Cardiorespiratory profiling during simulated lunar mission using impedance pneumography

Abstract Manned spaceflight requires research in diverse areas, including neuropsychology and human physiology. For these subjects, the Lunares Analog Research Station was established in Pila, Poland. It allows testing of crew members under space-like conditions. One experiment, Lunar Expedition I, was performed on a group of 6 analogue astronauts over 14 days. All were studied for their subjective perception of time and also asked to carry out mission-specific activities, like digging or repairing a rover during an extravehicular activity (EVA). The aims of the study were to measure cardiorespiratory signals using ECG and impedance pneumography devices under those conditions; to evaluate the quality of the data and the level of motion artefacts; and to assess the subjects’ status and adaptation. We used our own prototype, Pneumonitor 2, that enables registering respiratory-related impedance curve, a single-lead ECG and 3-axis accelerometer signals. Due to problems with a detachment of electrodes, we ultimately collected 10 full registrations from 5 astronauts. All signals were pre-processed and annotated. The set of cardiorespiratory parameters, including heart and respiratory activity indicators, was calculated for 3 main states: at rest, doing squats and performing various activities during EVA. We compared the results with normative values collected from elite athletes. The considered parameters were found to be in the normal range, typically slightly worse than the average for the athletes. The physiological responses are in line with expectations. Impedance pneumography enables to measure quantitative parameters of breathing like tidal volume and may be used during dynamic conditions. Combined with the ECG signal provides an objective astronaut’s cardiorespiratory profile. One can use it to assess the adaptation and to plan the schedule of the mission. However, there is a need for development of a wearable electronic textile solution for the target electrodes, to deal with sweating occurring while wearing a three-layer EVA suit.

[1]  Franz Konstantin Fuss,et al.  Comparison of Non-Invasive Individual Monitoring of the Training and Health of Athletes with Commercially Available Wearable Technologies , 2016, Front. Physiol..

[2]  G. Millet,et al.  Monitoring Fatigue Status with HRV Measures in Elite Athletes: An Avenue Beyond RMSSD? , 2015, Front. Physiol..

[3]  Uncoupling of cardiac and respiratory rhythm in atrial fibrillation , 2016, Biomedizinische Technik. Biomedical engineering.

[4]  Mirjana M. Platiša,et al.  Bidirectional Cardio-Respiratory Interactions in Heart Failure , 2018, Front. Physiol..

[5]  Mohammad B. Shamsollahi,et al.  ECG-derived respiration estimation from single-lead ECG using gaussian process and phase space reconstruction methods , 2018, Biomed. Signal Process. Control..

[6]  Thomas Ritz,et al.  Studying noninvasive indices of vagal control: The need for respiratory control and the problem of target specificity , 2009, Biological Psychology.

[7]  Jari Hyttinen,et al.  Assessment of Pulmonary Flow Using Impedance Pneumography , 2010, IEEE Transactions on Biomedical Engineering.

[8]  H. Krysztofiak,et al.  Discovery of Causal Paths in Cardiorespiratory Parameters: A Time-Independent Approach in Elite Athletes , 2018, Front. Physiol..

[9]  P. Larsen,et al.  Respiratory sinus arrhythmia in conscious humans during spontaneous respiration , 2010, Respiratory Physiology & Neurobiology.

[10]  Xuan Zeng,et al.  A novel single-arm-worn 24 h heart disease monitor empowered by machine intelligence , 2018, Biomed. Signal Process. Control..

[11]  David Giles,et al.  Validity of the Polar V800 heart rate monitor to measure RR intervals at rest , 2015, European Journal of Applied Physiology.

[12]  M. Buchheit Monitoring training status with HR measures: do all roads lead to Rome? , 2014, Front. Physiol..

[13]  Gerard Cybulski,et al.  Assessment of calibration methods on impedance pneumography accuracy , 2016, Biomedizinische Technik. Biomedical engineering.

[14]  Dian Zhou,et al.  A Novel Framework for Motion-Tolerant Instantaneous Heart Rate Estimation by Phase-Domain Multiview Dynamic Time Warping , 2017, IEEE Transactions on Biomedical Engineering.

[15]  Andrew E. Kilding,et al.  Training Adaptation and Heart Rate Variability in Elite Endurance Athletes: Opening the Door to Effective Monitoring , 2013, Sports Medicine.

[16]  Michael Kellmann,et al.  Markers for Routine Assessment of Fatigue and Recovery in Male and Female Team Sport Athletes during High-Intensity Interval Training , 2015, PloS one.

[17]  Gerard Cybulski,et al.  Ambulatory Devices Measuring Cardiorespiratory Activity with Motion , 2017, BIODEVICES.

[18]  J. Ip Wearable Devices for Cardiac Rhythm Diagnosis and Management. , 2019, JAMA.

[19]  Christophe Hautier,et al.  A pilot study on quantification of training load: The use of HRV in training practice , 2016, European journal of sport science.

[20]  P. Krzesiński,et al.  Cardiorespiratory coupling in young healthy subjects , 2017, Physiological measurement.

[21]  Gerard Cybulski,et al.  Graphene electrodes for long-term impedance pneumography - a feasibility study , 2017 .

[22]  A. Malliani,et al.  Heart rate variability. Standards of measurement, physiological interpretation, and clinical use , 1996 .

[23]  Marcel Młyńczak,et al.  Cardiorespiratory Temporal Causal Links and the Differences by Sport or Lack Thereof , 2019, Front. Physiol..

[24]  Eileen Y. Robertson,et al.  Monitoring Athletic Training Status Through Autonomic Heart Rate Regulation: A Systematic Review and Meta-Analysis , 2016, Sports Medicine.

[25]  Philippe Ravier,et al.  Use of cardiorespiratory coherence to separate spectral bands of the heart rate variability , 2018, Biomed. Signal Process. Control..

[26]  Jari Hyttinen,et al.  Novel electrode configuration for highly linear impedance pneumography , 2013, Biomedizinische Technik. Biomedical engineering.

[27]  P. Grossman,et al.  Toward understanding respiratory sinus arrhythmia: Relations to cardiac vagal tone, evolution and biobehavioral functions , 2007, Biological Psychology.

[28]  J. Sacha,et al.  Heart Rate and Respiratory Rate Influence on Heart Rate Variability Repeatability: Effects of the Correction for the Prevailing Heart Rate , 2016, Front. Physiol..

[29]  Ioannis Vlachos,et al.  Mutual information in the frequency domain for the study of biological systems , 2018, Biomed. Signal Process. Control..

[30]  Gerard Cybulski,et al.  Decomposition of the Cardiac and Respiratory Components from Impedance Pneumography Signals , 2017, BIOSIGNALS.

[31]  Maurizio Bertollo,et al.  Monitoring weekly heart rate variability in futsal players during the preseason: the importance of maintaining high vagal activity , 2016, Journal of sports sciences.

[32]  Luca Faes,et al.  Cardiovascular control and time domain Granger causality: insights from selective autonomic blockade , 2013, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[33]  M. Altini,et al.  Comparison of Heart-Rate-Variability Recording With Smartphone Photoplethysmography, Polar H7 Chest Strap, and Electrocardiography. , 2017, International journal of sports physiology and performance.