S, Bertagnoli L, Tomasini E, Rossi PD, Lombardi F. Autonomic function in amnestic and non-amnestic mild cognitive impairment: spectral heart rate variability analysis provides evidence for a brain-heart axis. Sci Rep 2020; 10:11661. [PMID: 32669640 PMCID: PMC7363846 DOI: 10.1038/s41598-020-68131-x] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Accepted: 06/15/2020] [Indexed: 12/27/2022] Open
Abstract
Mild cognitive impairment (MCI) is a heterogeneous syndrome with two main clinical subtypes, amnestic (aMCI) and non-amnestic (naMCI). The analysis of heart rate variability (HRV) is a tool to assess autonomic function. Cognitive and autonomic processes are linked via the central autonomic network. Autonomic dysfunction entails several adverse outcomes. However, very few studies have investigated autonomic function in MCI and none have considered MCI subtypes or the relationship of HRV indices with different cognitive domains and structural brain damage. We assessed autonomic function during an active orthostatic challenge in 253 oupatients aged ≥ 65, [n = 82 aMCI, n = 93 naMCI, n = 78 cognitively normal (CN), neuropsychologically tested] with power spectral analysis of HRV. We used visual rating scales to grade cerebrovascular burden and hippocampal/insular atrophy (HA/IA) on neuroimaging. Only aMCI showed a blunted response to orthostasis. Postural changes in normalised low frequency (LF) power and in the LF to high frequency ratio correlated with a memory test (positively) and HA/IA (negatively) in aMCI, and with attention/executive function tests (negatively) and cerebrovascular burden (positively) in naMCI. These results substantiate the view that the ANS is differentially impaired in aMCI and naMCI, consistently with the neuroanatomic substrate of Alzheimer's and small-vessel subcortical ischaemic disease.
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Affiliation(s)
- Paola Nicolini
- Cardiovascular Diseases Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Department of Clinical and Community Sciences, University of Milan, Milan, Italy.
| | - Daniela Mari
- Geriatric Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Department of Clinical and Community Sciences, University of Milan, Milan, Italy
| | - Carlo Abbate
- Geriatric Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Department of Clinical and Community Sciences, University of Milan, Milan, Italy.,IRCCS Fondazione Don Carlo Gnocchi, Milan, Italy
| | - Silvia Inglese
- Geriatric Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Department of Clinical and Community Sciences, University of Milan, Milan, Italy
| | - Laura Bertagnoli
- Geriatric Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Department of Clinical and Community Sciences, University of Milan, Milan, Italy
| | - Emanuele Tomasini
- Geriatric Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Department of Clinical and Community Sciences, University of Milan, Milan, Italy.,IRCCS Fondazione Don Carlo Gnocchi, Milan, Italy
| | - Paolo D Rossi
- Geriatric Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Department of Clinical and Community Sciences, University of Milan, Milan, Italy
| | - Federico Lombardi
- Cardiovascular Diseases Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Department of Clinical and Community Sciences, University of Milan, Milan, Italy
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Park S, Yu J, Kang S, Lee H, Dong SY. Effect of Daily Stress on Heart-Rate Variability during Stroop Color Word Task. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:5976-5979. [PMID: 33019333 DOI: 10.1109/embc44109.2020.9176448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
It is important to care and manage daily stress, even for young healthy individuals. Conventional approach to investigate the effect of mental stress on our body usually utilizes a cognitive task as an induced stressor and compares the response with a resting state. During the process, the effects of daily stress are ignored. We hypothesized that the response to mental stress may differ depending on the daily stress level. As far as our knowledge, the effect of daily stress on cognitive ability and electrophysiological response has not yet been widely investigated. We designed and conducted an experiment to record ECGs while performing a Stroop color word task for 42 young healthy female subjects. We repeated the experiment for two different days with different levels of stress measured by self-report. As a result, we could find significant differences on behavior and heart-rate variability depending on the stress level. These findings may provide a new insight on how to understand the trend of heart-rate variability according to subject's daily stress.
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A multimodal and signals fusion approach for assessing the impact of stressful events on Air Traffic Controllers. Sci Rep 2020; 10:8600. [PMID: 32451424 PMCID: PMC7248090 DOI: 10.1038/s41598-020-65610-z] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 05/04/2020] [Indexed: 11/08/2022] Open
Abstract
Stress is a word used to describe human reactions to emotionally, cognitively and physically challenging experiences. A hallmark of the stress response is the activation of the autonomic nervous system, resulting in the "fight-freeze-flight" response to a threat from a dangerous situation. Consequently, the capability to objectively assess and track a controller's stress level while dealing with air traffic control (ATC) activities would make it possible to better tailor the work shift and maintain high safety levels, as well as to preserve the operator's health. In this regard, sixteen controllers were asked to perform a realistic air traffic management (ATM) simulation during which subjective data (i.e. stress perception) and neurophysiological data (i.e. brain activity, heart rate, and galvanic skin response) were collected with the aim of accurately characterising the controller's stress level experienced in the various experimental conditions. In addition, external supervisors regularly evaluated the controllers in terms of manifested stress, safety, and efficiency throughout the ATM scenario. The results demonstrated 1) how the stressful events caused both supervisors and controllers to underestimate the experienced stress level, 2) the advantage of taking into account both cognitive and hormonal processes in order to define a reliable stress index, and 3) the importance of the points in time at which stress is measured owing to the potential transient effect once the stressful events have ceased.
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