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The cortical and subcortical circuit regulating both cognition and cardiac autonomic interactions are already well established. This circuit has mainly been analyzed from cortex to heart. Thus, the heart rate variability (HRV) is usually considered a reflection of cortical activity. In this paper, we investigate whether HRV changes affect cortical activity. Short-term local autonomic changes were induced by three breathing strategies: spontaneous (Control), normal (NB) and slow paced breathing (SB). We measured the performance in two cognition domains: executive functions and processing speed. Breathing maneuvres produced three clearly differentiated autonomic states, which preconditioned the cognitive tasks. We found that the SB significantly increased the HRV low frequency (LF) power and lowered the power spectral density (PSD) peak to 0.1Hz. Meanwhile, executive function was assessed by the working memory test, whose accuracy significantly improved after SB, with no significant changes in the response times. Processing speed was assessed by a multitasking test. Consistently, the proportion of correct answers (success rate) was the only dependent variable affected by short-term and long-term breath pacing. These findings suggest that accuracy, and not timing of these two cognitive domains would benefit from short-term SB in this study population.

Evidence of discrete scale invariance (DSI) in daytime healthy heart rate variability (HRV) is presented based on the log-periodic power law scaling of the heart beat interval increment. Our analysis suggests multiple DSI groups and a dynamic cascading process. A cascade model is presented to simulate such a property.

The purpose of this study is to suggest sound biofeedback, which is a new technique of early stress relief effect by observing change in the heart rate variability (HRV). The sound biofeedback imitating heart rate of the comfortable and stress state is termed parasympathetic stimulation sound (PSS) and sympathetic stimulation sound (SSS), respectively. Twelve subjects were selected without previous history of cardiovascular diseases and mental illness, such as arrhythmia, myocardial infarction, depression and panic disorder. To confirm the changes in the low-frequency (LF), high-frequency (HF) and LF/HF values of HRV as stress evaluation indicators, the HRV of subjects was measured by photoplethysmogram. Signals were processed using the peak detect algorithm, and fast Fourier transform. Results were obtained using power specific densities. During the PSS stimulation, the LF/HF tended to decrease generally. On the other hand, during the SSS stimulation, LF/HF tended to increase. The LF/HF Mean change value ($P<0.05$) using the PSS stimulation is similar to the effect of Transcutaneous Vagal Nerve Stimulation (tVNS). In addition, the quantitative effect of sound biofeedback was confirmed by judging changes in the parasympathetic and sympathetic nerves in the autonomic nervous system (ANS) through $Z$-score normalized data. These experimental results suggest that sound biofeedback has the same stimulation location and clinical applicability as tVNS. As a result, sound biofeedback may be used as a new method for stress reduction.

Rigorous clinical evaluation of the physiological performance is currently performed with complex and long procedures which need expensive technology and skilled operators. In a wide range of situations (frail patients, daily clinical practice, etc.), these approaches are difficult to be applied and simpler tests, with a lack of scientific background, are mandatory. To avoid these problems, we propose a test (test of physiological performance (TOPP)) to evaluate the physiological behavior of a subject, in a really easy and safe clinical setting, measuring only the heart rate. The subject is submitted to an active standing-up test and then two submaximal exercises (with a low power load) on a cycle-ergometer. The heart rate modifications due to each submaximal step are analyzed by exponential interpolation to calculate the ascending and descending time constants and evaluate the way each subject adapts his heart rate to work. The standard deviation of the RR for each stationary phase (warm-up, load, recovery) was calculated as an index of short-term variability. Then a standard Fourier analysis of the stationary periods of the standing-up procedures allows to quickly and easily evaluate the autonomic nervous activation. We tested the protocol on five healthy subjects to verify the feasibility and the acceptance of the procedure. The five subjects demonstrated a good tolerance of the entire procedure. The standing-up showed a behavior of the autonomic system consistent with the physiology (with an increase in sympathetic activation in the passage to standing position). The analysis of the two submaximal steps highlights how younger and trained subjects present lower heart rates (both in the ascending phase and in the recovery) with a quicker adaptation ability (smaller time constants) consistent with what is expected. The short-term variability of heart rate is greater in young and trained subjects, thus confirming how the sympatho-vagal balance, in these subjects, is more dynamic. The proposed test is well tolerated by the subjects and the results, albeit in a small cohort of healthy volunteers, are consistent with what is expected from physiology and is already present in the literature. Our work aims to be a proposal with a feasibility check of a method for evaluating performance. The work to be done for the clinical validation of the TOPP is still long, but we are aware that it can give important results and that the TOPP can become an effective tool for the assessment of the physiological performance even of fragile subjects.

In the present study, "obstructive sleep apnea (OSA) patients" and "non-OSA patients" were classified into two groups using with two synchronic heart rate variability (HRV) series obtained from electrocardiography (ECG) and photoplethysmography (PPG) signals. A linear synchronization method called cross power spectrum density (CPSD), commonly used on HRV series, was performed to obtain high-quality signal features to discriminate OSA from controls. To classify simultaneous sleep ECG and PPG signals recorded from OSA and non-OSA patients, various feed forward neural network (FFNN) architectures are used and mean relative absolute error (MRAE) is applied on FFNN results to show affectivities of developed algorithm. The FFNN architectures were trained with various numbers of neurons and hidden layers. The results show that HRV synchronization is directly related to sleep respiratory signals. The CPSD of the HRV series can confirm the clinical diagnosis; both groups determined by an expert physician can be 99% truly classified as a single hidden-layer FFNN structure with 0.0623 MRAE, in which the maximum and phase values of the CPSD curve are assigned as two features. In future work, features taken from different physiological signals can be added to define a single feature that can classify apnea without error.