Information Theory, Living Systems, and Communication Engineering.

Mainstream research on information theory within the field of living systems involves the application of analytical tools to understand a broad range of life processes. This paper is dedicated to an opposite problem: it explores the information theory and communication engineering methods that have counterparts in the data transmission process by way of DNA structures and neural fibers. Considering the requirements of modern multimedia, transmission methods chosen by nature may be different, suboptimal, or even far from optimal. However, nature is known for rational resource usage, so its methods have a significant advantage: they are proven to be sustainable. Perhaps understanding the engineering aspects of methods of nature can inspire a design of alternative green, stable, and low-cost transmission.

Assessment of Autonomic Nervous System Dysfunction in the Early Phase of Infection With SARS-CoV-2 Virus.

BACKGROUND: We are facing the outburst of coronavirus disease 2019 (COVID-19) defined as a serious, multisystem, disorder, including various neurological manifestations in its presentation. So far, autonomic dysfunction (AD) has not been reported in patients with COVID-19 infection. AIM: Assessment of AD in the early phase of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 virus). PATIENTS AND METHODS: We analyzed 116 PCR positive COVID-19 patients. After the exclusion of 41 patients with associate diseases (CADG), partitioned to patients with diabetes mellitus, hypertension, and syncope, the remaining patients were included into a severe group (45 patients with confirmed interstitial pneumonia) and mild group (30 patients). Basic cardiovascular autonomic reflex tests (CART) were performed, followed by beat-to-beat heart rate variability (HRV) and systolic and diastolic blood pressure variability (BPV) analysis, along with baroreceptor sensitivity (BRS). Non-linear analysis of HRV was provided by Poincare Plot. Results were compared to 77 sex and age-matched controls. RESULTS: AD (sympathetic, parasympathetic, or both) in our study has been revealed in 51.5% of severe, 78.0% of mild COVID-19 patients, and the difference compared to healthy controls was significant (p = 0.018). Orthostatic hypotension has been established in 33.0% COVID-19 patients compared to 2.6% controls (p = 0.001). Most of the spectral parameters of HRV and BPV confirmed AD, most prominent in the severe COVID-19 group. BRS was significantly lower in all patients (severe, mild, CADG), indicating significant sudden cardiac death risk. CONCLUSION: Cardiovascular autonomic neuropathy should be taken into account in COVID-19 patients' assessment. It can be an explanation for a variety of registered manifestations, enabling a comprehensive diagnostic approach and further treatment.

Phenomapping for classification of doxorubicin-induced cardiomyopathy in rats.

Cardiomyopathy resistant to treatment is the most serious adverse effect of doxorubicin (dox). The mechanisms of dox-induced cardiomyopathy (DCM) have been extensively studied in dilated forms of DCM. However, efficient treatment did not emerge. The aim of the present work was to revisit the experimental model of DCM in rats, to define phenotype/s and associate them to the changes in cardiac transcriptome. Male Wistar rats equipped with radiotelemetry device, were randomized in DOX group (5 mg/0,5 mL/kg, IV dox; n = 18) and CONT group (0,5 mL/kg IV saline; n = 6). Echocardiography, autonomic spectral markers and baroreceptor reflex evaluation was performed prior to, and after treatment. Blood samples were collected at the end of experimentation. Cardiac, renal and hepatic tissues were analysed post-mortem by histology. Changes in expression of key cardiac genes affected by dox were assessed by RT-qPCR. Phenotypes were identified by clustering non-redundant features using four different algorithms averaged by evidence accumulation cluster technique. The results emphasize the existence of two major phenotypes of DCM with comparably high mortality rates: phenotype 1 characterized by, left ventricular (LV) dilatation, thinning of LV posterior wall, reduced LV ejection fraction (LVEF) and fractional shortening (LVFS), decreased HR variability (HRV), decreased baroreceptor effectiveness index (BEI) and increased NT-proBNP; and phenotype 2 with LV hypertrophy - increased LV mass, preserved LVEF, LVFS, no changes in HRV and BEI and moderate NT-proBNP increase. Both phenotypes exhibited a genetic shift to a new-born program.

Entropy Analysis of COVID-19 Cardiovascular Signals.

The world has faced a coronavirus outbreak, which, in addition to lung complications, has caused other serious problems, including cardiovascular. There is still no explanation for the mechanisms of coronavirus that trigger dysfunction of the cardiac autonomic nervous system (ANS). We believe that the complex mechanisms that change the status of ANS could only be solved by advanced multidimensional analysis of many variables, obtained both from the original cardiovascular signals and from laboratory analysis and detailed patient history. The aim of this paper is to analyze different measures of entropy as potential dimensions of the multidimensional space of cardiovascular data. The measures were applied to heart rate and systolic blood pressure signals collected from 116 patients with COVID-19 and 77 healthy controls. Methods that indicate a statistically significant difference between patients with different levels of infection and healthy controls will be used for further multivariate research. As a result, it was shown that a statistically significant difference between healthy controls and patients with COVID-19 was shown by sample entropy applied to integrated transformed probability signals, common symbolic dynamics entropy, and copula parameters. Statistical significance between serious and mild patients with COVID-19 can only be achieved by cross-entropies of heart rate signals and systolic pressure. This result contributes to the hypothesis that the severity of COVID-19 disease is associated with ANS disorder and encourages further research.

On Quantization Errors in Approximate and Sample Entropy.

Approximate and sample entropies are acclaimed tools for quantifying the regularity and unpredictability of time series. This paper analyses the causes of their inconsistencies. It is shown that the major problem is a coarse quantization of matching probabilities, causing a large error between their estimated and true values. Error distribution is symmetric, so in sample entropy, where matching probabilities are directly summed, errors cancel each other. In approximate entropy, errors are accumulating, as sums involve logarithms of matching probabilities. Increasing the time series length increases the number of quantization levels, and errors in entropy disappear both in approximate and in sample entropies. The distribution of time series also affects the errors. If it is asymmetric, the matching probabilities are asymmetric as well, so the matching probability errors cease to be mutually canceled and cause a persistent entropy error. Despite the accepted opinion, the influence of self-matching is marginal as it just shifts the error distribution along the error axis by the matching probability quant. Artificial lengthening the time series by interpolation, on the other hand, induces large error as interpolated samples are statistically dependent and destroy the level of unpredictability that is inherent to the original signal.

Deficiency in insulin-like growth factors signalling in mouse Leydig cells increase conversion of testosterone to estradiol because of feminization.

AIM: A growing body of evidence pointed correlation between insulin-resistance, testosterone level and infertility, but there is scarce information about mechanisms. The aim of this study was to identify the possible mechanism linking the insulin-resistance with testosterone-producing-Leydig-cells functionality. METHODS: We applied in vivo and in vitro approaches. The in vivo model of functional genomics is represented by INSR/IGF1R-deficient-testosterone-producing Leydig cells obtained from the prepubertal (P21) and adult (P80) male mice with insulin + IGF1-receptors deletion in steroidogenic cells (Insr/Igf1r-DKO). The in vitro model of INSR/IGF1R-deficient-cell was mimicked by blockade of insulin/IGF1-receptors on the primary culture of P21 and P80 Leydig cells. RESULTS: Leydig-cell-specific-insulin-resistance induce the development of estrogenic characteristics of progenitor Leydig cells in prepubertal mice and mature Leydig cells in adult mice, followed with a dramatic reduction of androgen phenotype. Level of androgens in serum, testes and Leydig cells decrease as a consequence of the dramatic reduction of steroidogenic capacity and activity as well as all functional markers of Leydig cell. Oppositely, the markers for female-steroidogenic-cell differentiation and function increase. The physiological significances are the higher level of testosterone-to-estradiol-conversion in double-knock-out-mice of both ages and few spermatozoa in adults. Intriguingly, the transcription of pro-male sexual differentiation markers Sry/Sox9 increased in P21-Leydig-cells, questioning the current view about the antagonistic genetic programs underlying gonadal sex determination. CONCLUSION: The results provide new molecular mechanisms leading to the development of the female phenotype in Leydig cells from Insr/Igf1r-DKO mice and could help to better understand the correlation between insulin resistance, testosterone and male (in)fertility.

On Entropy of Probability Integral Transformed Time Series.

The goal of this paper is to investigate the changes of entropy estimates when the amplitude distribution of the time series is equalized using the probability integral transformation. The data we analyzed were with known properties-pseudo-random signals with known distributions, mutually coupled using statistical or deterministic methods that include generators of statistically dependent distributions, linear and non-linear transforms, and deterministic chaos. The signal pairs were coupled using a correlation coefficient ranging from zero to one. The dependence of the signal samples is achieved by moving average filter and non-linear equations. The applied coupling methods are checked using statistical tests for correlation. The changes in signal regularity are checked by a multifractal spectrum. The probability integral transformation is then applied to cardiovascular time series-systolic blood pressure and pulse interval-acquired from the laboratory animals and represented the results of entropy estimations. We derived an expression for the reference value of entropy in the probability integral transformed signals. We also experimentally evaluated the reliability of entropy estimates concerning the matching probabilities.

Emerging Wireless Sensor Networks and Internet of Things Technologies-Foundations of Smart Healthcare.

Future smart healthcare systems - often referred to as Internet of Medical Things (IoMT) - will combine a plethora of wireless devices and applications that use wireless communication technologies to enable the exchange of healthcare data. Smart healthcare requires sufficient bandwidth, reliable and secure communication links, energy-efficient operations, and Quality of Service (QoS) support. The integration of Internet of Things (IoT) solutions into healthcare systems can significantly increase intelligence, flexibility, and interoperability. This work provides an extensive survey on emerging IoT communication standards and technologies suitable for smart healthcare applications. A particular emphasis has been given to low-power wireless technologies as a key enabler for energy-efficient IoT-based healthcare systems. Major challenges in privacy and security are also discussed. A particular attention is devoted to crowdsourcing/crowdsensing, envisaged as tools for the rapid collection of massive quantities of medical data. Finally, open research challenges and future perspectives of IoMT are presented.

A Mobile Crowd Sensing Application for Hypertensive Patients.

Mobile crowd sensing (MCS) is an application that collects data from a network of conscientious volunteers and implements it for the common or personal benefit. This contribution proposes an implementation that collects the data from hypertensive patients, thus creating an experimental database using the cloud service Platform as a Service (PaaS). The challenge is to perform the analysis without the main diagnostic feature for hypertension-the blood pressure. The other problems consider the data reliability in an environment full of artifacts and with limited bandwidth and battery resources. In order to motivate the MCS volunteers, a feedback about the patient's current status is created, provided by the means of machine-learning (ML) techniques. Two techniques are investigated and the Random Forest algorithm yielded the best results. The proposed platform, with slight modifications, can be adapted to the patients with other cardiovascular problems.

Cardiovascular variability and ß-ARs gene expression at two stages of doxorubicin - Induced cardiomyopathy.

Using comprehensive analysis of heart rate (HRV) and blood pressure (BPV) short-term variability we estimated the time course of changes of autonomic nervous system remodeling in two stages of doxorubicin-induced cardiomyopathy (DCM). We also investigated the level of gene expression of cardiac ß-1 (ß-1AR) and ß-2 (ß-2AR) adrenoceptors. Experiments were performed in adult male Wistar rats equipped with indwelling catheters for BP recording and blood withdrawal. A 15 mg/kg total cumulative dose of doxorubicin was injected i.p. to rats to induce DCM or saline for control (n=18). Rats were assessed for general toxicity, cardiovascular hemodynamic and echocardiography before treatment (n=6), 35 days (DOX35; n=6) and 70 days (DOX70; n=6) post-treatment. HRV was evaluated by spectral analysis, Poincaré plots, sample and approximate entropy. Expression of ß-1AR and ß-2AR mRNA was evaluated by RT-qPCR. Doxorubicin-treated rats exhibited poor general condition and lower survival than saline-treated rats. In DOX35 rats, there were no echocardiography signs of decompensation, no increase in serum cardiac troponins, but there was an increase of HRV and decrease of HR complexity. In these rats typical microscopic signs of cardiotoxicity were seen along with over-expression of ß-1AR mRNA. 70 days post-treatment echocardiography revealed signs of decompensation and serum cardiac troponin T was increased. At this stage BPV decreased. In conclusion, HRV increase matches transient over-expression of cardiac ß-1AR mRNA in compensate stage of DCM while decompensate stage of DCM is characterized by a decrease of BPV and no changes in ß-1AR and ß-2AR gene expression.

A novel approach to probabilistic characterisation of neural firing patterns.

BACKGROUND: The advances in extracellular neural recording techniques result in big data volumes that necessitate fast, reliable, and automatic identification of statistically similar units. This study proposes a single framework yielding a compact set of probabilistic descriptors that characterise the firing patterns of a single unit. NEW METHOD: Probabilistic features are estimated from an inter-spike-interval time series, without assumptions about the firing distribution or the stationarity. The first level of proposed firing patterns decomposition divides the inter-spike intervals into bursting, moderate and idle firing modes, yielding a coarse feature set. The second level identifies the successive bursting spikes, or the spiking acceleration/deceleration in the moderate firing mode, yielding a refined feature set. The features are estimated from simulated data and from experimental recordings from the lateral prefrontal cortex in awake, behaving rhesus monkeys. RESULTS: An efficient and stable partitioning of neural units is provided by the ensemble evidence accumulation clustering. The possibility of selecting the number of clusters and choosing among coarse and refined feature sets provides an opportunity to explore and compare different data partitions. CONCLUSIONS: The estimation of features, if applied to a single unit, can serve as a tool for the firing analysis, observing either overall spiking activity or the periods of interest in trial-to-trial recordings. If applied to massively parallel recordings, it additionally serves as an input to the clustering procedure, with the potential to compare the functional properties of various brain structures and to link the types of neural cells to the particular behavioural states.

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.

Dependency Structures in Differentially Coded Cardiovascular Time Series.

Objectives. This paper analyses temporal dependency in the time series recorded from aging rats, the healthy ones and those with early developed hypertension. The aim is to explore effects of age and hypertension on mutual sample relationship along the time axis. Methods. A copula method is applied to raw and to differentially coded signals. The latter ones were additionally binary encoded for a joint conditional entropy application. The signals were recorded from freely moving male Wistar rats and from spontaneous hypertensive rats, aged 3 months and 12 months. Results. The highest level of comonotonic behavior of pulse interval with respect to systolic blood pressure is observed at time lags τ = 0, 3, and 4, while a strong counter-monotonic behavior occurs at time lags τ = 1 and 2. Conclusion. Dynamic range of aging rats is considerably reduced in hypertensive groups. Conditional entropy of systolic blood pressure signal, compared to unconditional, shows an increased level of discrepancy, except for a time lag 1, where the equality is preserved in spite of the memory of differential coder. The antiparallel streams play an important role at single beat time lag.

Binarized cross-approximate entropy in crowdsensing environment.

OBJECTIVES: Personalised monitoring in health applications has been recognised as part of the mobile crowdsensing concept, where subjects equipped with sensors extract information and share them for personal or common benefit. Limited transmission resources impose the use of local analyses methodology, but this approach is incompatible with analytical tools that require stationary and artefact-free data. This paper proposes a computationally efficient binarised cross-approximate entropy, referred to as (X)BinEn, for unsupervised cardiovascular signal processing in environments where energy and processor resources are limited. METHODS: The proposed method is a descendant of the cross-approximate entropy ((X)ApEn). It operates on binary, differentially encoded data series split into m-sized vectors. The Hamming distance is used as a distance measure, while a search for similarities is performed on the vector sets. The procedure is tested on rats under shaker and restraint stress, and compared to the existing (X)ApEn results. RESULTS: The number of processing operations is reduced. (X)BinEn captures entropy changes in a similar manner to (X)ApEn. The coding coarseness yields an adverse effect of reduced sensitivity, but it attenuates parameter inconsistency and binary bias. A special case of (X)BinEn is equivalent to Shannon's entropy. A binary conditional entropy for m =1 vectors is embedded into the (X)BinEn procedure. CONCLUSION: (X)BinEn can be applied to a single time series as an auto-entropy method, or to a pair of time series, as a cross-entropy method. Its low processing requirements makes it suitable for mobile, battery operated, self-attached sensing devices, with limited power and processor resources.

Time-frequency characterization of local field potential in a decision making task.

This study seeks to characterize the neuronal mechanisms underlying voluntary decisions to check/verify. In order to describe and potentially decode decisions from brain signals we analyzed intracortical recordings from monkey prefrontal regions obtained during a cognitive task requiring self-initiated as well as cue-instructed decisions. Using local field potentials (LFP) and single units, we analyzed power spectral density, oscillatory modes, power profiles in time, single unit firing rate, and spike-phase relationships in the ß band. Our results point toward specific but variable activation patterns of oscillations in ß band from separate recordings, with task-dependent frequency preference and amplitude modulation of power. The results suggest relationships between particular LFP oscillations and functions engaged at specific time in the task.

Blood pressure and pulse interval coupling: A copula approach.

In this paper a copula approach is applied as a tool for assessing the measure of statistical dependence of parallel cardiovascular time series. Families of Archimedean copulas (Clayton, Frank and Gumbel) are applied to pulse interval, systolic and diastolic blood pressure recorded from male Wistar rats at baseline conditions, and to their isodistributional surrogates with the same marginal, but randomized joint distribution functions. The influence of time offset of the parallel time series is explored. The amount of data required for a stable working point is discussed.

Unbiased entropy estimates in stress: a parameter study.

This study proposes a method for obtaining a stable working point that enables the unbiased estimates of the approximate entropy and the sample entropy. Pattern length, normalized threshold, time delay and tines series length are levels of freedom that are considered. Pulse interval signals used for the experiment are recorded from laboratory animals with different likelihood of developing cardiovascular disease--normal and borderline hypertensive rats--exposed to the acute and to the chronic stress. It is shown that the threshold level is the major source of the instability, and that the generally accepted and widely used methods for a threshold choice may lead to an incorrect psychological interpretation. A method for the threshold level correction is proposed.

Autonomic mechanisms underpinning the stress response in borderline hypertensive rats.

This study investigates blood pressure (BP) and heart rate (HR) short-term variability and spontaneous baroreflex functioning in adult borderline hypertensive rats and normotensive control animals kept on normal-salt diet. Arterial pulse pressure was recorded by radio telemetry. Systolic BP, diastolic BP and HR variabilities and baroreflex were assessed by spectral analysis and the sequence method, respectively. In all experimental conditions (baseline and stress), borderline hypertensive rats exhibited higher BP, increased baroreflex sensitivity and resetting, relative to control animals. Acute shaker stress (single exposure to 200 cycles min-1 shaking platform) increased BP in both strains, while chronic shaker stress (3-day exposure to shaking platform) increased systolic BP in borderline hypertensive rats alone. Low- and high-frequency HR variability increased only in control animals in response to acute and chronic shaker (single exposure to restrainer) stress. Acute restraint stress increased BP, HR, low- and high-frequency variability of BP and HR in both strains to a greater extent than acute shaker stress. Only normotensive rats exhibited a reduced ratio of low- to high-frequency HR variability, pointing to domination of vagal cardiac control. In borderline hypertensive rats, but not in control animals, chronic restraint stress (9-day exposure to restrainer) increased low- and high-frequency BP and HR variability and their ratio, indicating a shift towards sympathetic cardiovascular control. It is concluded that maintenance of BP in borderline hypertensive rats in basal conditions and during stress is associated with enhanced baroreflex sensitivity and resetting. Imbalance in sympathovagal control was evident only during exposure of borderline hypertensive rats to stressors.

Temporal sequence parameters in isodistributional surrogate data: model and exact expressions.

In this paper, a set of formulae for the temporal spontaneous baroreceptor reflex (sBRR) sequence parameters in isodistributional (ID) surrogate data is derived. This is facilitated by representing successive positive or negative amplitude changes as a Markov chain model. The obtained analytical tool measures the effect of random fluctuations on the overall number of sequences, estimated from the original biomedical time series. The formulae are tested using ID surrogates of systolic blood pressure and pulse-interval signals recorded from 13 healthy male Wistar rats at baseline conditions.

Temporal analysis of the spontaneous baroreceptor reflex during mild emotional stress in the rat.

The effect of emotional stress on the spontaneous baroreceptor reflex (sBRR) in freely moving rats was investigated. Six male Wistar rats equipped with an intra-arterial polyethylene catheter were exposed to a 2-min air-jet stress. For time course analysis of the sBRR response to stress, the records of systolic blood pressure (SBP) and pulse interval (PI) were divided into five regions: baseline (BASELINE), acute exposure to air-jet stress (STRESS), immediate recovery (IMMED. RECOVERY), remaining recovery (RECOVERY), and delayed response (DELAYED RESPONSE). In addition to sBRR sensitivity and effectiveness, we introduce the sequence coverage area and its median for evaluation of the sBRR operating range and set point. During exposure to STRESS and IMMED. RECOVERY, sBRR sensitivity was preserved, its effectiveness was decreased, its operating range was enlarged, and the set point was shifted towards higher SBP and lower PI values. According to the joint symbolic dynamics analysis, the SBP and PI relationship became less predictable hence more prone to respond to stress. In RECOVERY the parameters regained baseline values and DELAYED RESPONSE occurred during which re-setting of sBRR was noted. It follows that emotional stress modulates sBRR differentially during the time course of stress and recovery, affecting both linearity and unpredictability of the BP and PI relationship.