Risk evaluation of diabetes mellitus by relation of chaotic globals to HRV

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Investigation on Heart Rate Variability (HRV) and psycho-social-affective adjustment in Italian women with ovarian cancer

INTRODUZIONE: L’integrazione mente-corpo applicata ad un ambito patologico predominante in questi tempi, come il cancro, è il nucleo di questa tesi. Il background teorico entro cui è inserita, è quello della Psiconeuroendocrinoimmunologia (Bottaccioli, 1995) e Psico-Oncologia. Sono state identificate, nella letteratura scientifica, le connessioni tra stati psicologici (mente) e condizioni fisiologiche (corpo). Le variabili emerse come potenzialmente protettive in pazienti che si trovano ad affrontare il cancro sono: il supporto sociale, l’immagine corporea, il coping e la Qualità della Vita, insieme all’indice fisiologico Heart Rate Variability (HRV; Shaffer & Venner, 2013). Il potenziale meccanismo della connessione tra queste variabili potrebbe essere spiegato dall’azione del Nervo Vago, come esposto nella Teoria Polivagale di Stephen Porges (2007; 2009). OBIETTIVI: Gli obiettivi principali di questo studio sono: 1. Valutare l’adattamento psicologico alla patologia in termini di supporto sociale percepito, immagine corporea, coping prevalente e qualità della vita in donne con cancro ovarico; 2. Valutare i valori di base HRV in queste donne; 3. Osservare se livelli più elevati di HRV sono associati ad un migliore adattamento psicologico alla patologia; 4. Osservare se una peggiore percezione dell’immagine corporea e l’utilizzo di strategie di coping disadattive sono associate ad una Qualità della Vita più scarsa. METODO: 38 donne affette da cancro ovarico, al momento della valutazione libere da patologia, sono state reclutate presso la clinica oncologica del reparto di Ginecologia dell’Azienda Ospedaliero-Universitaria di Parma, Italia. Ad ogni partecipante è stato chiesto di compilare una batteria di test composta da: MSPSS, per la valutazione del supporto sociale percepito; DAS-59, per la valutazione dell’immagine corporea; MAC, per la valutazione delle strategie di coping prevalenti utilizzate verso il cancro; EORTC-QLQ30, per la valutazione della Qualità della Vita. Per ogni partecipante è stato registrato HRV di base utilizzando lo strumento emWave (HeartMath). RISULTATI PRINCIPALI: Rispondendo agli obiettivi 1 e 2, in queste donne si è rilevato una alto tasso di supporto sociale percepito, in particolare ricevuto dalla persona di riferimento. L’area rivelatasi più critica nel supporto sociale è quella degli amici. Per quanto riguarda l’immagine corporea, la porzione di campione dai 30 ai 61 anni, ha delle preoccupazioni globali legate all’immagine corporea paragonabili ai dati provenienti dalla popolazione generale con preoccupazioni riguardo l’aspetto corporeo. Invece, nella porzione di campione dai 61 anni in su, il pattern di disagio verso l’aspetto fisico sembra decisamente peggiorare. Inoltre, in questo campione, si è rilevato un disagio globale verso l’immagine corporea significativamente più alto rispetto ai valori normativi presenti in letteratura riferiti a donne con cancro al seno con o senza mastectomia (rispettivamente t(94)= -4.78; p<0.000001; t(110)= -6.81;p<0.000001). La strategia di coping più utilizzata da queste donne è lo spirito combattivo, seguito dal fatalismo. Questo campione riporta, inoltre, una Qualità della Vita complessivamente soddisfacente, con un buon livello di funzionamento sociale. L’area di funzionalità più critica risulta essere il funzionamento emotivo. Considerando i sintomi prevalenti, i più riferiti sono affaticamento, disturbi del sonno e dolore. Per definire, invece, il pattern HRV, sono stati confrontati i dati del campione con quelli presenti in letteratura, riguardanti donne con cancro ovarico. Il campione valutato in questo studio, ha un HRV SDNN (Me=28.2ms) significativamente più alto dell’altro gruppo. Tuttavia, confrontando il valore medio di questo campione con i dati normativi sulla popolazione sana (Me=50ms), i nostri valori risultano drasticamente più bassi. In ultimo, donne che hanno ricevuto diagnosi di cancro ovarico in età fertile, sembrano avere maggiore HRV, migliore funzionamento emotivo e minore sintomatologia rispetto alle donne che hanno ricevuto diagnosi non in età fertile. Focalizzando l’attenzione sulla ricerca di relazioni significative tra le variabili in esame (obiettivo 3 e 4) sono state trovate numerose correlazioni significative tra: l’età e HRV, supporto percepito , Qualità della Vita; Qualità della Vita e immagine corporea, supporto sociale, strategie di coping; strategie di coping e immagine corporea, supporto sociale; immagine corporea e supporto sociale; HRV e supporto sociale, Qualità della Vita. Per verificare la possibile connessione causale tra le variabili considerate, sono state applicate regressioni lineari semplici e multiple per verificare la bontà del modello teorico. Si è rilevato che HRV è significativamente positivamente influenzata dal supporto percepito dalla figura di riferimento, dal funzionamento di ruolo, dall’immagine corporea totale. Invece risulta negativamente influenzata dal supporto percepito dagli amici e dall’uso di strategie di coping evitanti . La qualità della vita è positivamente influenzata da: l’immagine corporea globale e l’utilizzo del fatalismo come strategia di coping prevalente. Il funzionamento emotivo è influenzato dal supporto percepito dalla figura di riferimento e dal fatalismo. DISCUSSIONI E CONCLUSIONI: Il campione Italiano valutato, sembra essere a metà strada nell’adattamento dello stato psicologico e dell’equilibrio neurovegetativo al cancro. Sicuramente queste donne vivono una vita accettabile, in quanto sopravvissute al cancro, ma sembra anche che portino con sé preoccupazioni e difficoltà, in particolare legate all’accettazione della loro condizione di sopravvissute. Infatti, il migliore adattamento si riscontra nelle donne che hanno avuto peggiori condizioni in partenza: stadio del cancro avanzato, più giovani, con diagnosi ricevuta in età fertile. Pertanto, è possibile suggerire che queste condizioni critiche forzino queste donne ad affrontare apertamente il cancro e la loro situazione di sopravvissute al cancro, portandole ad “andare avanti” piuttosto che “tornare indietro”. Facendo riferimento alle connessioni tra variabili psicologiche e fisiologiche in queste donne, si è evidenziato che HRV è influenzata dalla presenza di figure significative ma, in particolare, è presumibile che sia influenzata da un’appropriata condivisione emotiva con queste figure. Si è anche evidenziato che poter continuare ad essere efficaci nel proprio contesto personale si riflette in un maggiore HRV, probabilmente in quanto permette di preservare il senso di sé, riducendo in questo modo lo stress derivante dall’esperienza cancro. Pertanto, HRV in queste donne risulta associato con un migliore adattamento psicologico. Inoltre, si è evidenziato che in queste donne la Qualità della Vita è profondamente influenzata dalla percezione dell’immagine corporea. Si tratta di un aspetto innovativo che è stato rilevato in questo campione e che, invece, nei precedenti studi non è stato indagato. In ultimo, la strategia di coping fatalismo sembra essere protettiva e sembra facilitare il processo di accettazione del cancro. Si spera sinceramente che le ricerche future possano superare i limiti del presente studio, come la scarsa numerosità e l’uso di strumenti di valutazione che, per alcuni aspetti come la scala Evitamento nel MAC, non centrano totalmente il target di indagine. Le traiettorie future di questo studio sono: aumentare il numero di osservazioni, reclutando donne in diversi centri specialistici in diverse zone d’Italia; utilizzare strumenti più specifici per valutare i costrutti in esame; valutare se un intervento di supporto centrato sul miglioramento di HRV (come HRV Biofeedback) può avere una ricaduta positiva sull’adattamento emotivo e la Qualità della Vita.

Impact of artifact correction methods on R-R interbeat signals to quantifying heart rate variability (HRV) according to linear and nonlinear methods.

In the analysis of heart rate variability (HRV) are used temporal series that contains the distances between successive heartbeats in order to assess autonomic regulation of the cardiovascular system. These series are obtained from the electrocardiogram (ECG) signal analysis, which can be affected by different types of artifacts leading to incorrect interpretations in the analysis of the HRV signals. Classic approach to deal with these artifacts implies the use of correction methods, some of them based on interpolation, substitution or statistical techniques. However, there are few studies that shows the accuracy and performance of these correction methods on real HRV signals. This study aims to determine the performance of some linear and non-linear correction methods on HRV signals with induced artefacts by quantification of its linear and nonlinear HRV parameters. As part of the methodology, ECG signals of rats measured using the technique of telemetry were used to generate real heart rate variability signals without any error. In these series were simulated missing points (beats) in different quantities in order to emulate a real experimental situation as accurately as possible. In order to compare recovering efficiency, deletion (DEL), linear interpolation (LI), cubic spline interpolation (CI), moving average window (MAW) and nonlinear predictive interpolation (NPI) were used as correction methods for the series with induced artifacts. The accuracy of each correction method was known through the results obtained after the measurement of the mean value of the series (AVNN), standard deviation (SDNN), root mean square error of the differences between successive heartbeats (RMSSD), Lomb\'s periodogram (LSP), Detrended Fluctuation Analysis (DFA), multiscale entropy (MSE) and symbolic dynamics (SD) on each HRV signal with and without artifacts. The results show that, at low levels of missing points the performance of all correction techniques are very similar with very close values for each HRV parameter. However, at higher levels of losses only the NPI method allows to obtain HRV parameters with low error values and low quantity of significant differences in comparison to the values calculated for the same signals without the presence of missing points.

An evaluation of Landsat Thematoc Mapper (TM) and spot- HRV data, in grasslands inventory in the UK

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Cardiac state diagnosis using higher order spectra of heart rate variability

Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart, by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability analysis is an important tool to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computer-based intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Like many bio-signals, HRV signals are nonlinear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of nonlinear systems and provides good noise immunity. In this work, we studied the HOS of the HRV signals of normal heartbeat and seven classes of arrhythmia. We present some general characteristics for each of these classes of HRV signals in the bispectrum and bicoherence plots. We also extracted features from the HOS and performed an analysis of variance (ANOVA) test. The results are very promising for cardiac arrhythmia classification with a number of features yielding a p-value < 0.02 in the ANOVA test.

Molecular epidemiology of respiratory viruses in a paediatric cohort from Indonesia

Acute lower respiratory tract infections (ALRTIs) are a common cause of morbidity and mortality among children under 5 years of age and are found worldwide, with pneumonia as the most severe manifestation. Although the incidence of severe disease varies both between individuals and countries, there is still no clear understanding of what causes this variation. Studies of community-acquired pneumonia (CAP) have traditionally not focused on viral causes of disease due to a paucity of diagnostic tools. However, with the emergence of molecular techniques, it is now known that viruses outnumber bacteria as the etiological agents of childhood CAP, especially in children under 2 years of age. The main objective of this study was to investigate viruses contributing to disease severity in cases of childhood ALRTI, using a two year cohort study following 2014 infants and children enrolled in Bandung, Indonesia. A total of 352 nasopharyngeal washes collected from 256 paediatric ALRTI patients were used for analysis. A subset of samples was screened using a novel microarray pathogen detection method that identified respiratory syncytial virus (RSV), human metapneumovirus (hMPV) and human rhinovirus (HRV) in the samples. Real-time RT-PCR was used both for confirming and quantifying viruses found in the nasopharyngeal samples. Viral copy numbers were determined and normalised to the numbers of human cells collected with the use of 18S rRNA. Molecular epidemiology was performed for RSV A and hMPV using sequences to the glycoprotein gene and nucleoprotein gene respectively, to determine genotypes circulating in this Indonesian paediatric cohort. This study found that HRV (119/352; 33.8%) was the most common virus detected as the cause of respiratory tract infections in this cohort, followed by the viral pathogens RSV A (73/352; 20.7%), hMPV (30/352; 8.5%) and RSV B (12/352; 3.4%). Co-infections of more than two viruses were detected in 31 episodes (defined as an infection which occurred more than two weeks apart), accounting for 8.8% of the 352 samples tested or 15.4% of the 201 episodes with at least one virus detected. RSV A genotypes circulating in this population were predominantly GA2, GA5 and GA7, while hMPV genotypes circulating were mainly A2a (27/30; 90.0%), B2 (2/30; 6.7%) and A1 (1/30; 3.3%). This study found no evidence of disease severity associated either with a specific virus or viral strain, or with viral load. However, this study did find a significant association with co-infection of RSV A and HRV with severe disease (P = 0.006), suggesting that this may be a novel cause of severe disease.

Analysis of cardiac and epileptic signals using higher order spectra

The theory of nonlinear dyamic systems provides some new methods to handle complex systems. Chaos theory offers new concepts, algorithms and methods for processing, enhancing and analyzing the measured signals. In recent years, researchers are applying the concepts from this theory to bio-signal analysis. In this work, the complex dynamics of the bio-signals such as electrocardiogram (ECG) and electroencephalogram (EEG) are analyzed using the tools of nonlinear systems theory. In the modern industrialized countries every year several hundred thousands of people die due to sudden cardiac death. The Electrocardiogram (ECG) is an important biosignal representing the sum total of millions of cardiac cell depolarization potentials. It contains important insight into the state of health and nature of the disease afflicting the heart. Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability analysis is an important tool to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computerbased intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Like many bio-signals, HRV signals are non-linear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of non-linear systems and provides good noise immunity. In this work, we studied the HOS of the HRV signals of normal heartbeat and four classes of arrhythmia. This thesis presents some general characteristics for each of these classes of HRV signals in the bispectrum and bicoherence plots. Several features were extracted from the HOS and subjected an Analysis of Variance (ANOVA) test. The results are very promising for cardiac arrhythmia classification with a number of features yielding a p-value < 0.02 in the ANOVA test. An automated intelligent system for the identification of cardiac health is very useful in healthcare technology. In this work, seven features were extracted from the heart rate signals using HOS and fed to a support vector machine (SVM) for classification. The performance evaluation protocol in this thesis uses 330 subjects consisting of five different kinds of cardiac disease conditions. The classifier achieved a sensitivity of 90% and a specificity of 89%. This system is ready to run on larger data sets. In EEG analysis, the search for hidden information for identification of seizures has a long history. Epilepsy is a pathological condition characterized by spontaneous and unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. An automatic early detection of the seizure onsets would help the patients and observers to take appropriate precautions. Various methods have been proposed to predict the onset of seizures based on EEG recordings. The use of nonlinear features motivated by the higher order spectra (HOS) has been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, these features are used to train both a Gaussian mixture model (GMM) classifier and a Support Vector Machine (SVM) classifier. Results show that the classifiers were able to achieve 93.11% and 92.67% classification accuracy, respectively, with selected HOS based features. About 2 hours of EEG recordings from 10 patients were used in this study. This thesis introduces unique bispectrum and bicoherence plots for various cardiac conditions and for normal, background and epileptic EEG signals. These plots reveal distinct patterns. The patterns are useful for visual interpretation by those without a deep understanding of spectral analysis such as medical practitioners. It includes original contributions in extracting features from HRV and EEG signals using HOS and entropy, in analyzing the statistical properties of such features on real data and in automated classification using these features with GMM and SVM classifiers.

Application of higher order statistics/spectra in biomedical signals : a review

For many decades correlation and power spectrum have been primary tools for digital signal processing applications in the biomedical area. The information contained in the power spectrum is essentially that of the autocorrelation sequence; which is sufficient for complete statistical descriptions of Gaussian signals of known means. However, there are practical situations where one needs to look beyond autocorrelation of a signal to extract information regarding deviation from Gaussianity and the presence of phase relations. Higher order spectra, also known as polyspectra, are spectral representations of higher order statistics, i.e. moments and cumulants of third order and beyond. HOS (higher order statistics or higher order spectra) can detect deviations from linearity, stationarity or Gaussianity in the signal. Most of the biomedical signals are non-linear, non-stationary and non-Gaussian in nature and therefore it can be more advantageous to analyze them with HOS compared to the use of second order correlations and power spectra. In this paper we have discussed the application of HOS for different bio-signals. HOS methods of analysis are explained using a typical heart rate variability (HRV) signal and applications to other signals are reviewed.

Cardiac health diagnosis using higher order spectra and support vector machine

The Electrocardiogram (ECG) is an important bio-signal representing the sum total of millions of cardiac cell depolarization potentials. It contains important insight into the state of health and nature of the disease afflicting the heart. Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. The HRV signal can be used as a base signal to observe the heart's functioning. These signals are non-linear and non-stationary in nature. So, higher order spectral (HOS) analysis, which is more suitable for non-linear systems and is robust to noise, was used. An automated intelligent system for the identification of cardiac health is very useful in healthcare technology. In this work, we have extracted seven features from the heart rate signals using HOS and fed them to a support vector machine (SVM) for classification. Our performance evaluation protocol uses 330 subjects consisting of five different kinds of cardiac disease conditions. We demonstrate a sensitivity of 90% for the classifier with a specificity of 87.93%. Our system is ready to run on larger data sets.

Behavioural evidence for social preferences

A central topic in economics is the existence of social preferences. Behavioural economics in general has approached the issue from several angles. Controlled experimental settings, surveys, and field experiments are able to show that in a number of economic environments, people usually care about immaterial things such as fairness or equity of allocations. Findings from experimental economics specifically have lead to large increase in theories addressing social preferences. Most (pro)social phenomena are well understood in the experimental settings but very difficult to observe 'in the wild'. One criticism in this regard is that many findings are bound by the artificial environment of the computer lab or survey method used. A further criticism is that the traditional methods also fail to directly attribute the observed behaviour to the mental constructs that are expected to stand behind them. This thesis will first examine the usefulness of sports data to test social preference models in a field environment, thus overcoming limitations of the lab with regards to applicability to other - non-artificial - environments. The second major contribution of this research establishes a new neuroscientific tool - the measurement of the heart rate variability - to observe participants' emotional reactions in a traditional experimental setup.

Consecutive days of cold water immersion: effects on cycling performance and heart rate variability.

We investigated performance and heart rate (HR) variability (HRV) over consecutive days of cycling with post-exercise cold water immersion (CWI) or passive recovery (PAS). In a crossover design, 11 cyclists completed two separate 3-day training blocks (120 min cycling per day, 66 maximal sprints, 9 min time trialling [TT]), followed by 2 days of recovery-based training. The cyclists recovered from each training session by standing in cold water (10 °C) or at room temperature (27 °C) for 5 min. Mean power for sprints, total TT work and HR were assessed during each session. Resting vagal-HRV (natural logarithm of square-root of mean squared differences of successive R-R intervals; ln rMSSD) was assessed after exercise, after the recovery intervention, during sleep and upon waking. CWI allowed better maintenance of mean sprint power (between-trial difference [90 % confidence limits] +12.4 % [5.9; 18.9]), cadence (+2.0 % [0.6; 3.5]), and mean HR during exercise (+1.6 % [0.0; 3.2]) compared with PAS. ln rMSSD immediately following CWI was higher (+144 % [92; 211]) compared with PAS. There was no difference between the trials in TT performance (-0.2 % [-3.5; 3.0]) or waking ln rMSSD (-1.2 % [-5.9; 3.4]). CWI helps to maintain sprint performance during consecutive days of training, whereas its effects on vagal-HRV vary over time and depend on prior exercise intensity.

Heart beats and economic decisions : Observing mental stress in the ultimatum bargaining game

One aim of experimental economics is to try to better understand human economic decision making. Early research of the ultimatum bargaining game (Gueth et al., 1982) revealed that other motives than pure monetary reward play a role. Neuroeconomic research has introduced the recording of physiological observations as signals of emotional responses. In this study, we apply heart rate variability (HRV) measuring technology to explore the behaviour and physiological reactions of proposers and responders in the ultimatum bargaining game. Since this technology is small and non-intrusive, we are able to run the experiment in a standard experimental economic setup. We show that low o�ers by a proposer cause signs of mental stress in both the proposer and the responder, as both exhibit high ratios of low to high frequency activity in the HRV spectrum.

The role of psychological and physiological factors in decision making under risk and in a dilemma

We study the difference in the result of two different risk elicitation methods by linking estimates of risk attitudes to gender, age, personality traits, a decision in a dilemma situation, and physiological states measured by heart rate variability (HRV). Our results indicate that differences between the methods can partly be explained by gender, but not by personality traits. Furthermore, HRV is linked to risktaking in the experiment for at least one of the methods, indicating that more stressed individuals display more risk aversion. Finally, we and that risk attitudes are not predictive of the ability to decide in a dilemma, but personality traits are. Surprisingly, there is also no apparent relationship between the physiological state during the dilemma situation and the ability to make a decision.

Positive reappraisal improves heart rate variability during stressful work

Recent developments in wearable ECG technology have seen renewed interest in the use of Heart Rate Variability (HRV) feedback for stress management. Yet, little is know about the efficacy of such interventions. Positive reappraisal is an emotion regulation strategy that involves changing the way a situation is construed to decrease emotional impact. We sought to test the effectiveness of an intervention that used feedback on HRV data to prompt positive reappraisal during a stressful work task. Participants (N=122) completed two 20-minute trials of an inbox activity. In-between the first and the second trial participants were assigned to the waitlist control condition, a positive reappraisal via psycho-education condition, or a positive reappraisal via HRV feedback condition. Results revealed that using HRV data to frame a positive reappraisal message is more effective than using psycho-education (or no intervention)–especially for increasing positive mood and reducing arousal.

Regulatory focus moderates the relationship between task control and physiological and psychological markers of stress : a work simulation study

This experiment examined whether trait regulatory focus moderates the effects of task control on stress reactions during a demanding work simulation. Regulatory focus describes two ways in which individuals self-regulate toward desired goals: promotion and prevention. As highly promotion-focused individuals are oriented toward growth and challenge, it was expected that they would show better adaptation to demanding work under high task control. In contrast, as highly prevention-focused individuals are oriented toward safety and responsibility they were expected to show better adaptation under low task control. Participants (N = 110) completed a measure of trait regulatory focus and then three trials of a demanding inbox activity under either low, neutral, or high task control. Heart rate variability (HRV), affective reactions (anxiety & task dissatisfaction), and task performance were measured at each trial. As predicted, highly promotion-focused individuals found high (compared to neutral) task control stress-buffering for performance. Moreover, highly prevention-focused individuals found high (compared to low) task control stress-exacerbating for dissatisfaction. In addition, highly prevention-focused individuals found low task control stress-buffering for dissatisfaction, performance, and HRV. However, these effects of low task control for highly prevention-focused individuals depended on their promotion focus.