Applications of nonlinear dynamics to clinical cardiology

Analysis of Sinus Variability in a Group of Cameroonian Athletes

Background

Heart rate variability (HRV) analysis is a useful method for assessing the heart's ability to adapt to endogenous and exogenous loads. Data from African population on HRV are scarce and even more so in sports populations. This study aimed to compare cardiac autonomic modulation response in Cameroonian athletes and sedentary. Methodology. We conducted a prospective and analytical study in sports teams in the city of Yaoundé, Cameroon. The participants in our study were divided in three groups; people who practiced little or no sporting activity (sedentary as group 1) or who were regularly physically active as part of a sports team (footballers or handballers as second and third groups). They had to be aged 18 or over and have given their informed consent. Heart rate (HR) was continuously recorded at rest for ten minutes and then transferred to a computer equipped with Kubios HRV Standard software for analysis. Means ± mean standard errors were compared using the one-way ANOVA test, followed by Tukey's post-test. The significance threshold was set at 0.05.

Results

Of the 60 people selected to participate to our study, 75.0% were sportsmen (40.0% handball players and 35.0% footballers). The resting HR of sedentary people was higher (p < 0.001) than that of footballers and handball players. The SDNN, RMSSD, and pNN50 of sedentary people (16.22 ± 1.04; 9.97 ± 0.46; and 0.16 ± 0.06) were lower than those of footballers (30.13 ± 2.93; 20.61 ± 2.46; and 2.99 ± 0.63, with p < 0.001) and handball players (29.00 ± 1.86; 16.44 ± 1.16; and 2.15 ± 0.38, with p < 0.001 and p < 0.05 respectively). Absolute and relative very-low-frequency (VLF) power, absolute low and high-frequency (LF and HF) power, as well as total power (TP) were lower in sedentary people (3.66 ± 0.08 and 16.21 ± 0.64; 5.04 ± 0.15 and 2.50 ± 0.16 and 246.40 ± 18.04) compared to footballers (5.09 ± 0.24 and 26.87 ± 1.76; 5.85 ± 0.32 and 3.92 ± 0.22 and 836.10 ± 103.70, with p < 0.001, p < 0.01, and p < 0.001) and handball players (4.86 ± 0.16 and 30.82 ± 2.67; 6.03 ± 0.19 and 3.46 ± 0.16 and 927.30 ± 94.12, with p < 0.001, p < 0.05, p < 0.01, and p < 0.001). The LF/HF ratio was 12.1% and 20.1% lower in sedentary people (7.55 ± 0.58) compared with footballers (8.46 ± 0.50) and handball players (9.07 ± 0.60), respectively.

Conclusion

Sportsmen showed greater parasympathetic and global modulation when compared to sedentary people.

From cause and effect to causes and effects

It is now-at least loosely-acknowledged that most health and clinical outcomes are influenced by different interacting causes. Surprisingly, medical research studies are nearly universally designed to study-usually in a binary way-the effect of a single cause. Recent experiences during the coronavirus disease 2019 pandemic brought to the forefront that most of our challenges in medicine and healthcare deal with systemic, that is, interdependent and interconnected problems. Understanding these problems defy simplistic dichotomous research methodologies. These insights demand a shift in our thinking from 'cause and effect' to 'causes and effects' since this transcends the classical way of Cartesian reductionist thinking. We require a shift to a 'causes and effects' frame so we can choose the research methodology that reflects the relationships between variables of interest-one-to-one, one-to-many, many-to-one or many-to-many. One-to-one (or cause and effect) relationships are amenable to the traditional randomized control trial design, while all others require systemic designs to understand 'causes and effects'. Researchers urgently need to re-evaluate their science models and embrace research designs that allow an exploration of the clinically obvious multiple 'causes and effects' on health and disease. Clinical examples highlight the application of various systemic research methodologies and demonstrate how 'causes and effects' explain the heterogeneity of clinical outcomes. This shift in scientific thinking will allow us to find the necessary personalized or precise clinical interventions that address the underlying reasons for the variability of clinical outcomes and will contribute to greater health equity.

Fourier phase index for extracting signatures of determinism and nonlinear features in time series

Detecting determinism and nonlinear properties from empirical time series is highly nontrivial. Traditionally, nonlinear time series analysis is based on an error-prone phase space reconstruction that is only applicable for stationary, largely noise-free data from a low-dimensional system and requires the nontrivial adjustment of various parameters. We present a data-driven index based on Fourier phases that detects determinism at a well-defined significance level, without using Fourier transform surrogate data. It extracts nonlinear features, is robust to noise, provides time-frequency resolution by a double running window approach, and potentially distinguishes regular and chaotic dynamics. We test this method on data derived from dynamical models as well as on real-world data, namely, intracranial recordings of an epileptic patient and a series of density related variations of sediments of a paleolake in Tlaxcala, Mexico.

Brain-inspired chaotic backpropagation for MLP

Backpropagation (BP) algorithm is one of the most basic learning algorithms in deep learning. Although BP has been widely used, it still suffers from the problem of easily falling into the local minima due to its gradient dynamics. Inspired by the fact that the learning of real brains may exploit chaotic dynamics, we propose the chaotic backpropagation (CBP) algorithm by integrating the intrinsic chaos of real neurons into BP. By validating on multiple datasets (e.g. cifar10), we show that, for multilayer perception (MLP), CBP has significantly better abilities than those of BP and its variants in terms of optimization and generalization from both computational and theoretical viewpoints. Actually, CBP can be regarded as a general form of BP with global searching ability inspired by the chaotic learning process in the brain. Therefore, CBP not only has the potential of complementing or replacing BP in deep learning practice, but also provides a new way for understanding the learning process of the real brain.

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.

Multiscale adaptive multifractal analysis and its applications

To precisely analyze the fractal nature of a short-term time series under the multiscale framework, this study introduces multiscale adaptive multifractal analysis (MAMFA) combining the adaptive fractal analysis method with the multiscale multifractal analysis (MMA). MAMFA and MMA are both applied to the two kinds of simulation sequences, and the results show that the MAMFA method achieves better performances than MMA. MAMFA is also applied to the Chinese and American stock indexes and the R-R interval of heart rate data. It is found that the multifractal characteristics of stock sequences are related to the selection of the scale range s. There is a big difference in the Hurst surface's shape of Chinese and American stock indexes and Chinese stock indexes have more obvious multifractal characteristics. For the R-R interval sequence, we find that the subjects with abnormal heart rate have significant shape changes in three areas of Hurst surface compared with healthy subjects, thereby patients can be effectively distinguished from healthy subjects.

Entropy Profiling: A Reduced-Parametric Measure of Kolmogorov-Sinai Entropy from Short-Term HRV Signal

Entropy profiling is a recently introduced approach that reduces parametric dependence in traditional Kolmogorov-Sinai (KS) entropy measurement algorithms. The choice of the threshold parameter r of vector distances in traditional entropy computations is crucial in deciding the accuracy of signal irregularity information retrieved by these methods. In addition to making parametric choices completely data-driven, entropy profiling generates a complete profile of entropy information as against a single entropy estimate (seen in traditional algorithms). The benefits of using "profiling" instead of "estimation" are: (a) precursory methods such as approximate and sample entropy that have had the limitation of handling short-term signals (less than 1000 samples) are now made capable of the same; (b) the entropy measure can capture complexity information from short and long-term signals without multi-scaling; and (c) this new approach facilitates enhanced information retrieval from short-term HRV signals. The novel concept of entropy profiling has greatly equipped traditional algorithms to overcome existing limitations and broaden applicability in the field of short-term signal analysis. In this work, we present a review of KS-entropy methods and their limitations in the context of short-term heart rate variability analysis and elucidate the benefits of using entropy profiling as an alternative for the same.

Modified Distribution Entropy as a Complexity Measure of Heart Rate Variability (HRV) Signal

The complexity of a heart rate variability (HRV) signal is considered an important nonlinear feature to detect cardiac abnormalities. This work aims at explaining the physiological meaning of a recently developed complexity measurement method, namely, distribution entropy (DistEn), in the context of HRV signal analysis. We thereby propose modified distribution entropy (mDistEn) to remove the physiological discrepancy involved in the computation of DistEn. The proposed method generates a distance matrix that is devoid of over-exerted multi-lag signal changes. Restricted element selection in the distance matrix makes "mDistEn" a computationally inexpensive and physiologically more relevant complexity measure in comparison to DistEn.

Chaos-Based Analysis of Heart Rate Variability Time Series in Obstructive Sleep Apnea Subjects

Obstructive sleep apnea (OSA) is a common disorder which can cause periodic fluctuations in heart rate. To diagnose sleep apnea, some studies analyze electrocardiogram (ECG) signals by adopting chaos-based analysis. This research is going to specifically focus on whether it is possible to use chaos-based analysis of heart rate variability (HRV) signals rather than using chaotic analysis of ECG signals to diagnose OSA. While conventional studies mostly use chaos-based analysis of ECG signals to detect OSA, here, we apply correlation dimension (CD) as a chaotic index to analyze HRV data in OSA patients. For this purpose, 17 patients with OSA and 9 healthy individuals referred to a sleep clinic in Isfahan/Iran are studied, and their HRV time series were extracted from 1-h ECG signals recorded overnight. The preliminary step to calculate CD is phase-space reconstruction of the system based on HRV time series. Corresponding parameters, including embedding dimension and lag time, are estimated optimally using enhanced related methods, and then CD is calculated using Grassberger–Procaccia algorithm. Moreover, to evaluate our results, detrended fluctuation analysis (DFA), one of the well-known nonlinear methods in HRV analysis to detect OSA, is also applied to our data and the result is compared with those obtained from CD analysis of HRV. CD index with P < 0.005 indicates a significant difference in nonlinear dynamics of HRV signals detected from OSA patients and healthy individuals.

A simple method for detecting chaos in nature

Chaos, or exponential sensitivity to small perturbations, appears everywhere in nature. Moreover, chaos is predicted to play diverse functional roles in living systems. A method for detecting chaos from empirical measurements should therefore be a key component of the biologist's toolkit. But, classic chaos-detection tools are highly sensitive to measurement noise and break down for common edge cases, making it difficult to detect chaos in domains, like biology, where measurements are noisy. However, newer tools promise to overcome these limitations. Here, we combine several such tools into an automated processing pipeline, and show that our pipeline can detect the presence (or absence) of chaos in noisy recordings, even for difficult edge cases. As a first-pass application of our pipeline, we show that heart rate variability is not chaotic as some have proposed, and instead reflects a stochastic process in both health and disease. Our tool is easy-to-use and freely available.

Quantification of Contractile Dynamic Complexities Exhibited by Human Stem Cell-Derived Cardiomyocytes Using Nonlinear Dimensional Analysis

Understanding the complexity of biological signals has been gaining widespread attention due to increasing knowledge on the nonlinearity that exists in these systems. Cardiac signals are known to exhibit highly complex dynamics, consisting of high degrees of interdependency that regulate the cardiac contractile functions. These regulatory mechanisms are important to understand for the development of novel in vitro cardiac systems, especially with the exponential growth in deriving cardiac tissue directly from human induced pluripotent stem cells (hiPSCs). This work describes a unique analytical approach that integrates linear amplitude and frequency analysis of physical cardiac contraction, with nonlinear analysis of the contraction signals to measure the signals’ complexity. We generated contraction motion waveforms reflecting the physical contraction of hiPSC-derived cardiomyocytes (hiPSC-CMs) and implemented these signals to nonlinear analysis to compute the capacity and correlation dimensions. These parameters allowed us to characterize the dynamics of the cardiac signals when reconstructed into a phase space and provided a measure of signal complexity to supplement contractile physiology data. Thus, we applied this approach to evaluate drug response and observed that relationships between contractile physiology and dynamic complexity were unique to each tested drug. This illustrated the applicability of this approach in not only characterization of cardiac signals, but also monitoring and diagnostics of cardiac health in response to external stress.

Motion During Acquisition is Associated With fMRI Brain Entropy

Measures of fMRI brain entropy have been used to investigate age and disease related neural changes. However, it is unclear if movement in the scanner is associated with brain entropy after geometric correction for movement. As age and disease can affect motor control, quantifying and correcting for the influence of movement will avoid false findings. This paper examines the influence of head motion on fMRI brain entropy. Resting-state and task-based fMRI data from 281 individuals born in Aberdeen between 1950 and 1956 were analyzed. The images were realigned, followed by nuisance regression of the head motion parameters. The images were either high-pass filtered (0.008 Hz) or band-pass (0.008-0.1 Hz) filtered in order to compare the two methods; fuzzy approximate entropy and fuzzy sample entropy were calculated for every voxel. Motion was quantified as the mean displacement and mean rotation in three dimensions. Greater mean motion was correlated with decreased entropy for all four methods of calculating entropy. Different movement characteristics produce different patterns of associations, which appear to be artefact. However, across all motion metrics, entropy calculation methods, and scan conditions, a number of regions consistently show a significant negative association: the right cerebellar crus, left precentral gyrus (primary motor cortex), the left postcentral gyrus (primary somatosensory cortex), and the opercular part of the left inferior frontal gyrus. The robustness of our findings at these locations suggests that decreased entropy in specific brain regions may be a marker for decreased motor control.

Association of long-term exposure to traffic-related PM10 with heart rate variability and heart rate dynamics in healthy subjects

BACKGROUND

Epidemiological evidence on the influence of long-term exposure to traffic-related particulate matter (TPM₁₀) on heart rate variability (HRV) is weak.

OBJECTIVE

To evaluate the association of long-term exposure (10 years) with TPM₁₀ on the regulation of the autonomic cardiovascular system and heart rate dynamics (HRD) in an aging general population, as well as potential modifying effects by the a priori selected factors sex, smoking status, obesity, and gene variation in selected glutathione S-transferases (GSTs).

METHODS

We analyzed data from 1593 SAPALDIA cohort participants aged ≥ 50 years. For each participant, various HRV and HRD parameters were derived from 24-hour electrocardiogram recordings. Each parameter obtained was then used as the outcome variable in multivariable mixed linear regression models in order to evaluate the association with TPM₁₀. Potential modifying effects were assessed using interaction terms.

RESULTS

No association between long-term exposure to TPM₁₀ and HRV/HRD was observed in the entire study population. However, HRD changes were found in subjects without cardiovascular morbidity and both HRD and HRV changes in non-obese subjects without cardiovascular morbidity. Subjects without cardiovascular morbidity with homozygous GSTM1 gene deletion appeared to be more susceptible to the effects of TPM₁₀.

CONCLUSION

This study suggests that long-term exposure to TPM₁₀ triggers adverse changes in the regulation of the cardiovascular system. These adverse effects were more visible in the subjects without cardiovascular disease, in whom the overall relationship between TPM₁₀ and HRV/HRD could not be masked by underlying morbidities and the potential counteracting effects of related drug treatments.

Chaos, mitochondria and type 2 diabetes; does type 2 diabetes arise from a metabolic dysrhythmia?

The increasing incidence of type 2 diabetes transcends all cultures, largely due to populations transitioning from traditional diets and manual occupations, to sedentary, calorific lifestyles. Excess calorie intake leads to intramuscular fat accumulation and insulin resistance. Physical inactivity causes underutilization of mitochondria causing dysfunction and inflammation. Both insulin resistance and mitochondrial dysfunction mechanisms are known to be closely related and to antagonise one another, although the precise nature of the relationship has eluded characterization. It is poorly understood why this mutual dysfunction progresses on to clinical diabetes in only some patients, why progression is often stepwise and why diabetes control only weakly predicts future cardiovascular disease in individuals. Clinical prediction in patients is therefore currently unsatisfactory and current linear assumptions require challenging. Cells contain networks of oscillating ionic fluxes. Cellular activity is characterised by complex patterns of fluctuation with sudden transitions between patterns. The non-linear nature of these oscillations is well characterised in neuronal activity, cardiac impulses and more recently mitochondria, but not previously in relation to diabetes. Cells under metabolic stress demonstrate complex fluctuations of mitochondrial distribution, coupling strength and synchronisation resulting in periodic or chaotic oscillations of function, causing accumulation of intracellular fat and excess reactive oxygen species (ROS), which exacerbates insulin resistance. Glucose, insulin and HbA1c in patients are also known to oscillate in complex patterns but the mechanisms and significance are largely unknown. Drawing on existing evidence and models from other diseases, a nonlinear, dynamical hypothesis of diabetes onset and progression is proposed. Insulin receptor pathways and mitochondria are treated as two populations of coupled, phase oscillators. Health or disease states depend on system stability or instability and reflect the balance of substrate supply and energy demand. The implication of this novel mechanism is that diabetes and the complications are not the consequence of a distinct pathological agent or pathway, but more an evolving dysrhythmia of normal cellular energetics systems, resulting from accumulated adverse lifestyle conditions. This hypothesis is proposed with the intention of stimulating research into non-linear dynamical constructs as an alternative to current linear models, to improve risk prediction and trajectory analysis in type 2 diabetes.

Comparison and Efficacy of Synergistic Intelligent Tutoring Systems with Human Physiological Response

The analysis of physiological signals is ubiquitous in health and medical diagnosis as a primary tool for investigation and inquiry. Physiological signals are now being widely used for psychological and social fields. They have found promising application in the field of computer-based learning and tutoring. Intelligent Tutoring Systems (ITS) is a fast-paced growing field which deals with the design and implementation of customized computer-based instruction and feedback methods without human intervention. This paper introduces the key concepts and motivations behind the use of physiological signals. It presents a detailed discussion and experimental comparison of ITS. The synergism of ITS and physiological signals in automated tutoring systems adapted to the learner's emotions and mental states are presented and compared. The insights are developed, and details are presented. The accuracy and classification methods of existing systems are highlighted as key areas of improvement. High-precision measurement systems and neural networks for machine-learning classification are deemed prospective directions for future improvements to existing systems.

Mitochondrial chaotic dynamics: Redox-energetic behavior at the edge of stability

Mitochondria serve multiple key cellular functions, including energy generation, redox balance, and regulation of apoptotic cell death, thus making a major impact on healthy and diseased states. Increasingly recognized is that biological network stability/instability can play critical roles in determining health and disease. We report for the first-time mitochondrial chaotic dynamics, characterizing the conditions leading from stability to chaos in this organelle. Using an experimentally validated computational model of mitochondrial function, we show that complex oscillatory dynamics in key metabolic variables, arising at the “edge” between fully functional and pathological behavior, sets the stage for chaos. Under these conditions, a mild, regular sinusoidal redox forcing perturbation triggers chaotic dynamics with main signature traits such as sensitivity to initial conditions, positive Lyapunov exponents, and strange attractors. At the “edge” mitochondrial chaos is exquisitely sensitive to the antioxidant capacity of matrix Mn superoxide dismutase as well as to the amplitude and frequency of the redox perturbation. These results have potential implications both for mitochondrial signaling determining health maintenance, and pathological transformation, including abnormal cardiac rhythms.

Automated characterization of cardiovascular diseases using relative wavelet nonlinear features extracted from ECG signals

Cardiovascular diseases (CVDs) are the leading cause of deaths worldwide. The rising mortality rate can be reduced by early detection and treatment interventions. Clinically, electrocardiogram (ECG) signal provides useful information about the cardiac abnormalities and hence employed as a diagnostic modality for the detection of various CVDs. However, subtle changes in these time series indicate a particular disease. Therefore, it may be monotonous, time-consuming and stressful to inspect these ECG beats manually. In order to overcome this limitation of manual ECG signal analysis, this paper uses a novel discrete wavelet transform (DWT) method combined with nonlinear features for automated characterization of CVDs. ECG signals of normal, and dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM) and myocardial infarction (MI) are subjected to five levels of DWT. Relative wavelet of four nonlinear features such as fuzzy entropy, sample entropy, fractal dimension and signal energy are extracted from the DWT coefficients. These features are fed to sequential forward selection (SFS) technique and then ranked using ReliefF method. Our proposed methodology achieved maximum classification accuracy (acc) of 99.27%, sensitivity (sen) of 99.74%, and specificity (spec) of 98.08% with K-nearest neighbor (kNN) classifier using 15 features ranked by the ReliefF method. Our proposed methodology can be used by clinical staff to make faster and accurate diagnosis of CVDs. Thus, the chances of survival can be significantly increased by early detection and treatment of CVDs.

Detection of coronary artery disease by reduced features and extreme learning machine

Objective

Cardiovascular diseases generate the highest mortality in the globe population, mainly due to coronary artery disease (CAD) like arrhythmia, myocardial infarction and heart failure. Therefore, an early identification of CAD and diagnosis is essential. For this, we have proposed a new approach to detect the CAD patients using heart rate variability (HRV) signals. This approach is based on subspaces decomposition of HRV signals using multiscale wavelet packet (MSWP) transform and entropy features extracted from decomposed HRV signals. The detection performance was analyzed using Fisher ranking method, generalized discriminant analysis (GDA) and binary classifier as extreme learning machine (ELM). The ranking strategies designate rank to the available features extracted by entropy methods from decomposed heart rate variability (HRV) signals and organize them according to their clinical importance. The GDA diminishes the dimension of ranked features. In addition, it can enhance the classification accuracy by picking the best discerning of ranked features. The main advantage of ELM is that the hidden layer does not require tuning and it also has a fast rate of detection.

Methodology

For the detection of CAD patients, the HRV data of healthy normal sinus rhythm (NSR) and CAD patients were obtained from a standard database. Self recorded data as normal sinus rhythm (Self_NSR) of healthy subjects were also used in this work. Initially, the HRV time-series was decomposed to 4 levels using MSWP transform. Sixty two features were extracted from decomposed HRV signals by non-linear methods for HRV analysis, fuzzy entropy (FZE) and Kraskov nearest neighbour entropy (K-NNE). Out of sixty-two features, 31 entropy features were extracted by FZE and 31 entropy features were extracted by K-NNE method. These features were selected since every feature has a different physical premise and in this manner concentrates and uses HRV signals information in an assorted technique. Out of 62 features, top ten features were selected, ranked by a ranking method called as Fisher score. The top ten features were applied to the proposed model, GDA with Gaussian or RBF kernal + ELM having hidden node as sigmoid or multiquadric. The GDA method transforms top ten features to only one feature and ELM has been used for classification.

Results

Numerical experimentations were performed on the combination of datasets as NSR-CAD and Self_NSR- CAD subjects. The proposed approach has shown better performance using top ten ranked entropy features. The GDA with RBF kernel + ELM having hidden node as multiquadric method and GDA with Gaussian kernel + ELM having hidden node as sigmoid or multiquadric method achieved an approximate detection accuracy of 100% compared to ELM and linear discriminant analysis (LDA)+ELM for both datasets. The subspaces level-4 and level-3 decomposition of HRV signals by MSWP transform can be used for detection and analysis of CAD patients.

THE EFFECT OF DIABETES ON CARDIOVASCULAR SYSTEM

Diabetes mellitus (DM) is a critical and long-term disorder due to the insufficient production of insulin by the pancreas or ineffective use of insulin by the body. Importantly, cardiovascular disease (CVD) has long been thought to be linked with diabetes. Despite more diabetic individuals surviving from better medications and treatments, there has been significant rise in the morbidity and mortality from CVD. Indeed, the classification of DM based on the electrocardiogram signals of the heart will be an advantageous system. Further, computer-aided classification of DM with integrated algorithms may enhance the execution of the system. In this paper, we have reviewed various studies using heart rate variability signals for automated classification of diabetes. Furthermore, the different techniques used to extract the features and the efficiency of the classification systems are discussed.

SHOCKABLE VERSUS NONSHOCKABLE LIFE-THREATENING VENTRICULAR ARRHYTHMIAS USING DWT AND NONLINEAR FEATURES OF ECG SIGNALS

Shockable ventricular arrhythmias (VAs) such as ventricular tachycardia (VT) and ventricular fibrillation (VFib) are the life-threatening conditions requiring immediate attention. Cardiopulmonary resuscitation (CPR) and defibrillation are the significant immediate recommended treatments for these shockable arrhythmias to obtain the return of spontaneous circulation. However, accurate classification of these shockable VAs from nonshockable ones is the key step during defibrillation by automated external defibrillator (AED). Therefore, in this work, we have proposed a novel algorithm for an automated differentiation of shockable and nonshockable VAs from electrocardiogram (ECG) signal. The ECG signals are segmented into 5, 8 and 10[Formula: see text]s. These segmented ECGs are subjected to four levels of discrete wavelet transformation (DWT). Various nonlinear features such as approximate entropy ([Formula: see text], signal energy ([Formula: see text]), Fuzzy entropy ([Formula: see text]), Kolmogorov Sinai entropy ([Formula: see text], permutation entropy ([Formula: see text]), Renyi entropy ([Formula: see text]), sample entropy ([Formula: see text]), Shannon entropy ([Formula: see text]), Tsallis entropy ([Formula: see text]), wavelet entropy ([Formula: see text]), fractal dimension ([Formula: see text]), Kolmogorov complexity ([Formula: see text]), largest Lyapunov exponent ([Formula: see text]), recurrence quantification analysis (RQA) parameters ([Formula: see text]), Hurst exponent ([Formula: see text]), activity entropy ([Formula: see text]), Hjorth complexity ([Formula: see text]), Hjorth mobility ([Formula: see text]), modified multi scale entropy ([Formula: see text]) and higher order statistics (HOS) bispectrum ([Formula: see text]) are obtained from the DWT coefficients. Later, these features are subjected to sequential forward feature selection (SFS) method and selected features are then ranked using seven ranking methods namely, Bhattacharyya distance, entropy, Fuzzy maximum relevancy and minimum redundancy (mRMR), receiver operating characteristic (ROC), Student’s [Formula: see text]-test, Wilcoxon and ReliefF. These ranked features are supplied independently into the [Formula: see text]-Nearest Neighbor (kNN) classifier. Our proposed system achieved maximum accuracy, sensitivity and specificity of (i) 97.72%, 94.79% and 98.74% for 5[Formula: see text]s, (ii) 98.34%, 95.49% and 99.14% for 8[Formula: see text]s and (iii) 98.32%, 95.16% and 99.20% for 10[Formula: see text]s of ECG segments using only ten features. The integration of the proposed algorithm with ECG acquisition systems in the intensive care units (ICUs) can help the clinicians to decipher the shockable and nonshockable life-threatening arrhythmias accurately. Hence, doctors can use the CPR or AED immediately and increase the chance of survival during shockable life-threatening arrhythmia intervals.

Healthcare teams as complex adaptive systems: Focus on interpersonal interaction

OBJECTIVE

The aim of this study is to test the feasibility of a tool to objectify the functioning of healthcare teams operating in the complexity zone, and to evaluate its usefulness in identifying areas for team quality improvement.

METHODS

We distributed The Complex Adaptive Leadership (CAL™) Organisational Capability Questionnaire (OCQ) to all members of one palliative care team (n=15) and to palliative care physicians in Flanders, Belgium (n=15). Group discussions were held on feasibility aspects and on the low scoring topics. Data was analysed calculating descriptive statistics (sum score, mean and standard deviation). The one sample T-Test was used to detect differences within each group.

RESULTS

Both groups of participants reached mean scores ranging from good to excellent. The one sample T test showed statistically significant differences between participants' sum scores within each group (p<0,001). Group discussion led to suggestions for quality improvement e.g. enhanced feedback strategies between team members.

CONCLUSION

The questionnaire used in our study shows to be a feasible and useful instrument for the evaluation of the palliative care teams' day-to-day operations and to identify areas for quality improvement.

PRACTICAL IMPLICATIONS

The CAL™OCQ is a promising instrument to evaluate any healthcare team functioning. A group discussion on the questionnaire scores can serve as a starting point to identify targets for quality improvement initiatives.

Hidden Signals-The History and Methods of Heart Rate Variability

The understanding of heart rate variability (HRV) has increased parallel with the development of modern physiology. Discovered probably first in 1847 by Ludwig, clinical applications evolved in the second part of the twentieth century. Today HRV is mostly used in cardiology and research settings. In general, HRV can be measured over shorter (e.g., 5-10 min) or longer (12 or 24 h) periods. Since 1996, most measurements and calculations are made according to the standard of the Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. As the first step, the series of times between successive R-peaks in the ECG are in milliseconds. It is crucial, however, to identify and remove extrasystoles and artifacts according to standard protocols. The series of QRS distances between successive heartbeats can be analyzed with simple or more sophisticated algorithms, beginning with standard deviation (SDNN) or by the square root of the mean of the sum of squares of differences between adjacent normal RR (rMSSD). Short-term HRV is frequently analyzed with the help of a non-parametric fast Fourier transformation quantifying the different frequency bands during the measurement period. In the last decades, various non-linear algorithms have been presented, such as different entropy and fractal measures or wavelet analysis. Although most of them have a strong theoretical foundation, their clinical relevance is still debated.

Heart rhythm characterization through induced physiological variables

Atrial fibrillation remains a major cause of morbi-mortality, making mass screening desirable and leading industry to actively develop devices devoted to automatic AF detection. Because there is a tendency toward mobile devices, there is a need for an accurate, rapid method for studying short inter-beat interval time series for real-time automatic medical monitoring. We report a new methodology to efficiently select highly discriminative variables between physiological states, here a normal sinus rhythm or atrial fibrillation. We generate induced variables using the first ten time derivatives of an RR interval time series and formally express a new multivariate metric quantifying their discriminative power to drive state variable selection. When combined with a simple classifier, this new methodology results in 99.9% classification accuracy for 1-min RR interval time series (n = 7,400), with heart rate accelerations and jerks being the most discriminant variables. We show that the RR interval time series can be drastically reduced from 60 s to 3 s, with a classification accuracy of 95.0%. We show that heart rhythm characterization is facilitated by induced variables using time derivatives, which is a generic methodology that is particularly suitable to real-time medical monitoring.

Dynamics and complexity of body temperature in preterm infants nursed in incubators

BACKGROUND

Poor control of body temperature is associated with mortality and major morbidity in preterm infants. We aimed to quantify its dynamics and complexity to evaluate whether indices from fluctuation analyses of temperature time series obtained within the first five days of life are associated with gestational age (GA) and body size at birth, and presence and severity of typical comorbidities of preterm birth.

METHODS

We recorded 3h-time series of body temperature using a skin electrode in incubator-nursed preterm infants. We calculated mean and coefficient of variation of body temperature, scaling exponent alpha (Talpha) derived from detrended fluctuation analysis, and sample entropy (TSampEn) of temperature fluctuations. Data were analysed by multilevel multivariable linear regression.

RESULTS

Data of satisfactory technical quality were obtained from 285/357 measurements (80%) in 73/90 infants (81%) with a mean (range) GA of 30.1 (24.0-34.0) weeks. We found a positive association of Talpha with increasing levels of respiratory support after adjusting for GA and birth weight z-score (p<0.001; R2 = 0.38).

CONCLUSION

Dynamics and complexity of body temperature in incubator-nursed preterm infants show considerable associations with GA and respiratory morbidity. Talpha may be a useful marker of autonomic maturity and severity of disease in preterm infants.

Heart Rate Variability in Intensive Care

Clinicians have long been aware that the normal oscillations in a heart beat are lost during fetal distress, during the early stages of heart failure, with advanced aging, and with critical illness and injury. However, these oscillations, or variability in heart rate and other cardiovascular signals, have largely been ignored or discounted as variances from the mean or average values. It is becoming increasingly clear that these oscillations reflect the dynamic interactions of many physiologic processes, including neuroautonomic regulation of heart rate and blood pressure. We present a synthesis and review of the current literature concerning heart rate variability with special reference to intensive care. This article describes the background of time series analysis of heart rate variability including time and frequency domain and nonlinear measurements. The implications and potential for time series analysis of variability in cardiovascular signals in clinical diagnosis and management of critically ill and injured patients are discussed.

Differentiation of Overweight from Normal Weight Young Adults by Postprandial Heart Rate Variability and Systolic Blood Pressure

INTRODUCTION

Obesity and cardiovascular disease are inextricably linked and the health community's response to the current epidemic of adolescent obesity may be improved by the ability to target adolescents at highest risk for developing cardiovascular disease in the future. Overweight manifests early as autonomic dysregulation and current methods do not permit differentiation of overweight adolescents or young adults at highest risk for developing cardiovascular disease.

AIM

This study was designed to test the hypothesis that scaling exponents motivated by nonlinear fractal analyses of Heart Rate Variability (HRV) differentiate overweight, otherwise healthy adolescent/young adult subjects at risk for developing prehypertension, the primary forerunner of cardiovascular disease.

MATERIALS AND METHODS

The subjects were 18-20year old males with Body Mass Index (BMI) 20.1-42.5kg/m(2). Electrocardiographic inter-beat (RR) intervals were measured during 3h periods of bed rest after overnight fasting and ingestion of 900Cal high-carbohydrate and high-fat test beverages on separate days. Detrended Fluctuation Analysis (DFA), k-means cluster and ANOVA analyses of scaling coefficients α, α(1), and α(2), showed dependencies on hourly measurements of systolic blood pressure and on premeasured BMI.

RESULTS

It was observed that α value increased during the caloric challenge, appears to represent metabolically-induced changes in HRV across the participants. An ancillary analysis was performed to determine the dependency on BMI without BMI as a parameter. Cluster analysis of the high-carbohydrate test beverage treatment and the high-fat treatment produced grouping with very little overlap. ANOVA on both clusters demonstrated significance at p<0.001. We were able to demonstrate increased sympathetic modulation of our study group during ingestion and metabolism of isocaloric high-carbohydrate and high-fat test beverages.

CONCLUSION

These findings demonstrate significantly different clustering of α, α1, and α2 and Systolic Blood Pressure (SBP) with respect to normal, overweight and obese BMI.

A preprocessing tool for removing artifact from cardiac RR interval recordings using three-dimensional spatial distribution mapping

Artifact is common in cardiac RR interval data that is recorded for heart rate variability (HRV) analysis. A novel algorithm for artifact detection and interpolation in RR interval data is described. It is based on spatial distribution mapping of RR interval magnitude and relationships to adjacent values in three dimensions. The characteristics of normal physiological RR intervals and artifact intervals were established using 24-h recordings from 20 technician-assessed human cardiac recordings. The algorithm was incorporated into a preprocessing tool and validated using 30 artificial RR (ARR) interval data files, to which known quantities of artifact (0.5%, 1%, 2%, 3%, 5%, 7%, 10%) were added. The impact of preprocessing ARR files with 1% added artifact was also assessed using 10 time domain and frequency domain HRV metrics. The preprocessing tool was also used to preprocess 69 24-h human cardiac recordings. The tool was able to remove artifact from technician-assessed human cardiac recordings (sensitivity 0.84, SD = 0.09, specificity of 1.00, SD = 0.01) and artificial data files. The removal of artifact had a low impact on time domain and frequency domain HRV metrics (ranging from 0% to 2.5% change in values). This novel preprocessing tool can be used with human 24-h cardiac recordings to remove artifact while minimally affecting physiological data and therefore having a low impact on HRV measures of that data.

Long-term smoking cessation and heart rate dynamics in an aging healthy cohort: Is it possible to fully recover?

AIM

To evaluate the long-term influence of smoking cessation on the regulation of the autonomic cardiovascular system in an aging general population, using the subpopulation of lifelong non-smokers as control group.

METHODS

We analyzed 1481 participants aged ≥50 years from the SAPALDIA cohort. In each participant, heart rate variability and heart rate dynamics were characterized by means of various quantitative analyzes of the inter-beat interval time series generated from 24-hour electrocardiogram recordings. Each parameter obtained was then used as the outcome variable in multivariable linear regression models in order to evaluate the association with smoking status and time elapsed since smoking cessation. The models were adjusted for known confounding factors and stratified by the time elapsed since smoking cessation.

RESULTS

Our findings indicate that smoking triggers adverse changes in the regulation of the cardiovascular system, even at low levels of exposure since current light smokers exhibited significant changes as compared to lifelong non-smokers. Moreover, there was evidence for a dose-response effect. Indeed, the changes observed in current heavy smokers were more marked as compared to current light smokers. Furthermore, full recovery was achieved in former smokers (i.e., normalization to the level of lifelong non-smokers). However, while light smokers fully recovered within the 15 first years of cessation, heavy former smokers might need up to 15-25 years to fully recover.

CONCLUSION

This study supports the substantial benefits of smoking cessation, but also warns of important long-term alterations caused by heavy smoking.

Classification of cardiac rhythm using heart rate dynamical measures: validation in MIT-BIH databases

BACKGROUND

Identification of atrial fibrillation (AF) is a clinical imperative. Heartbeat interval time series are increasingly available from personal monitors, allowing new opportunity for AF diagnosis.

GOAL

Previously, we devised numerical algorithms for identification of normal sinus rhythm (NSR), AF, and SR with frequent ectopy using dynamical measures of heart rate. Here, we wished to validate them in the canonical MIT-BIH ECG databases.

METHODS

We tested algorithms on the NSR, AF and arrhythmia databases.

RESULTS

When the databases were combined, the positive predictive value of the new algorithms exceeded 95% for NSR and AF, and was 40% for SR with ectopy. Further, dynamical measures did not distinguish atrial from ventricular ectopy. Inspection of individual 24hour records showed good correlation of observed and predicted rhythms.

CONCLUSION

Heart rate dynamical measures are effective ingredients in numerical algorithms to classify cardiac rhythm from the heartbeat intervals time series alone.

The trajectory of life. Decreasing physiological network complexity through changing fractal patterns

In this position paper, we submit a synthesis of theoretical models based on physiology, non-equilibrium thermodynamics, and non-linear time-series analysis. Based on an understanding of the human organism as a system of interconnected complex adaptive systems, we seek to examine the relationship between health, complexity, variability, and entropy production, as it might be useful to help understand aging, and improve care for patients. We observe the trajectory of life is characterized by the growth, plateauing and subsequent loss of adaptive function of organ systems, associated with loss of functioning and coordination of systems. Understanding development and aging requires the examination of interdependence among these organ systems. Increasing evidence suggests network interconnectedness and complexity can be captured/measured/associated with the degree and complexity of healthy biologic rhythm variability (e.g., heart and respiratory rate variability). We review physiological mechanisms linking the omics, arousal/stress systems, immune function, and mitochondrial bioenergetics; highlighting their interdependence in normal physiological function and aging. We argue that aging, known to be characterized by a loss of variability, is manifested at multiple scales, within functional units at the small scale, and reflected by diagnostic features at the larger scale. While still controversial and under investigation, it appears conceivable that the integrity of whole body complexity may be, at least partially, reflected in the degree and variability of intrinsic biologic rhythms, which we believe are related to overall system complexity that may be a defining feature of health and it's loss through aging. Harnessing this information for the development of therapeutic and preventative strategies may hold an opportunity to significantly improve the health of our patients across the trajectory of life.

Linear and nonlinear analysis of normal and CAD-affected heart rate signals

Coronary artery disease (CAD) is one of the dangerous cardiac disease, often may lead to sudden cardiac death. It is difficult to diagnose CAD by manual inspection of electrocardiogram (ECG) signals. To automate this detection task, in this study, we extracted the heart rate (HR) from the ECG signals and used them as base signal for further analysis. We then analyzed the HR signals of both normal and CAD subjects using (i) time domain, (ii) frequency domain and (iii) nonlinear techniques. The following are the nonlinear methods that were used in this work: Poincare plots, Recurrence Quantification Analysis (RQA) parameters, Shannon entropy, Approximate Entropy (ApEn), Sample Entropy (SampEn), Higher Order Spectra (HOS) methods, Detrended Fluctuation Analysis (DFA), Empirical Mode Decomposition (EMD), Cumulants, and Correlation Dimension. As a result of the analysis, we present unique recurrence, Poincare and HOS plots for normal and CAD subjects. We have also observed significant variations in the range of these features with respect to normal and CAD classes, and have presented the same in this paper. We found that the RQA parameters were higher for CAD subjects indicating more rhythm. Since the activity of CAD subjects is less, similar signal patterns repeat more frequently compared to the normal subjects. The entropy based parameters, ApEn and SampEn, are lower for CAD subjects indicating lower entropy (less activity due to impairment) for CAD. Almost all HOS parameters showed higher values for the CAD group, indicating the presence of higher frequency content in the CAD signals. Thus, our study provides a deep insight into how such nonlinear features could be exploited to effectively and reliably detect the presence of CAD.

The impact of noise on the reliability of heart-rate variability and complexity analysis in trauma patients

This study focused on the impact of noise on the reliability of heart-rate variability and complexity (HRV, HRC) to discriminate between different trauma patients and to monitor individual patients. Life-saving interventions (LSIs) were chosen as an endpoint because performance of LSIs is a critical aspect of trauma patient care. Noise was modeled and simulated by modifying original R-R interval (RRI) sequences via decimation, concatenation, and division of RRIs, as well as R-wave detection using the electrocardiogram. Results showed that under increasing simulated noise, entropy and autocorrelation measures can still effectively discriminate between LSI and non-LSI patients and monitor individuals over time.

Automated identification of normal and diabetes heart rate signals using nonlinear measures

Diabetes mellitus (DM) affects considerable number of people in the world and the number of cases is increasing every year. Due to a strong link to the genetic basis of the disease, it is extremely difficult to cure. However, it can be controlled to prevent severe consequences, such as organ damage. Therefore, diabetes diagnosis and monitoring of its treatment is very important. In this paper, we have proposed a non-invasive diagnosis support system for DM. The system determines whether or not diabetes is present by determining the cardiac health of a patient using heart rate variability (HRV) analysis. This analysis was based on nine nonlinear features namely: Approximate Entropy (ApEn), largest Lyapunov exponet (LLE), detrended fluctuation analysis (DFA) and recurrence quantification analysis (RQA). Clinically significant measures were used as input to classification algorithms, namely AdaBoost, decision tree (DT), fuzzy Sugeno classifier (FSC), k-nearest neighbor algorithm (k-NN), probabilistic neural network (PNN) and support vector machine (SVM). Ten-fold stratified cross-validation was used to select the best classifier. AdaBoost, with least squares (LS) as weak learner, performed better than the other classifiers, yielding an average accuracy of 90%, sensitivity of 92.5% and specificity of 88.7%.

Multiscale analysis of heart rate variability in non-stationary environments

Heart rate variability (HRV) is highly non-stationary, even if no perturbing influences can be identified during the recording of the data. The non-stationarity becomes more profound when HRV data are measured in intrinsically non-stationary environments, such as social stress. In general, HRV data measured in such situations are more difficult to analyze than those measured in constant environments. In this paper, we analyze HRV data measured during a social stress test using two multiscale approaches, the adaptive fractal analysis (AFA) and scale-dependent Lyapunov exponent (SDLE), for the purpose of uncovering differences in HRV between chronic fatigue syndrome (CFS) patients and their matched-controls. CFS is a debilitating, heterogeneous illness with no known biomarker. HRV has shown some promise recently as a non-invasive measure of subtle physiological disturbances and trauma that are otherwise difficult to assess. If the HRV in persons with CFS are significantly different from their healthy controls, then certain cardiac irregularities may constitute good candidate biomarkers for CFS. Our multiscale analyses show that there are notable differences in HRV between CFS and their matched controls before a social stress test, but these differences seem to diminish during the test. These analyses illustrate that the two employed multiscale approaches could be useful for the analysis of HRV measured in various environments, both stationary and non-stationary.

COMPREHENSIVE ANALYSIS OF NORMAL AND DIABETIC HEART RATE SIGNALS: A REVIEW

A large section of the world's population is affected by diabetes mellitus (DM), commonly referred to as "diabetes." Every year, the number of cases of DM is increasing. Diabetes has a strong genetic basis, hence it is very difficult to cure, but can be controlled with medications to prevent subsequent organ damage. Therefore, early diagnosis of diabetes is very important. In this paper, we examine how diabetes affects cardiac health, which is reflected through heart rate variability (HRV), as observed in electrocardiography (ECG) signals. Such signals provide clues for both the presence and severity of diabetes as well as diabetes-induced cardiac impairments. Heart rate (HR) is a non-linear and non-stationary signal. Thus, extracting useful information from HRV signals is a difficult task. We review several sophisticated signal processing and information extraction methods in order to establish measurable relationships between the presence and the extent of diabetes as well as the changes in the HRV signals. Furthermore, we discuss a typical range of values for several statistical, geometric, time domain, frequency domain, time–frequency, and non-linear features for HR signals from 15 normal and 15 diabetic subjects. We found that non-linear analysis is the most suitable approach to capture and analyze the subtle changes in HRV signals caused by diabetes.

AUTOMATED DIAGNOSIS OF CARDIAC HEALTH USING RECURRENCE QUANTIFICATION ANALYSIS

The sum total of millions of cardiac cell depolarization potentials can be represented using an electrocardiogram (ECG). By inspecting the P-QRS-T wave in the ECG of a patient, the cardiac health can be diagnosed. Since the amplitude and duration of the ECG signal are too small, subtle changes in the ECG signal are very difficult to be deciphered. In this work, the heart rate variability (HRV) signal has been used as the base signal to observe the functioning of the heart. The HRV signal is non-linear and non-stationary. Recurrence quantification analysis (RQA) has been used to extract the important features from the heart rate signals. These features were fed to the fuzzy, Gaussian mixture model (GMM), and probabilistic neural network (PNN) classifiers for automated classification of cardiac bio-electrical contractile disorders. Receiver operating characteristics (ROC) was used to test the performance of the classifiers. In our work, the Fuzzy classifier performed better than the other classifiers and demonstrated an average classification accuracy, sensitivity, specificity, and positive predictive value of more than 83%. The developed system is suitable to evaluate large datasets.

Multiscale analysis of biological data by scale-dependent lyapunov exponent

Physiological signals often are highly non-stationary (i.e., mean and variance change with time) and multiscaled (i.e., dependent on the spatial or temporal interval lengths). They may exhibit different behaviors, such as non-linearity, sensitive dependence on small disturbances, long memory, and extreme variations. Such data have been accumulating in all areas of health sciences and rapid analysis can serve quality testing, physician assessment, and patient diagnosis. To support patient care, it is very desirable to characterize the different signal behaviors on a wide range of scales simultaneously. The Scale-Dependent Lyapunov Exponent (SDLE) is capable of such a fundamental task. In particular, SDLE can readily characterize all known types of signal data, including deterministic chaos, noisy chaos, random 1/f(α) processes, stochastic limit cycles, among others. SDLE also has some unique capabilities that are not shared by other methods, such as detecting fractal structures from non-stationary data and detecting intermittent chaos. In this article, we describe SDLE in such a way that it can be readily understood and implemented by non-mathematically oriented researchers, develop a SDLE-based consistent, unifying theory for the multiscale analysis, and demonstrate the power of SDLE on analysis of heart-rate variability (HRV) data to detect congestive heart failure and analysis of electroencephalography (EEG) data to detect seizures.

Heart rate variability - a historical perspective

Heart rate variability (HRV), the beat-to-beat variation in either heart rate or the duration of the R-R interval - the heart period, has become a popular clinical and investigational tool. The temporal fluctuations in heart rate exhibit a marked synchrony with respiration (increasing during inspiration and decreasing during expiration - the so called respiratory sinus arrhythmia, RSA) and are widely believed to reflect changes in cardiac autonomic regulation. Although the exact contributions of the parasympathetic and the sympathetic divisions of the autonomic nervous system to this variability are controversial and remain the subject of active investigation and debate, a number of time and frequency domain techniques have been developed to provide insight into cardiac autonomic regulation in both health and disease. It is the purpose of this essay to provide an historical overview of the evolution in the concept of HRV. Briefly, pulse rate was first measured by ancient Greek physicians and scientists. However, it was not until the invention of the "Physician's Pulse Watch" (a watch with a second hand that could be stopped) in 1707 that changes in pulse rate could be accurately assessed. The Rev. Stephen Hales (1733) was the first to note that pulse varied with respiration and in 1847 Carl Ludwig was the first to record RSA. With the measurement of the ECG (1895) and advent of digital signal processing techniques in the 1960s, investigation of HRV and its relationship to health and disease has exploded. This essay will conclude with a brief description of time domain, frequency domain, and non-linear dynamic analysis techniques (and their limitations) that are commonly used to measure HRV.

A topographical map of the causal network of mechanisms underlying the relationship between major depressive disorder and coronary heart disease

OBJECTIVE

Major depressive disorder (MDD) and coronary heart disease (CHD) are both clinically important public health problems. Depression is linked with a higher incidence of ischaemic cardiac events and MDD is more prevalent in patients with CHD. No single comprehensive model has yet described the causal mechanisms linking MDD to CHD. Several key mechanisms have been put forward, comprising behavioural mechanisms, genetic mechanisms, dysregulation of immune mechanisms, coagulation abnormalities and vascular endothelial dysfunction, polyunsaturated omega-3 free fatty acid deficiency, and autonomic mechanisms. It has been suggested that these mechanisms form a network, which links MDD and CHD. The aim of this review is to examine the causal mechanisms underlying the relationship between MDD and CHD, with the aim of constructing a topological map of the causal network which describes the relationship between MDD and CHD.

METHODS

The search term 'depression and heart disease' was entered into an electronic multiple database search engine. Abstracts were screened for relevance and individually selected articles were collated.

RESULTS

This review introduces the first topological map of the causal network which describes the relationship between MDD and CHD.

CONCLUSIONS

Viewing the causal pathways as an interdependent network presents a new paradigm in this field and provides fertile ground for further research. The causal network can be studied using the methodology of systems biology, which is briefly introduced. Future research should focus on the creation of a more comprehensive topological map of the causal network and the quantification of the activity between each node of the causal network.