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

The fractal heart — embracing mathematics in the cardiology clinic

For clinicians grappling with quantifying the complex spatial and temporal patterns of cardiac structure and function (such as myocardial trabeculae, coronary microvascular anatomy, tissue perfusion, myocyte histology, electrical conduction, heart rate, and blood-pressure variability), fractal analysis is a powerful, but still underused, mathematical tool. In this Perspectives article, we explain some fundamental principles of fractal geometry and place it in a familiar medical setting. We summarize studies in the cardiovascular sciences in which fractal methods have successfully been used to investigate disease mechanisms, and suggest potential future clinical roles in cardiac imaging and time series measurements. We believe that clinical researchers can deploy innovative fractal solutions to common cardiac problems that might ultimately translate into advancements for patient care

Models and Analysis of Vocal Emissions for Biomedical Applications

The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy. This edition celebrates twenty years of uninterrupted and succesfully research in the field of voice analysis

Fractal correlation property of heart rate variability in chronic obstructive pulmonary disease

Background: It was reported that autonomic nervous system function is altered in subjects with chronic obstructive pulmonary disease (COPD). We evaluated short-and long-term fractal exponents of heart rate variability (HRV) in COPD subjects.Patients and methods: We analyzed data from 30 volunteers, who were divided into two groups according to spirometric values: COPD (n = 15) and control (n = 15). for analysis of HRV indices, HRV was recorded beat by beat with the volunteers in the supine position for 30 minutes. We analyzed the linear indices in the time (SDNN [standard deviation of normal to normal] and RMSSD [root-mean square of differences]) and frequency domains (low frequency [LF], high frequency [HF], and LF/HF), and the short-and long-term fractal exponents were obtained by detrended fluctuation analysis. We considered P < 0.05 to be a significant difference.Results: COPD patients presented reduced levels of all linear exponents and decreased short-term fractal exponent (alpha-1: 0.899 +/- 0.18 versus 1.025 +/- 0.09, P = 0.026). There was no significant difference between COPD and control groups in alpha-2 and alpha-1/alpha-2 ratio.Conclusion: COPD subjects present reduced short-term fractal correlation properties of HRV, which indicates that this index can be used for risk stratification, assessment of systemic disease manifestations, and therapeutic procedures to monitor those patients.Fundação para o Desenvolvimento da UNESP (FUNDUNESP)Univ Estadual Paulista, Fac Ciencias & Tecnol, Dept Fisioterapia, São Paulo, BrazilUniversidade Federal de São Paulo, Disciplina Cardiol, Dept Med, São Paulo, BrazilFac Med Sao Jose do Rio Preto, Dept Cardiol & Cirurgia Cardiovasc, São Paulo, BrazilUniv Estadual Londrina, Lab Pesquisa Fisioterapia Pulm, Dept Fisioterapia, Londrina, BrazilFac Med ABC, Dept Morfol & Fisiol, Santo Andre, BrazilUniversidade Federal de São Paulo, Disciplina Cardiol, Dept Med, São Paulo, BrazilFUNDUNESP: 00704/08 - DFPWeb of Scienc

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

Analysis of long-term heart rate variability using Labview

Long-term heart rate variability measurement is important in understanding the activities of the autonomic nervous system. Many methods and implications of long-terrn HRV were available in the literature. The first two studies focused on two data analysis techniques that were used on a normal subject. The first study focused on the 1/f fluctuations. For this analysis, three 1 1h heart rate variability data sets were collected with the Polar Vantage NV. A LabVIEW program was used to calculate the power spectrum of the heart rate, and then the 1/f line was calculated by taking the log of the power spectrum versus the log of the frequency. The second study focused on the 24h circadian rhythm characteristics from the low frequency and high frequency portions of the HRV spectrum. The same watch was used to collect three 22h heart rate variability data sets. The 22h data sets were divided in to 15min segments. The same computer algorithm was used to calculate the low and high frequency portions of the power spectrum. The plot of the low frequency and high frequency versus time was determined. The third study focused on the non-linear dynamics of the HRV. For this analysis, fifteen long-term ECG data sets were collected with the Polar NV watch from 2 cardiac patients and 3 healthy subjects. 1h interval was obtained from each data set, and each data set was analyzed using the Benoit 1.1 R/S analysis program and the LabVIEW standard deviation program. The results showed that in normal subjects at rest, the 1/f fluctuation was observed and the 2-hour circadian rhythm was present. The non-linear dynamics of HRV was useful in separating the healthy from the cardiac patients

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

Impedance Pneumography for the Nocturnal Assessment of Lower Airway Obstruction

Tidal breathing analysis is a lung function technique suggested for infants and children who are unable to cooperate with forced spirometry. This technique aims to quantify lower airway obstruction from average changes in the shape or the breath-to-breath variations of the tidal breathing flow-volume loop (TBFV) profiles. If tidal airflow is recorded with a mouth pneumotachograph (PNT), tidal breathing analysis finds the same limitations as other alternatives to spirometry. These are typically the need for sedation and the assessment of lung function only for sort times at the hospital. Recent improvements in impedance pneumography (IP) enable for the first time the continuous non-invasive monitoring of respiratory airflow overnight. This can improve the analysis of tidal breathing by capturing circadian and nocturnal worsening in lower airway obstruction. However, due to the lack of previous methods recording nocturnal airflow, little is known about how the interaction of sleep physiology and lower airway obstruction is reflected in the shape and variability of tidal breathing. This thesis reviews the literature regarding shape and variability analysis of tidal breathing during lower airway obstruction, sleep, or maturation. The thesis also extends this knowledge by presenting four original publications. The first publication describes a technical improvement in the IP method. The other three study the nocturnal TBFV’s shape in wheezing infants and children, and the nocturnal TBFV’s variability in healthy children. Both the literature and the results agree that for the TBFVs’ shape, increasing lower air- way obstruction advances the peak of expired flow and turns the middle part from convex to concave. However, these changes occur at a different degree of obstruction for differ- ent subjects depending on the compensation strategy that they have chosen. In infants, changes putatively occur at a higher degree of obstruction because most of the expiration is controlled by the respiratory musculature. During rapid eye movement (REM) sleep, changes putatively occur at a lower degree of obstruction because muscle atony limits the compensation strategies. For the variability of TBFVs, increasing lower airway obstruction decreases the variability in the early part of expiration in the long term (the whole night). However, the short-term variability is dominated by the stage-dependent variations in the respiratory drive. The thesis concludes that, at the present, tidal breathing analysis can estimate lower airway obstruction but cannot quantify its degree with accuracy. However, nocturnal IP recordings are easy to conduct and can serve as a first-line diagnosis or for the monitoring of disease progression. Nonetheless, future improvements in signal processing and the understanding of the tidal airflow signal can easily increase the accuracy and find new applications

Multivariate multiscale complexity analysis

Established dynamical complexity analysis measures operate at a single scale and thus fail to quantify inherent long-range correlations in real world data, a key feature of complex systems. They are designed for scalar time series, however, multivariate observations are common in modern real world scenarios and their simultaneous analysis is a prerequisite for the understanding of the underlying signal generating model. To that end, this thesis first introduces a notion of multivariate sample entropy and thus extends the current univariate complexity analysis to the multivariate case. The proposed multivariate multiscale entropy (MMSE) algorithm is shown to be capable of addressing the dynamical complexity of such data directly in the domain where they reside, and at multiple temporal scales, thus making full use of all the available information, both within and across the multiple data channels. Next, the intrinsic multivariate scales of the input data are generated adaptively via the multivariate empirical mode decomposition (MEMD) algorithm. This allows for both generating comparable scales from multiple data channels, and for temporal scales of same length as the length of input signal, thus, removing the critical limitation on input data length in current complexity analysis methods. The resulting MEMD-enhanced MMSE method is also shown to be suitable for non-stationary multivariate data analysis owing to the data-driven nature of MEMD algorithm, as non-stationarity is the biggest obstacle for meaningful complexity analysis. This thesis presents a quantum step forward in this area, by introducing robust and physically meaningful complexity estimates of real-world systems, which are typically multivariate, finite in duration, and of noisy and heterogeneous natures. This also allows us to gain better understanding of the complexity of the underlying multivariate model and more degrees of freedom and rigor in the analysis. Simulations on both synthetic and real world multivariate data sets support the analysis