Recovery of heart rate variability after treadmill exercise analyzed by lagged Poincaré plot and spectral characteristics

© 2017 International Federation for Medical and Biological Engineering The aim of this study was to analyze the recovery of heart rate variability (HRV) after treadmill exercise and to investigate the autonomic nervous system response after exercise. Frequency domain indices, i.e., LF(ms 2 ), HF(ms 2 ), LF(n.u.), HF(n.u.) and LF/HF, and lagged Poincaré plot width (SD1 m ) and length (SD2 m ) were introduced for comparison between the baseline period (Pre-E) before treadmill running and two periods after treadmill running (Post-E1 and Post-E2). The correlations between lagged Poincaré plot indices and frequency domain indices were applied to reveal the long-range correlation between linear and nonlinear indices during the recovery of HRV. The results suggested entirely attenuated autonomic nervous activity to the heart following the treadmill exercise. After the treadmill running, the sympathetic nerves achieved dominance and the parasympathetic activity was suppressed, which lasted for more than 4 min. The correlation coefficients between lagged Poincaré plot indices and spectral power indices could separate not only Pre-E and two sessions after the treadmill running, but also the two sessions in recovery periods, i.e., Post-E1 and Post-E2. Lagged Poincaré plot as an innovative nonlinear method showed a better performance over linear frequency domain analysis and conventional nonlinear Poincaré plot

QUANTIFYING GAIT ADAPTABILITY: FRACTALITY, COMPLEXITY, AND STABILITY DURING ASYMMETRIC WALKING

Successful walking necessitates modifying locomotor patterns when encountering organism, task, or environmental constraints. The structure of stride-to-stride variance (fractal dynamics) may represent the adaptive capacity of the locomotor system. To date, however, fractal dynamics have been assessed during unperturbed walking. Quantifying gait adaptability requires tasks that compel locomotor patterns to adapt. The purpose of this dissertation was to determine the potential relationship between fractal dynamics and gait adaptability. The studies presented herein represent a necessary endeavor to incorporate both an analysis of gait fractal dynamics and a task requiring adaptation of locomotor patterns. The adaptation task involved walking asymmetrically on a split-belt treadmill, whereby individuals adapted the relative phasing between legs. This experimental design provided a better understanding of the prospective relationship between fractal dynamics and adaptive capacity. Results from the first study indicated there was no association between unperturbed walking fractal dynamics and gait adaptability in young, healthy adults. However, there was an emergent relationship between asymmetric walking fractal dynamics and gait adaptability. Moreover, fractal dynamics increased during asymmetric walking. The second study investigated fractal dynamics and gait adaptability in healthy, active young and older adults. The findings from study 2 showed no differences between young and older adults regarding unperturbed or asymmetric walking fractal dynamics, or gait adaptability performance. The second study provided further evidence for the lack of association between unperturbed fractal dynamics and gait adaptability. Furthermore, study 2 delivered additional support that asymmetric walking not only yields increased fractal scaling values, but also associates with adaptive gait performance in older adults. Finally, while the first two studies explored stride time monofractality during various walking tasks, the third study aimed to understand the potential multifractality, i.e. temporal evolution of fractal dynamics, of unperturbed and asymmetric walking. The results suggest that unperturbed walking is monofractal in nature, while more challenging asymmetric walking reveals multifractal characteristics, and that multifractality does not associate with adaptive gait performance. This dissertation provides preliminary evidence for the lack of relationship between gait adaptability and unperturbed fractal dynamics, and the emergent association between adaptive gait and asymmetric walking fractality

THE EFFECTS OF CREATINE MONOHYDRATE LOADING ON RECOVERY IN HEALTHY WOMEN THROUGHOUT THE MENSTRUAL CYCLE

This study evaluated the effect of CrM supplementation on exercise recovery, measured from HRV and repeated sprint performance, in women throughout menstrual phase. Data were analyzed for 39 participants randomized to either a CrM group (n=19) or PL group (n=20). CrM supplementation did not appear to influence HRV values as no significant differences were seen in HRV values at rest or post-exercise. For repeated sprint outcomes, there was a significant phase × supplement interaction (p=0.048) for fatigue index, with the greatest improvement seen in luteal phase in the CrM group (-5.8 ± 19.0%) compared to changes in the placebo group (0.1 ± 8.1%). Performance and recovery were reduced in the LP for both groups. Though not statistically significant, the data suggests that CrM could help counteract sprint performance decrements in the LP. This data can help inform CrM loading strategies for active females, demonstrating potential benefits in the LP.Master of Art

CEPS: An Open Access MATLAB Graphical User Interface (GUI) for the Analysis of Complexity and Entropy in Physiological Signals

Background: We developed CEPS as an open access MATLAB® GUI (graphical user interface) for the analysis of Complexity and Entropy in Physiological Signals (CEPS), and demonstrate its use with an example data set that shows the effects of paced breathing (PB) on variability of heart, pulse and respiration rates. CEPS is also sufficiently adaptable to be used for other time series physiological data such as EEG (electroencephalography), postural sway or temperature measurements. Methods: Data were collected from a convenience sample of nine healthy adults in a pilot for a larger study investigating the effects on vagal tone of breathing paced at various different rates, part of a development programme for a home training stress reduction system. Results: The current version of CEPS focuses on those complexity and entropy measures that appear most frequently in the literature, together with some recently introduced entropy measures which may have advantages over those that are more established. Ten methods of estimating data complexity are currently included, and some 28 entropy measures. The GUI also includes a section for data pre-processing and standard ancillary methods to enable parameter estimation of embedding dimension m and time delay τ (‘tau’) where required. The software is freely available under version 3 of the GNU Lesser General Public License (LGPLv3) for non-commercial users. CEPS can be downloaded at https://bitbucket.org/deepak_panday/ceps/src/pipeline_v2/. In our illustration on PB, most complexity and entropy measures decreased significantly in response to breathing at 7 breaths per minute, differentiating more clearly than conventional linear, time- and frequency-domain measures between breathing states. In contrast, Higuchi fractal dimension increased during paced breathing. Conclusions: We have developed CEPS software as a physiological data visualiser able to integrate state of the art techniques. The interface is designed for clinical research and has a structure designed for integrating new tools. The aim is to strengthen collaboration between clinicians and the biomedical community, as demonstrated here by using CEPS to analyse various physiological responses to paced breathing

Training for Optimal Sports Performance and Health

In this book, the emphasis is on various training interventions. Types of exercises that can help improve performance in athletes and health in people facing poor movement diseases.Also, we have presented a variety of strength training interventions in the form of various types of research. On the other hand, we continue to monitor internal and external loads related to non-contact injuries and performance analysis

The effects of regularity of simulated ship motions on the behaviour and physiology of sheep

Floor movement influences sheep responses to transport, but the importance of movement regularity and interactions between sheep are unknown. To test this, sheep were restrained in pairs in a crate mounted on a moveable, programmable platform for 60 min periods, changing treatments over 12 consecutive days. In an initial experiment a repeated speed of movement and change in angle (regular movement) was compared to variable angles and speeds (irregular movement) of roll, pitch or combined movements, for sheep behaviour, heart rate and feed and water intake responses. Feed intake was increased by irregular roll+pitch motion (P = 0.04). During irregular sequences sheep affiliated more, with their heads one above the other (P = 0.001) and supported themselves against the crate (P < 0.001) or kneeling (P = 0.03). Irregular sequences and combined roll and pitch synergistically increased stepping behaviour, indicating loss of balance, and heart rate, possibly indicating stress (P < 0.001). Heat rate data demonstrated that the RMSSD band was reduced during irregular movement (P = 0.04), and LF/HF ratio increased during irregular sequences of roll+pitch (P = 0.007), suggesting less parasympathetic nervous system activity. In a second experiment, we investigated the effects of these floor motion patterns with and without a barrier to separate the sheep. With no barrier or irregular motion, sheep stepped more to avoid loss of balance (P < 0.001) and were again more affiliative. During irregular motion they supported themselves more against the crate (P < 0.001). With no barrier there was more agonistic behaviour (body pushing (P = 0.02), butting (P = 0.02) and evading the other sheep (P = 0.001) and less rumination (P = 0.02), which together with a reduction in RMSSD and NN50 suggested that sheep welfare was reduced by the close proximity of the other sheep. The ratio of low to high frequency beats was highest (P = 0.005) and the RMSSD and NN50 were lowest (P < 0.001) during irregular motion and no barrier. Evidence is provided that sheep were both more stressed in this combination of treatments and also exercising more, through stepping behaviour. Thus irregular sequences and combined roll and pitch caused stress and increased activity to correct loss of balance, as well as increased affiliative behaviour. Separating sheep during irregular motion reduced body instability and stress, suggesting that close stocking is detrimental to their welfare

Fear Classification using Affective Computing with Physiological Information and Smart-Wearables

Mención Internacional en el título de doctorAmong the 17 Sustainable Development Goals proposed within the 2030 Agenda and adopted by all of the United Nations member states, the fifth SDG is a call for action to effectively turn gender equality into a fundamental human right and an essential foundation for a better world. It includes the eradication of all types of violence against women. Focusing on the technological perspective, the range of available solutions intended to prevent this social problem is very limited. Moreover, most of the solutions are based on a panic button approach, leaving aside the usage and integration of current state-of-the-art technologies, such as the Internet of Things (IoT), affective computing, cyber-physical systems, and smart-sensors. Thus, the main purpose of this research is to provide new insight into the design and development of tools to prevent and combat Gender-based Violence risky situations and, even, aggressions, from a technological perspective, but without leaving aside the different sociological considerations directly related to the problem. To achieve such an objective, we rely on the application of affective computing from a realist point of view, i.e. targeting the generation of systems and tools capable of being implemented and used nowadays or within an achievable time-frame. This pragmatic vision is channelled through: 1) an exhaustive study of the existing technological tools and mechanisms oriented to the fight Gender-based Violence, 2) the proposal of a new smart-wearable system intended to deal with some of the current technological encountered limitations, 3) a novel fear-related emotion classification approach to disentangle the relation between emotions and physiology, and 4) the definition and release of a new multi-modal dataset for emotion recognition in women. Firstly, different fear classification systems using a reduced set of physiological signals are explored and designed. This is done by employing open datasets together with the combination of time, frequency and non-linear domain techniques. This design process is encompassed by trade-offs between both physiological considerations and embedded capabilities. The latter is of paramount importance due to the edge-computing focus of this research. Two results are highlighted in this first task, the designed fear classification system that employed the DEAP dataset data and achieved an AUC of 81.60% and a Gmean of 81.55% on average for a subjectindependent approach, and only two physiological signals; and the designed fear classification system that employed the MAHNOB dataset data achieving an AUC of 86.00% and a Gmean of 73.78% on average for a subject-independent approach, only three physiological signals, and a Leave-One-Subject-Out configuration. A detailed comparison with other emotion recognition systems proposed in the literature is presented, which proves that the obtained metrics are in line with the state-ofthe- art. Secondly, Bindi is presented. This is an end-to-end autonomous multimodal system leveraging affective IoT throughout auditory and physiological commercial off-theshelf smart-sensors, hierarchical multisensorial fusion, and secured server architecture to combat Gender-based Violence by automatically detecting risky situations based on a multimodal intelligence engine and then triggering a protection protocol. Specifically, this research is focused onto the hardware and software design of one of the two edge-computing devices within Bindi. This is a bracelet integrating three physiological sensors, actuators, power monitoring integrated chips, and a System- On-Chip with wireless capabilities. Within this context, different embedded design space explorations are presented: embedded filtering evaluation, online physiological signal quality assessment, feature extraction, and power consumption analysis. The reported results in all these processes are successfully validated and, for some of them, even compared against physiological standard measurement equipment. Amongst the different obtained results regarding the embedded design and implementation within the bracelet of Bindi, it should be highlighted that its low power consumption provides a battery life to be approximately 40 hours when using a 500 mAh battery. Finally, the particularities of our use case and the scarcity of open multimodal datasets dealing with emotional immersive technology, labelling methodology considering the gender perspective, balanced stimuli distribution regarding the target emotions, and recovery processes based on the physiological signals of the volunteers to quantify and isolate the emotional activation between stimuli, led us to the definition and elaboration of Women and Emotion Multi-modal Affective Computing (WEMAC) dataset. This is a multimodal dataset in which 104 women who never experienced Gender-based Violence that performed different emotion-related stimuli visualisations in a laboratory environment. The previous fear binary classification systems were improved and applied to this novel multimodal dataset. For instance, the proposed multimodal fear recognition system using this dataset reports up to 60.20% and 67.59% for ACC and F1-score, respectively. These values represent a competitive result in comparison with the state-of-the-art that deal with similar multi-modal use cases. In general, this PhD thesis has opened a new research line within the research group under which it has been developed. Moreover, this work has established a solid base from which to expand knowledge and continue research targeting the generation of both mechanisms to help vulnerable groups and socially oriented technology.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: David Atienza Alonso.- Secretaria: Susana Patón Álvarez.- Vocal: Eduardo de la Torre Arnan

Simple Tests for Assessing Kidney and Cardiovascular Function in Chromic Kidney Disease Patients (Stages 2, 3 and 4)

Reliable measurement of glomerular filtration rate (GFR) is an important aspect of clinical decision making and forms the basis of classification for kidney function. Moreover the interpretation of risk factors for progression of CKD linked to cardiovascular disease (CVD) remains difficult, particularly in older subjects. To address this GFR and the cardiovascular parameters arterial stiffness, heart rate variability (HRV) and natriuretic peptides (NT- proBNP and NT-proCNP) were measured in a cohort of CKD patients of stages 2,3 and 4. The primary aim however of the thesis was to measure the Kidney Functional Reserve (KFR) of CKD3 and CKD4 patients as it has been thought of as analogous to cardiac functional reserve. CysC, creatinine clearance (CrCl) and with simultaneous radionuclide 99technetium diethylenetriaminepentaacetatic acid (Tc-99m) measured GFR (mGFR) of KFR in 19 CKD Stage 3 and 21 CKD Stage 4 patients yielded good agreement. KFR was not correlated with baseline kidney function. Eight CKD Stage 3 (42%) and 11 CKD Stage 4 (52%) subjects reached their lowest serum CysC concentration 4 hours after OPL. CysC KFR and baseline serum creatinine (sCr) predicted Major Adverse Kidney Event, death or dialysis (MAKE-T) and MAKE-F (fast progression with GFR decrease > 5ml/min/year) with a respective area under the curve (AUC) of 0.73 (95% confidence interval [CI] 0.48 – 0.890) and 0.71 (95% CI: 0.51–0.84). Including CysC KFR, age, baseline sCr and nadir CysC predicted a decrease in sCr-estimated GFR >1.2 ml/min/year (MAKE-S) with an AUC of 0.89. In the latter case the inclusion of cardiovascular variables; “Recovery” (1-4 minutes post exercise) RMSSD, urinary and serum NT-proCNP concentration and whether CysC GFR reserve was early or late in nadir improved the AUC to 1.00 (AICc =15.41). In conclusion serial CysC may facilitate monitoring of KFR in clinical practice along with the use of a portable exercise stress test to increase c-f-PWV (carotid-femoral Pulse Wave Velocity) and identify CKD patients with serious/subliminal vascular stiffening. Future research may confirm this studies other preliminary finding that urinary NT-proCNP concentration may relate to renal haemodynamic function in CKD stages, as this correlated with stimulated mGFR (R2 = 0.41). In short this thesis has furthered the premise that the heart and kidney are interlinked via cardiovascular physiology which likely can be used to predict CKD progression

Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders

The aging population and the increased prevalence of neurological diseases have raised the issue of gait and balance disorders as a major public concern worldwide. Indeed, gait and balance disorders are responsible for a high healthcare and economic burden on society, thus, requiring new solutions to prevent harmful consequences. Recently, wearable sensors have provided new challenges and opportunities to address this issue through innovative diagnostic and therapeutic strategies. Accordingly, the book “Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders” collects the most up-to-date information about the objective evaluation of gait and balance disorders, by means of wearable biosensors, in patients with various types of neurological diseases, including Parkinson’s disease, multiple sclerosis, stroke, traumatic brain injury, and cerebellar ataxia. By adopting wearable technologies, the sixteen original research articles and reviews included in this book offer an updated overview of the most recent approaches for the objective evaluation of gait and balance disorders

Multidimensional embedded MEMS motion detectors for wearable mechanocardiography and 4D medical imaging

Background: Cardiovascular diseases are the number one cause of death. Of these deaths, almost 80% are due to coronary artery disease (CAD) and cerebrovascular disease. Multidimensional microelectromechanical systems (MEMS) sensors allow measuring the mechanical movement of the heart muscle offering an entirely new and innovative solution to evaluate cardiac rhythm and function. Recent advances in miniaturized motion sensors present an exciting opportunity to study novel device-driven and functional motion detection systems in the areas of both cardiac monitoring and biomedical imaging, for example, in computed tomography (CT) and positron emission tomography (PET). Methods: This Ph.D. work describes a new cardiac motion detection paradigm and measurement technology based on multimodal measuring tools — by tracking the heart’s kinetic activity using micro-sized MEMS sensors — and novel computational approaches — by deploying signal processing and machine learning techniques—for detecting cardiac pathological disorders. In particular, this study focuses on the capability of joint gyrocardiography (GCG) and seismocardiography (SCG) techniques that constitute the mechanocardiography (MCG) concept representing the mechanical characteristics of the cardiac precordial surface vibrations. Results: Experimental analyses showed that integrating multisource sensory data resulted in precise estimation of heart rate with an accuracy of 99% (healthy, n=29), detection of heart arrhythmia (n=435) with an accuracy of 95-97%, ischemic disease indication with approximately 75% accuracy (n=22), as well as significantly improved quality of four-dimensional (4D) cardiac PET images by eliminating motion related inaccuracies using MEMS dual gating approach. Tissue Doppler imaging (TDI) analysis of GCG (healthy, n=9) showed promising results for measuring the cardiac timing intervals and myocardial deformation changes. Conclusion: The findings of this study demonstrate clinical potential of MEMS motion sensors in cardiology that may facilitate in time diagnosis of cardiac abnormalities. Multidimensional MCG can effectively contribute to detecting atrial fibrillation (AFib), myocardial infarction (MI), and CAD. Additionally, MEMS motion sensing improves the reliability and quality of cardiac PET imaging.Moniulotteisten sulautettujen MEMS-liiketunnistimien käyttö sydänkardiografiassa sekä lääketieteellisessä 4D-kuvantamisessa Tausta: Sydän- ja verisuonitaudit ovat yleisin kuolinsyy. Näistä kuolemantapauksista lähes 80% johtuu sepelvaltimotaudista (CAD) ja aivoverenkierron häiriöistä. Moniulotteiset mikroelektromekaaniset järjestelmät (MEMS) mahdollistavat sydänlihaksen mekaanisen liikkeen mittaamisen, mikä puolestaan tarjoaa täysin uudenlaisen ja innovatiivisen ratkaisun sydämen rytmin ja toiminnan arvioimiseksi. Viimeaikaiset teknologiset edistysaskeleet mahdollistavat uusien pienikokoisten liiketunnistusjärjestelmien käyttämisen sydämen toiminnan tutkimuksessa sekä lääketieteellisen kuvantamisen, kuten esimerkiksi tietokonetomografian (CT) ja positroniemissiotomografian (PET), tarkkuuden parantamisessa. Menetelmät: Tämä väitöskirjatyö esittelee uuden sydämen kineettisen toiminnan mittaustekniikan, joka pohjautuu MEMS-anturien käyttöön. Uudet laskennalliset lähestymistavat, jotka perustuvat signaalinkäsittelyyn ja koneoppimiseen, mahdollistavat sydämen patologisten häiriöiden havaitsemisen MEMS-antureista saatavista signaaleista. Tässä tutkimuksessa keskitytään erityisesti mekanokardiografiaan (MCG), joihin kuuluvat gyrokardiografia (GCG) ja seismokardiografia (SCG). Näiden tekniikoiden avulla voidaan mitata kardiorespiratorisen järjestelmän mekaanisia ominaisuuksia. Tulokset: Kokeelliset analyysit osoittivat, että integroimalla usean sensorin dataa voidaan mitata syketiheyttä 99% (terveillä n=29) tarkkuudella, havaita sydämen rytmihäiriöt (n=435) 95-97%, tarkkuudella, sekä havaita iskeeminen sairaus noin 75% tarkkuudella (n=22). Lisäksi MEMS-kaksoistahdistuksen avulla voidaan parantaa sydämen 4D PET-kuvan laatua, kun liikeepätarkkuudet voidaan eliminoida paremmin. Doppler-kuvantamisessa (TDI, Tissue Doppler Imaging) GCG-analyysi (terveillä, n=9) osoitti lupaavia tuloksia sydänsykkeen ajoituksen ja intervallien sekä sydänlihasmuutosten mittaamisessa. Päätelmä: Tämän tutkimuksen tulokset osoittavat, että kardiologisilla MEMS-liikeantureilla on kliinistä potentiaalia sydämen toiminnallisten poikkeavuuksien diagnostisoinnissa. Moniuloitteinen MCG voi edistää eteisvärinän (AFib), sydäninfarktin (MI) ja CAD:n havaitsemista. Lisäksi MEMS-liiketunnistus parantaa sydämen PET-kuvantamisen luotettavuutta ja laatua