Complex systems and the technology of variability analysis

Characteristic patterns of variation over time, namely rhythms, represent a defining feature of complex systems, one that is synonymous with life. Despite the intrinsic dynamic, interdependent and nonlinear relationships of their parts, complex biological systems exhibit robust systemic stability. Applied to critical care, it is the systemic properties of the host response to a physiological insult that manifest as health or illness and determine outcome in our patients. Variability analysis provides a novel technology with which to evaluate the overall properties of a complex system. This review highlights the means by which we scientifically measure variation, including analyses of overall variation (time domain analysis, frequency distribution, spectral power), frequency contribution (spectral analysis), scale invariant (fractal) behaviour (detrended fluctuation and power law analysis) and regularity (approximate and multiscale entropy). Each technique is presented with a definition, interpretation, clinical application, advantages, limitations and summary of its calculation. The ubiquitous association between altered variability and illness is highlighted, followed by an analysis of how variability analysis may significantly improve prognostication of severity of illness and guide therapeutic intervention in critically ill patients

Detection of subjects with ischemic heart disease by using machine learning technique based on heart rate total variability parameters

OBJECTIVE: Ischemic heart disease (IHD), in its chronic stable form, is a subtle pathology due to its silent behavior before developing in unstable angina, myocardial infarction or sudden cardiac death. The clinical assessment is based on typical symptoms and finally confirmed, invasively, by coronary angiography. Recently, heart rate variability (HRV) analysis as well as some machine learning algorithms like Artificial Neural Networks (ANNs) were used to identify cardiovascular arrhythmias and, only in few cases, to classify IHD segments in a limited number of subjects. The goal of this study was the identification of the ANN structure and the HRV parameters producing the best performance to identify IHD patients in a non-invasive way, validating the results on a large sample of subjects. Moreover, we examined the influence of a clinical non-invasive parameter, the left ventricular ejection fraction (LVEF), on the classification performance.APPROACH: To this aim, we extracted several linear and non-linear parameters from 24h RR signal, considering both normal and ectopic beats (Heart Rate Total Variability), of 251 normal and 245 IHD subjects, matched by age and gender. ANNs using several different combinations of these parameters together with age and gender were tested. For each ANN, we varied the number of hidden neurons from 2 to 7 and simulated 100 times changing randomly training and test dataset.MAIN RESULTS: The HRTV parameters showed significant greater variability in IHD than in normal subjects. The ANN applied to meanRR, LF, LF/HF, Beta exponent, SD2 together with age and gender reached a maximum accuracy of 71.8% and, by adding as input LVEF, an accuracy of 79.8%.SIGNIFICANCE: The study provides a deep insight into how a combination of some HRTV parameters and LVEF could be exploited to reliably detect the presence of subjects affected by IHD

Sleep-time predictors of cardiovascular complications in surgical peripheral arterial disease

ABSTRACT Patients with peripheral arterial disease (PAD) undergoing surgical revascularisation are in high risk of postoperative cardiovascular complications and death, due to advancing age and multiple comorbidities in the population. In addition, PAD needing surgery represents a severe form of systemic atherosclerosis but the exact underlying pathophysiology of acute myocardial infarction (AMI) in these patients is unclear and predicting outcome especially in the long-term is challenging. Obstructive sleep apnoea (OSA) is increasingly common in the general population and independently associated with various manifestations of cardiovascular disease or their risk factors; OSA is highly prevalent in patients with coronary artery disease (CAD), stroke, hypertension and diabetes. To expand this knowledge, we determined the prevalence and severity (in terms of the apnoeahypopnoea index, AHI) of OSA in surgical PAD as well as its impact on the incidence of major adverse cardiovascular and cerebrovascular events (MACCE) in this patient group. Heart rate variability (HRV) reflects fluctuations in sympathetic and parasympathetic activation responsible for neurocirculatory control in various physiological and pathophysiological situations. Depressed HRV is associated with increased cardiovascular morbidity and mortality following AMI and major surgery. In this study, the alterations of nocturnal HRV and their association with the severity of OSA and incidence of MACCE in patients with PAD was assessed, including the fractal correlation properties of HRV. HRV in a control group of 15 healthy subjects was also examined. Patients scheduled for sub-inguinal vascular surgery (n=84, age 67±9 years) underwent polysomnography and HRV analyses. OSA was detected in 86% of patients and in 56% it was moderate or severe. Age, male gender, depressed left ventricular function and decreasing high density lipoprotein/cholesterol ratio (HDL/Chol) predicted the presence and severity of OSA. The latter two remained significant after adjusting for age and gender. OSA with AHI ≥20/hour, used as a cut-off in the outcome analyses, predicted a higher risk of MACCE (p=0.001) along with pre-existing CAD (p=0.001), decreasing HDL/Chol (p=0.048) and <4 years history of PAD (p=0.018). HRV was altered in patients with PAD when compared to controls but the time domain measures were mostly unchanged. In the frequency domain, low frequency power was generally lower, high frequency power was mostly higher and fractal correlation was consistently lower. Very low frequency power was increased the most in patients with AHI 10-20/hour when compared to <10/hour while those with AHI ≥20/hour had lower fractal correlation in the morning. Patients suffering a MACCE had lower high frequency power during S3-4 and rapid eye movement sleep. In conclusion, OSA is associated with worsening atherosclerosis and predicts MACCE after vascular surgery. HRV alterations, although associated with PAD, have limited predictive value. Keywords: atherosclerosis, peripheral arterial disease, sleep apnoea, heart rate variabilityTIIVISTELMÄ Unenaikaiset sydänkomplikaatioiden ennustetekijät kirurgista hoitoa vaativassa perifeerisessä valtimotaudissa Perifeeristä valtimotautia sairastavilla potilailla on suuri leikkauksenjälkeisten sydänkomplikaatioiden riski johtuen yhä iäkkäämmästä väestöstä sekä lukuisista rinnakkaissairauksista. Lisäksi perifeerinen valtimotauti merkitsee vaikea-asteista yleistynyttä ateroskleroosia, mutta sydäninfarktin tarkka syntymekanismi näillä potilailla on epäselvä ja erityisesti pitkän aikavälin ennusteen arviointi on haastavaa. Obstruktiivinen uniapnea yleistyy väestössä ja sillä on itsenäinen yhteys useisiin sydän- ja verisuonisairauksiin ja niiden riskitekijöihin; uniapnea on erittäin yleinen sepelvaltimotauti-, aivohalvaus-, verenpainetauti- ja diabetespotilailla. Tämän tietopohjan laajentamiseksi tässä tutkimuksessa määritettiin uniapnean esiintyvyys ja vaikeusaste (määrittäjänä apnea-hypopneaindeksi, AHI) vaikea-asteista yleistynyttä ateroskleroosia sairastavilla potilailla sekä sen vaikutus vakavien sydän- ja aivotapahtumien ilmaantuvuuteen. Sydämen sykevaihtelu kuvastaa autonomisen hermoston toiminnan muutoksia, jotka puolestaan vastaavat verenkierron säätelystä erilaisissa fysiologisissa ja patofysiologisissa tilanteissa. Alentunut sykevaihtelu on yhteydessä lisääntyneeseen kardiovaskulaariseen sairastuvuuteen ja kuolleisuuteen sairastetun sydäninfarktin tai suuren leikkauksen jälkeen. Tässä tutkimuksessa arvioitiin yöllisen sydämen sykevaihtelun muutosten yhteyttä uniapnean vaikeusasteeseen sekä vakavien sydän- ja aivotapahtumien ilmaantuvuuteen, mukaan lukien sykevaihtelun fraktaalikorrelaatio-ominaisuudet. Tutkimuksessa analysoitiin sykevaihtelu myös 15 terveen henkilön vertailuryhmältä. Nivustason alapuoliseen verisuonileikkaukseen meneville potilaille (n=84, ikä 67±9 vuotta) tehtiin unipolygrafia ja sykevaihteluanalyysi. Uniapnea todettiin 86 %:lla potilaista ja 56 %:lla se oli kohtalainen tai vaikea. Ikä, miessukupuoli, heikentynyt vasemman kammion toiminta ja alentunut HDL-kolesterolin suhde kokonaiskolesteroliin ennustivat uniapneaa ja sen vaikeutumista; 2 viimeksi mainittua säilyivät merkitsevinä ikä- ja sukupuolivakioinnin jälkeen. AHI ≥20/tunti, joka valittiin kynnysarvoksi päätetapahtumaanalyyseihin, ennusti merkitsevästi vakavia sydän- ja aivotapahtumia (p=0.001). Muita merkitseviä tekijöitä olivat sepelvaltimotauti (p=0.001), alentunut HDL-suhde (p=0.048) ja lyhyt (alle 4 vuotta) perifeerisen valtimotaudin kesto ennen leikkaushoidon tarvetta (p=0.018). Sykevaihtelu oli muuttunut valtimotautipotilailla verrattuna kontrolleihin, mutta aikakenttäparametrit säilyivät lähes ennallaan. Pienitaajuuksinen sykevaihtelu oli yleisesti vähäisempää, suuritaajuuksinen enimmäkseen voimakkaampaa ja fraktaalikorrelaatio johdonmukaisesti heikompaa. Hyvin pienitaajuuksinen vaihtelu oli eniten lisääntynyt AHI 10-20/tunti -alaryhmässä verrattuna AHI <10/tunti -ryhmään, mutta AHI ≥20/tunti -potilailla aamun fraktaalikorrelaatio oli heikompaa. Potilaiden, jotka saivat vakavia sydän- ja aivotapahtumia, suuritaajuusvaihtelu oli heikompaa syvän unen ja vilkeunen aikana. Johtopäätöksinä todetaan, että uniapnea on yhteydessä vaikeutuvaan valtimotautiin sekä ennustaa vakavia sydän- ja aivotapahtumia verisuonileikkauksen jälkeen sykevaihtelun muutosten ennustearvon ollessa tässä aineistossa hyvin rajallinen. Avainsanat: ateroskleroosi, perifeerinen valtimotauti, uniapnea, sykevaihtel

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

Sudden death: Neurogenic causes, prediction and prevention

Sudden death is a major health problem all over the world. The most common causes of sudden death are cardiac but there are also other causes such as neurological conditions (stroke, epileptic attacks and brain trauma), drugs, catecholamine toxicity, etc. A common feature of all these diverse pathologies underlying sudden death is the imbalance of the autonomic nervous system control of the cardiovascular system. This paper reviews different pathologies underlying sudden death with emphasis on the autonomic nervous system contribution, possibilities of early diagnosis and prognosis of sudden death using various clinical markers including autonomic markers (heart rate variability and baroreflex sensitivity), present possibilities of management and promising prevention by electrical neuromodulation

Fractals analysis of cardiac arrhythmias

Heart rhythms are generated by complex self-regulating systems governed by the laws of chaos. Consequently, heart rhythms have fractal organization, characterized by self-similar dynamics with long-range order operating over multiple time scales. This allows for the self-organization and adaptability of heart rhythms under stress. Breakdown of this fractal organization into excessive order or uncorrelated randomness leads to a less-adaptable system, characteristic of aging and disease. With the tools of nonlinear dynamics, this fractal breakdown can be quantified with potential applications to diagnostic and prognostic clinical assessment. In this paper, I review the methodologies for fractal analysis of cardiac rhythms and the current literature on their applications in the clinical context. A brief overview of the basic mathematics of fractals is also included. Furthermore, I illustrate the usefulness of these powerful tools to clinical medicine by describing a novel noninvasive technique to monitor drug therapy in atrial fibrillation

Physiological time-series investigations of cardiovascular regulation in healthy young adults during physical exercise.

Physiological parameters may be recorded non-invasively to gain information on cardiovascular function which can then characterise populations with various pathologies. Physical exercise produces specific autonomic nervous system (ANS) changes. There has been no comprehensive profiling of cardiovascular function during exercise or simultaneous characterisation of the influence of exercise on cardiac ventricular function and electrical activity. This work aims to address that, using a combination of physiological parameters. Between-lead agreement for ambulatory electrocardiographic (EGG) depolarisation-repolarisation (QT) interval was quantified during rest and exercise. In contrast to cardiac interval (RR) data, between-lead bias and limits of agreement for QT interval data should be quantified when reporting results from an ambulatory EGG system and between-gender QT differences should also be accounted for. EGG electrode location appears to significantly affect QT-RR hysteresis, the shortening of the post-exercise QT interval relative to that at similar heart rates during exercise or pre-exercise rest, further emphasising the need for standardisation of EGG electrode placement. Sample entropy (SampEn) measures data complexity. Few studies have compared SampEn of RR data (SampEn-RR) during exercise, whilst none have examined SampEn for the corresponding QT interval (SampEn-QT). Fractal analysis assesses data correlation and scaling structures. Detrended fluctuation analysis (DFA) provides a scaling exponent (a) which describes these properties. This has not been quantified for RR interval data during post-exercise recovery and has not been reported for QT interval data. Differences in a magnitudes for RR and QT data suggest that these quantities have different fractal properties. Exercise perturbs the resting QT-RR relationship via hysteresis. The QT variability index (QTVI) quantifies the relative autonomic influence on the atrial and ventricular myocardium during rest and exercise. QTVI is a consistent measure of cardiac ventricular function and as such appears to be a more useful index than other parameters based on RR or QT interval alone

Review of Journal of Cardiovascular Magnetic Resonance 2015

There were 116 articles published in the Journal of Cardiovascular Magnetic Resonance (JCMR) in 2015, which is a 14 % increase on the 102 articles published in 2014. The quality of the submissions continues to increase. The 2015 JCMR Impact Factor (which is published in June 2016) rose to 5.75 from 4.72 for 2014 (as published in June 2015), which is the highest impact factor ever recorded for JCMR. The 2015 impact factor means that the JCMR papers that were published in 2013 and 2014 were cited on average 5.75 times in 2015. The impact factor undergoes natural variation according to citation rates of papers in the 2 years following publication, and is significantly influenced by highly cited papers such as official reports. However, the progress of the journal's impact over the last 5 years has been impressive. Our acceptance rate is <25 % and has been falling because the number of articles being submitted has been increasing. In accordance with Open-Access publishing, the JCMR articles go on-line as they are accepted with no collating of the articles into sections or special thematic issues. For this reason, the Editors have felt that it is useful once per calendar year to summarize the papers for the readership into broad areas of interest or theme, so that areas of interest can be reviewed in a single article in relation to each other and other recent JCMR articles. The papers are presented in broad themes and set in context with related literature and previously published JCMR papers to guide continuity of thought in the journal. We hope that you find the open-access system increases wider reading and citation of your papers, and that you will continue to send your quality papers to JCMR for publication