Review and classification of variability analysis techniques with clinical applications

Analysis of patterns of variation of time-series, termed variability analysis, represents a rapidly evolving discipline with increasing applications in different fields of science. In medicine and in particular critical care, efforts have focussed on evaluating the clinical utility of variability. However, the growth and complexity of techniques applicable to this field have made interpretation and understanding of variability more challenging. Our objective is to provide an updated review of variability analysis techniques suitable for clinical applications. We review more than 70 variability techniques, providing for each technique a brief description of the underlying theory and assumptions, together with a summary of clinical applications. We propose a revised classification for the domains of variability techniques, which include statistical, geometric, energetic, informational, and invariant. We discuss the process of calculation, often necessitating a mathematical transform of the time-series. Our aims are to summarize a broad literature, promote a shared vocabulary that would improve the exchange of ideas, and the analyses of the results between different studies. We conclude with challenges for the evolving science of variability analysis

Advanced analyses of physiological signals and their role in Neonatal Intensive Care

Preterm infants admitted to the neonatal intensive care unit (NICU) face an array of life-threatening diseases requiring procedures such as resuscitation and invasive monitoring, and other risks related to exposure to the hospital environment, all of which may have lifelong implications. This thesis examined a range of applications for advanced signal analyses in the NICU, from identifying of physiological patterns associated with neonatal outcomes, to evaluating the impact of certain treatments on physiological variability. Firstly, the thesis examined the potential to identify infants at risk of developing intraventricular haemorrhage, often interrelated with factors leading to preterm birth, mechanical ventilation, hypoxia and prolonged apnoeas. This thesis then characterised the cardiovascular impact of caffeine therapy which is often administered to prevent and treat apnoea of prematurity, finding greater pulse pressure variability and enhanced responsiveness of the autonomic nervous system. Cerebral autoregulation maintains cerebral blood flow despite fluctuations in arterial blood pressure and is an important consideration for preterm infants who are especially vulnerable to brain injury. Using various time and frequency domain correlation techniques, the thesis found acute changes in cerebral autoregulation of preterm infants following caffeine therapy. Nutrition in early life may also affect neurodevelopment and morbidity in later life. This thesis developed models for identifying malnutrition risk using anthropometry and near-infrared interactance features. This thesis has presented a range of ways in which advanced analyses including time series analysis, feature selection and model development can be applied to neonatal intensive care. There is a clear role for such analyses in early detection of clinical outcomes, characterising the effects of relevant treatments or pathologies and identifying infants at risk of later morbidity

Application of the entropy of approximation for the nonlinear characterizationin patients with Chagas disease

Chagas disease American trypanosomiasis is caused by a flagellated parasite: Trypanosoma cruzi, transmitted by an insect of the genus Triatoma and also by blood transfusions. In Latin America, the number of infected people is approximately 6 million, with a population exposed to the risk of infection of 550000. It is our interest to develop a non-invasive and low-cost methodology, capable of detecting any early cardiac alteration that also allows us to see dysautonomia or dysfunction within 24 hours and with this it could be used to detect any cardiac alteration caused by T early Cruzi. For this, we analyzed the 24-hour Holter ECG records in 107 patients with ECG abnormalities (CH2), 102 patients without ECG alterations (CH1) who had positive serological results for Chagas disease and 83 volunteers without positive serological results for Chagas disease (CONTROL). Approximate entropy was used to quantify the regularity of electrocardiograms (ECG) in the three groups. We analyzed 288 ECG segments per patient. Significant differences were found between the CONTROL-CH1, CONTROL-CH2 and CH1-CH2 groups.Postprint (published version

Heart Rate Variability (HRV) analysis : a methodology for organizational neuroscience

Recently, the application of neuroscience methods and findings to the study of organizational phenomena has gained significant interest and converged in the emerging field of organizational neuroscience. Yet, this body of research has principally focused on the brain, often overlooking fuller analysis of the activities of the human nervous system and associated methods available to assess them. In this paper, we aim to narrow this gap by reviewing heart rate variability (HRV) analysis, which is that set of methods assessing beat-to-beat changes in the heart rhythm over time, used to draw inference on the outflow of the autonomic nervous system (ANS). In addition to anatomo- physiological and detailed methodological considerations, we discuss related theoretical, ethical, and practical implications. Overall, we argue that this methodology offers the opportunity not only to inform on a wealth of constructs relevant for management inquiries, but also to advance the organizational neuroscience research agenda and its ecological validity

A Comprehensive Review of Techniques for Processing and Analyzing Fetal Heart Rate Signals

The availability of standardized guidelines regarding the use of electronic fetal monitoring (EFM) in clinical practice has not effectively helped to solve the main drawbacks of fetal heart rate (FHR) surveillance methodology, which still presents inter- and intra-observer variability as well as uncertainty in the classification of unreassuring or risky FHR recordings. Given the clinical relevance of the interpretation of FHR traces as well as the role of FHR as a marker of fetal wellbeing autonomous nervous system development, many different approaches for computerized processing and analysis of FHR patterns have been proposed in the literature. The objective of this review is to describe the techniques, methodologies, and algorithms proposed in this field so far, reporting their main achievements and discussing the value they brought to the scientific and clinical community. The review explores the following two main approaches to the processing and analysis of FHR signals: traditional (or linear) methodologies, namely, time and frequency domain analysis, and less conventional (or nonlinear) techniques. In this scenario, the emerging role and the opportunities offered by Artificial Intelligence tools, representing the future direction of EFM, are also discussed with a specific focus on the use of Artificial Neural Networks, whose application to the analysis of accelerations in FHR signals is also examined in a case study conducted by the authors

Characterizing psychological dimensions in non-pathological subjects through autonomic nervous system dynamics

The objective assessment of psychological traits of healthy subjects and psychiatric patients has been growing interest in clinical and bioengineering research fields during the last decade. Several experimental evidences strongly suggest that a link between Autonomic Nervous System (ANS) dynamics and specific dimensions such as anxiety, social phobia, stress, and emotional regulation might exist. Nevertheless, an extensive investigation on a wide range of psycho-cognitive scales and ANS non-invasive markers gathered from standard and non-linear analysis still needs to be addressed. In this study, we analyzed the discerning and correlation capabilities of a comprehensive set of ANS features and psycho-cognitive scales in 29 non-pathological subjects monitored during resting conditions. In particular, the state of the art of standard and non-linear analysis was performed on Heart Rate Variability, InterBreath Interval series, and InterBeat Respiration series, which were considered as monovariate and multivariate measurements. Experimental results show that each ANS feature is linked to specific psychological traits. Moreover, non-linear analysis outperforms the psychological assessment with respect to standard analysis. Considering that the current clinical practice relies only on subjective scores from interviews and questionnaires, this study provides objective tools for the assessment of psychological dimensions

Slow 0.1 Hz Breathing and Body Posture Induced Perturbations of RRI and Respiratory Signal Complexity and Cardiorespiratory Coupling

Objective: We explored the physiological background of the non-linear operating mode of cardiorespiratory oscillators as the fundamental question of cardiorespiratory homeodynamics and as a prerequisite for the understanding of neurocardiovascular diseases. We investigated 20 healthy human subjects for changes using electrocardiac RR interval (RRI) and respiratory signal (Resp) Detrended Fluctuation Analysis (DFA, α1RRI, α2RRI, α1Resp, α2Resp), Multiple Scaling Entropy (MSERRI1−4, MSERRI5−10, MSEResp1−4, MSEResp5−10), spectral coherence (CohRRI−Resp), cross DFA (ρ1 and ρ2) and cross MSE (XMSE1−4 and XMSE5−10) indices in four physiological conditions: supine with spontaneous breathing, standing with spontaneous breathing, supine with 0.1 Hz breathing and standing with 0.1 Hz breathing. Main results: Standing is primarily characterized by the change of RRI parameters, insensitivity to change with respiratory parameters, decrease of CohRRI−Resp and insensitivity to change of in ρ1, ρ2, XMSE1−4, and XMSE5−10. Slow breathing in supine position was characterized by the change of the linear and non-linear parameters of both signals, reflecting the dominant vagal RRI modulation and the impact of slow 0.1 Hz breathing on Resp parameters. CohRRI−Resp did not change with respect to supine position, while ρ1 increased. Slow breathing in standing reflected the qualitatively specific state of autonomic regulation with striking impact on both cardiac and respiratory parameters, with specific patterns of cardiorespiratory coupling. Significance: Our results show that cardiac and respiratory short term and long term complexity parameters have different, state dependent patterns. Sympathovagal non-linear interactions are dependent on the pattern of their activation, having different scaling properties when individually activated with respect to the state of their joint activation. All investigated states induced a change of α1 vs. α2 relationship, which can be accurately expressed by the proposed measure—inter-fractal angle θ. Short scale (α1 vs. MSE1−4) and long scale (α2 vs. MSE5−10) complexity measures had reciprocal interrelation in standing with 0.1 Hz breathing, with specific cardiorespiratory coupling pattern (ρ1 vs. XMSE1−4). These results support the hypothesis of hierarchical organization of cardiorespiratory complexity mechanisms and their recruitment in ascendant manner with respect to the increase of behavioral challenge complexity. Specific and comprehensive cardiorespiratory regulation in standing with 0.1 Hz breathing suggests this state as the potentially most beneficial maneuver for cardiorespiratory conditioning. © Copyright © 2020 Matić, Platiša, Kalauzi and Bojić

Application of linear and nonlinear methods for processing HRV and EEG signals

2013/2014L'elaborazione dei segnali biomedici è fondamentale per l'interpretazione oggettiva dei sistemi fisiologici, infatti, permette di estrarre e quantificare le informazioni contenute nei segnali che sono generati dai sistemi oggetto di studio. Per analizzare i segnali biomedici, sono stati introdotti un gran numero di algoritmi inizialmente nati in ambiti di ricerca differenti. Negli ultimi decenni, il classico approccio lineare, basato principalmente sull'analisi spettrale, è stato affiancato con successo da metodi e tecniche derivanti dalla teoria della dinamica nonlineare e, in particolare, da quella del caos deterministico. L'obiettivo di questa tesi è quello di valutare i risultati dell'applicazione di diversi metodi di elaborazione, lineari e non lineari, a specifici studi clinici basati sul segnale di variabilità cardiaca (Heart Rate Variability, HRV) e sul segnale elettroencefalografico (EEG). Questi segnali, infatti, mostrano comportamenti attribuibili a sistemi la cui natura può essere alternativamente di tipo lineare o non, a seconda delle condizioni nelle quali i sistemi vengono analizzati. Nella prima parte della tesi, sono presentati i due segnali oggetto di studio (HRV ed EEG) e le tecniche di analisi utilizzate. Nel capitolo 1 vengono descritti il significato fisiologico, i requisiti necessari per l'acquisizione dei dati e i metodi di pre-elaborazione dei segnali. Nel capitolo 2 sono presentati i metodi e gli algoritmi utilizzati in questa tesi per la caratterizzazione delle diverse condizioni sperimentali in cui HRV e EEG sono stati studiati, prestando particolare attenzione alle tecniche di analisi non lineare. Nei capitoli seguenti (capitoli 3-7), sono presentate le cinque applicazioni dell'analisi dei segnali HRV ed EEG esaminate durante il dottorato. Più precisamente, le prime tre riguardano la variabilità cardiaca, le altre due il segnale EEG. Per quanto riguarda il segnale HRV, il primo studio analizza le variazioni delle proprietà spettrali e frattali in soggetti sani di diversa età; il secondo è focalizzatosull'importanza dell'approccio nonlineare nell'analisi del segnale HRV ricavato da registrazioni polisonnografiche di pazienti affetti da gravi apnee notturne; il terzo presenta le differenze nelle caratteristiche spettrali e nonlineari della variabilità cardiaca in pazienti con scompenso cardiaco determinato da diverse eziologie. Invece, per il segnale EEG, il primo studio analizza le alterazioni negli indici spettrali e nonlineari in pazienti con deficit cognitivi soggettivi e lievi, mentre il secondo valuta l'efficacia di un nuovo protocollo per la riabilitazione della malattia di Parkinson, attraverso la quantificazione dei parametri spettrali dell'EEG.XXVII Ciclo198

Heart rate variability in psychology: a review of hrv indices and an analysis tutorial

The use of heart rate variability (HRV) in research has been greatly popularized over the past decades due to the ease and affordability of HRV collection, coupled with its clinical relevance and significant relationships with psychophysiological constructs and psychopathological disorders. Despite the wide use of electrocardiograms (ECG) in research and advancements in sensor technology, the analytical approach and steps applied to obtain HRV measures can be seen as complex. Thus, this poses a challenge to users who may not have the adequate background knowledge to obtain the HRV indices reliably. To maximize the impact of HRV-related research and its reproducibility, parallel advances in users’ understanding of the indices and the standardization of analysis pipelines in its utility will be crucial. This paper addresses this gap and aims to provide an overview of the most up-to-date and commonly used HRV indices, as well as common research areas in which these indices have proven to be very useful, particularly in psychology. In addition, we also provide a step-by-step guide on how to perform HRV analysis using an integrative neurophysiological toolkit, NeuroKit2

Physiological mechanisms mediating the coupling between heart period and arterial pressure in response to postural changes in humans

The upright posture strengthens the coupling between heart period (HP) and systolic arterial pressure (SAP) consistently with a greater contribution of the arterial baroreflex to cardiac control, while paradoxically decreasing cardiac baroreflex sensitivity (cBRS). To investigate the physiological mechanisms that mediate the coupling between HP and SAP in response to different postures, we analyzed the cross-correlation functions between low-frequency HP and SAP fluctuations and estimated cBRS with the sequence technique in healthy male subjects during passive head-up tilt test (HUTT, n = 58), during supine wakefulness, supine slow-wave sleep (SWS), and in the seated and active standing positions (n = 8), and during progressive loss of 1 L blood (n = 8) to decrease central venous pressure in the supine position. HUTT, SWS, the seated, and the standing positions, but not blood loss, entailed significant increases in the positive correlation between HP and the previous SAP values, which is the expected result of arterial baroreflex control, compared with baseline recordings in the supine position during wakefulness. These increases were mirrored by increases in the low-frequency variability of SAP in each condition but SWS. cBRS decreased significantly during HUTT, in the seated and standing positions, and after blood loss compared with baseline during wakefulness. These decreases were mirrored by decreases in the RMSSD index, which reflects cardiac vagal modulation. These results support the view that the cBRS decrease associated with the upright posture is a byproduct of decreased cardiac vagal modulation, triggered by the arterial baroreflex in response to central hypovolemia. Conversely, the greater baroreflex contribution to cardiac control associated with upright posture may be explained, at least in part, by enhanced fluctuations of SAP, which elicit a more effective entrainment of HP fluctuations by the arterial baroreflex. These SAP fluctuations may result from enhanced fluctuations of vascular resistance specific to the upright posture, and not be driven by the accompanying central hypovolemia