Autonomic nervous system (ANS) plays an important role in the regulation of the physiological processes of the human organism during normal and pathological conditions. Among the techniques used in its evaluation, the heart rate variability (HRV) has arising as a simple and non-invasive measure of the autonomic impulses, representing one of the most promising quantitative markers of the autonomic balance. The HRV describes the oscillations in the interval between consecutive heart beats (RR interval), as well as the oscillations between consecutive instantaneous heart rates. It is a measure that can be used to assess the ANS modulation under physiological conditions, such as wakefulness and sleep conditions, different body positions, physical training and also pathological conditions. Changes in the HRV patterns provide a sensible and advanced indicator of health involvements. Higher HRV is a signal of good adaptation and characterizes a health person with efficient autonomic mechanisms, while lower HRV is frequently an indicator of abnormal and insufficient adaptation of the autonomic nervous system, provoking poor patient's physiological function. Because of its importance as a marker that reflects the ANS activity on the sinus node and as a clinical instrument to assess and identify health involvements, this study reviews conceptual aspects of the HRV, measurement devices, filtering methods, indexes used in the HRV analyses, limitations in the use and clinical applications of the HRV.

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Basic notions of heart rate variability and its clinical applicability

The impact of estrogen exposure in preventing or treating cardiovascular disease is controversial. But it is clear that estrogen has important effects on vascular physiology and pathophysiology, with potential therapeutic implications. Therefore, the goal of this review is to summarize, using an integrated approach, current knowledge of the vascular effects of estrogen, both in humans and in experimental animals. Aspects of estrogen synthesis and receptors, as well as general mechanisms of estrogenic action are reviewed with an emphasis on issues particularly relevant to the vascular system. Recent understanding of the impact of estrogen on mitochondrial function suggests that the longer lifespan of women compared with men may depend in part on the ability of estrogen to decrease production of reactive oxygen species in mitochondria. Mechanisms by which estrogen increases endothelial vasodilator function, promotes angiogenesis, and modulates autonomic function are summarized. Key aspects of the relevant pathophysiology of inflammation, atherosclerosis, stroke, migraine, and thrombosis are reviewed concerning current knowledge of estrogenic effects. A number of emerging concepts are addressed throughout. These include the importance of estrogenic formulation and route of administration and the impact of genetic polymorphisms, either in estrogen receptors or in enzymes responsible for estrogen metabolism, on responsiveness to hormone treatment. The importance of local metabolism of estrogenic precursors and the impact of timing for initiation of treatment and its duration are also considered. Although consensus opinions are emphasized, controversial views are presented to stimulate future research.

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Vascular Actions of Estrogens: Functional Implications

Our previous studies demonstrated that premenopausal women have dominant vagal and subordinate sympathetic activity compared with age-matched men This study was designed to investigate the role of

https://www.physiology.org/doi/pdf/10.1152/ajpheart.00256.2003

Effects of estrogen on gender-related autonomic differences in humans

Emotional or physical stress triggers Tako-tsubo cardiomyopathy in postmenopausal females, which is characterized by an elevation of the ST segment in the electrocardiogram (ECG) and left ventricular apical ballooning in the left ventriculogram (LVG). Immobilization stress (IMO) of rats can reproduce these ECG and LVG changes, both of which are normalized by combined blockade of alpha- and beta-adrenoceptors. An increase of serum estrogen partially attenuated these cardiac changes. IMO induced a rapid activation of p44/p42 mitogen-activated protein kinase, followed by a transient upregulation of immediate early genes (IEG) in the coronary artery and myocardium. Blocking of both alpha- and beta-adrenoceptors eliminated the upregulation of IEG induced by stress, while alpha- or beta-agonists upregulated IEG in the perfused heart. Heat shock protein 70 was induced in the aorta, coronary artery, and the myocardium. Natriuretic peptide genes (ANP and BNP) were also upregulated in the myocardium. Sequential gene expression can be considered as an adaptive response to stress. Activation of alpha- or beta-adrenoceptors is the primary trigger of emotional stress-induced cardiac changes.

Emotional Stress-Induced Tako-tsubo Cardiomyopathy: Animal Model and Molecular Mechanism

To our knowledge, the relationship between all four endogenous female sex hormones and resting cardiac autonomic function has not been studied. The aim of the current study was to examine the association between the normal endogenous levels of oestrogen (17beta-oestradiol), progesterone, luteinising hormone and follicle-stimulating hormone and heart rate variability (HRV) during the menstrual cycle in young eumenorrheic women. Ten healthy, young, female subjects volunteered for this study. HRV and endogenous hormone levels were recorded at three phases of the menstrual cycle: menses (day 3.8 +/- 0.5), ovulation (day 15.8 +/- 0.7) and luteal (day 22.1 +/- 0.4) to ensure HRV recordings at times of low (menses) and high (ovulation and luteal) hormonal influence. Heart rate recordings were obtained from supine resting subjects and analysed on a Holter analysis system. Total power (TP, 0-1.0 Hz), low frequency (LF, 0.041-0.15 Hz), high frequency (HF, 0.15-0.80 Hz) and LF/HF components of HRV were examined. Despite a significantly greater HR at ovulation and normal cyclic variations in all endogenous sex hormone levels, no measure of HRV was significantly different between menstrual cycle phases. Significant correlations between oestrogen levels and absolute measures of HRV at ovulation were identified. The results of the current study demonstrated that the normal cyclic variations in endogenous sex hormone levels during the menstrual cycle were not significantly associated with changes in cardiac autonomic control as measured by HRV. Significant correlation between peak oestrogen levels and HRV measures at ovulation provided further support for the reported cardioprotective effects of oestrogen in healthy females.

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Heart rate variability and endogenous sex hormones during the menstrual cycle in young women.

We studied heart rate variability in 14 healthy women before and after oophorectomy compared with 14 matched women who underwent hysterectomy with ovarian conservation. Surgical menopause induced a decline in cardiac vagal modulation with a shift toward sympathetic hyperactivity. Recovery of the baseline condition after 3 months of estrogen replacement therapy in oophorectomized women suggests a role of estrogen in the autonomic nervous control of the cardiovascular system.

Evidence of a role of endogenous estrogen in the modulation of autonomic nervous system.

The stock market forecasting is one of the most challenging application of machine learning, as its historical data are naturally noisy and unstable. Most of the successful approaches act in a supervised manner, labeling training data as being of positive or negative moments of the market. However, training machine learning classifiers in such a way may suffer from over-fitting, since the market behavior depends on several external factors like other markets trends, political events, etc. In this paper, we aim at minimizing such problems by proposing an ensemble of reinforcement learning approaches which do not use annotations (i.e. market goes up or down) to learn, but rather learn how to maximize a return function over the training stage. In order to achieve this goal, we exploit a Q-learning agent trained several times with the same training data and investigate its ensemble behavior in important real-world stock markets. Experimental results in intraday trading indicate better performance than the conventional Buy-and-Hold strategy, which still behaves well in our setups. We also discuss qualitative and quantitative analyses of these results.

Multi-DQN: An ensemble of Deep Q-learning agents for stock market forecasting

In the last decade, market financial forecasting has attracted high interests amongst the researchers in pattern recognition. Usually, the data used for analysing the market, and then gamble on its future trend, are provided as time series; this aspect, along with the high fluctuation of this kind of data, cuts out the use of very efficient classification tools, very popular in the state of the art, like the well known convolutional neural networks &#x0028 CNNs &#x0029 models such as Inception, ResNet, AlexNet, and so on. This forces the researchers to train new tools from scratch. Such operations could be very time consuming. This paper exploits an ensemble of CNNs, trained over Gramian angular fields &#x0028 GAF &#x0029 images, generated from time series related to the Standard &#x0026 Poor &#x02BC s 500 index future; the aim is the prediction of the future trend of the U.S. market. A multi-resolution imaging approach is used to feed each CNN, enabling the analysis of different time intervals for a single observation. A simple trading system based on the ensemble forecaster is used to evaluate the quality of the proposed approach. Our method outperforms the buy-and-hold &#x0028 B &#x0026 H &#x0029 strategy in a time frame where the latter provides excellent returns. Both quantitative and qualitative results are provided.

Deep learning and time series-to-image encoding for financial forecasting

The adoption of computer-aided stock trading methods is gaining popularity in recent years, mainly because of their ability to process efficiently past information through machine learning to predict future market behavior. Several approaches have been proposed to this task, with the most effective ones using fusion of a pile of classifiers decisions to predict future stock values. However, using prices information in single supervised classifiers has proven to lead to poor results, mainly because market history is not enough to be an indicative of future market behavior. In this paper, we propose to tackle this issue by proposing a multi-layer and multi-ensemble stock trader. Our method starts by pre-processing data with hundreds of deep neural networks. Then, a reward-based classifier acts as a meta-learner to maximize profit and generate stock signals through different iterations. Finally, several metalearner trading decisions are fused in order to get a more robust trading strategy, using several trading agents to take a final decision. We validate the effectiveness of the approach in a real-world trading scenario, by extensively testing it on the Standard & Poor’s 500 future market and the J.P. Morgan and Microsoft stocks. Experimental results show that the proposed method clearly outperforms all the considered baselines (which still performs very well in the analysed period), and even the conventional Buy-and-Hold strategy, which replicates the market behaviour.

A multi-layer and multi-ensemble stock trader using deep learning and deep reinforcement learning

Peripheral vascular responses to acute administration of natural progesterone were studied in 12 postmenopausal women (mean ± SD age 50.3 ± 4.8 years) with no evidence of cardiovascular disease. According to a randomized, double-blind protocol, all subjects were given natural progesterone as a vaginal cream, able to produce a rapid peak and decay of plasma hormone concentrations, or matched placebo, with crossover after a 1-week washout period. Forearm blood flow and peak flow after ischemic stress (ml/100 ml/min), local vascular resistance (mm Hg/ml/100 ml/min), venous volume (ml/100 ml), and venous compliance (ml/100 ml/mm Hg) were measured by strain-gauge venous occlusion plethysmography at baseline and after progesterone or placebo administration. Plasma norepinephrine concentrations were determined by high-performance liquid chromatography with electrochemical detection. Progesterone sharply decreased forearm blood flow (p

Alessandro Sebastian Podda

Papers

Evidence of a role of endogenous estrogen in the modulation of autonomic nervous system.

Multi-DQN: An ensemble of Deep Q-learning agents for stock market forecasting

Deep learning and time series-to-image encoding for financial forecasting

A multi-layer and multi-ensemble stock trader using deep learning and deep reinforcement learning

Effects of acute administration of natural progesterone on peripheral vascular responsiveness in healthy postmenopausal women.