One Heartbeat At A Time Mp3 Download

The healthy heartbeat is traditionally thought to be regulated according to the classical principle of homeostasis whereby physiologic systems operate to reduce variability and achieve an equilibrium-like state [Physiol. Rev. 9, 399-431 (1929)]. However, recent studies [Phys. Rev. Lett. 70, 1343-1346 (1993); Fractals in Biology and Medicine (Birkhauser-Verlag, Basel, 1994), pp. 55-65] reveal that under normal conditions, beat-to-beat fluctuations in heart rate display the kind of long-range correlations typically exhibited by dynamical systems far from equilibrium [Phys. Rev. Lett. 59, 381-384 (1987)]. In contrast, heart rate time series from patients with severe congestive heart failure show a breakdown of this long-range correlation behavior. We describe a new method--detrended fluctuation analysis (DFA)--for quantifying this correlation property in non-stationary physiological time series. Application of this technique shows evidence for a crossover phenomenon associated with a change in short and long-range scaling exponents. This method may be of use in distinguishing healthy from pathologic data sets based on differences in these scaling properties.

Time perception typically has been tested over longer intervals, when research has shown that thoughts and emotions may distort our sense time, perhaps making it fly or crawl. Sadeghi and Anderson recently reported, for example, that crowding made a simulated train ride seem to pass more slowly.

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The scholars said the connection between time perception and the heart suggests our momentary perception of time is rooted in bioenergetics, helping the brain manage effort and resources based on changing body states including heart rate.

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Emotion recognition through computational modeling and analysis of physiological signals has been widely investigated in the last decade. Most of the proposed emotion recognition systems require relatively long-time series of multivariate records and do not provide accurate real-time characterizations using short-time series. To overcome these limitations, we propose a novel personalized probabilistic framework able to characterize the emotional state of a subject through the analysis of heartbeat dynamics exclusively. The study includes thirty subjects presented with a set of standardized images gathered from the international affective picture system, alternating levels of arousal and valence. Due to the intrinsic nonlinearity and nonstationarity of the RR interval series, a specific point-process model was devised for instantaneous identification considering autoregressive nonlinearities up to the third-order according to the Wiener-Volterra representation, thus tracking very fast stimulus-response changes. Features from the instantaneous spectrum and bispectrum, as well as the dominant Lyapunov exponent, were extracted and considered as input features to a support vector machine for classification. Results, estimating emotions each 10 seconds, achieve an overall accuracy in recognizing four emotional states based on the circumplex model of affect of 79.29%, with 79.15% on the valence axis and 83.55% on the arousal axis.

The detection and recognition of emotional information is an important topic in the field of affective computing, i.e. the study of human affects by technological systems and devices1. Changes in emotional states often reflect facial, vocal and gestural modifications in order to communicate, sometimes sub-unconsciously, personal feelings to other people. Such changes can be generalized across cultures, e.g. nonverbal emotional, or can be culture-specific2. Since mood alteration strongly affects the normal emotional process, emotion recognition is also an ambitious objective in the field of mood disorder psychopathology. In the last decade, several efforts have tried to obtain a reliable methodology to automatically identify the emotional/mood state of a subject, starting from the analysis of facial expressions, behavioral correlates and physiological signals. Despite such efforts, current practices still use simple mood questionnaires or interviews for emotional assessment. In mental care, for instance, the diagnosis of pathological emotional fluctuations is mainly made through the physician's experience. Several epidemiological studies report that more than two million Americans have been diagnosed with bipolar disorder3 and about 82.7 million of the adult European population from 18 to 65 years of age, have been affected by at least one mental disorder4. Several computational methods for emotion recognition based on variables associated with the Central Nervous System (CNS), for example the Electroencephalogram (EEG), have been recently proposed5,6,7,8,9,10,11,12. These methods are justified by the fact that human emotions originate in the cerebral cortex involving several areas for their regulation and feeling. The prefrontal cortex and amygdala, in fact, represent the essence of two specific pathways: affective elicitations longer than 6 seconds allow the prefrontal cortex to encode the stimulus information and transmit it to other areas of the Central Autonomic Network (CAN) to the brainstem, thus producing a context appropriate response13; briefly presented stimuli access the fast route of emotion recognition via the amygdala. Of note, it has been found that the visual cortex is involved in emotional reactions to different classes of stimuli14. Dysfunctions on these CNS recruitment circuits lead to pathological effects15 such as anhedonia, i.e. the loss of pleasure or interest in previously rewarding stimuli, which is a core feature of major depression and other serious mood disorders.

Experimental evidence over the past two decades shows that Heart Rate Variability (HRV) analysis, in both time and frequency domain, can provide a unique, noninvasive assessment of autonomic function24,25,88. Nevertheless, HRV analysis by means of standard procedures presents several limitations when high time and frequency resolutions are needed, due mainly to associated inherent assumptions of stationarity required to define most of the relevant HRV time and frequency domain indices24,25. More importantly, standard methods are generally not suitable to provide accurate nonlinear measures in the absence of information regarding phase space fitting. It has been well-accepted by the scientific community that physiological models should be nonlinear in order to thoroughly describe the characteristics of such complex systems. Within the cardiovascular system, the complex and nonstationary dynamics of heartbeat variations have been associated to nonlinear neural interactions and integrations occurring at the neuron and receptor levels, so that the sinoatrial node responds in a nonlinear way to the changing levels of efferent autonomic inputs27. In fact, HRV nonlinear measures have been demonstrated to be of prognostic value in aging and diseases24,25,26,28,29,30,31,32,33,34,35,36,41. In several previous works37,38,39,40,41,42,43, we have demonstrated how it is possible to estimate heartbeat dynamics in cardiovascular recordings under nonstationary conditions by means of the analysis of the probabilistic generative mechanism of the heartbeat. Concerning emotion recognition, we recently demonstrated the important role of nonlinear dynamics for a correct arousal and valence recognition from ANS signals44,45,46,71 including a preliminary feasibility study on the dataset considered here47.

In the light of all these issues, we here propose a new methodology in the field of affective computing, able to recognize emotional swings (positive or negative), as well as two levels of arousal and valence (low-medium and medium-high), using only one biosignal, the ECG and able to instantaneously assess the subject's state even in short-time events (

The y axis relates to the official IAPS score, whereas the x axis relates to the time. The neutral sessions, which are marked with blue lines, alternate with the arousal ones, which are marked with red staircases. Along the time, the red line follows the four arousal sessions having increasing intensity of activation. The dotted green line indicates the valence levels distinguishing the low-medium (L-M) and the medium-high (M-H) level within an arousing session. The neutral sessions are characterized by lowest arousal (

The autonomic nervous system acts on the cardiovascular system modulating its electrical activity. This activity affects the heartbeat dynamics, which can be non-invasively revealed by the analysis and modeling of the RR interval series. To perform this task, we propose to consider a point-process probability density function in order to characterize cardiovascular dynamics at each moment in time. In particular, we use Wiener-Volterra nonlinear autoregressive integrative functions to estimate quantitative tools such as spectrum and bispectrum from the linear and nonlinear terms, respectively. Given the instantaneous spectra and high-order spectra, several features are combined to define the feature set, which is the input of the personalized pattern recognition procedure. Support vector machines are engaged to perform this task by adopting a leave-one-out procedure.

The ECG signal was analyzed off-line to extract the RR intervals24, then further processed to correct for erroneous and ectopic beats by a previously developed algorithm52. The presence of nonlinear behaviors in such heartbeat series was tested by using a well-established time-domain test based on high-order statistics53. The null hypothesis assumes that the time series are generated by a linear system. We set the number of laps to M = 8 and a total of 500 bootstrap replications for every test. Experimental results are shown in Table 1. The nonlinearity test gave significant results (p < 0.05) on 27 out of 30 subjects. In light of this result, we based our methodology on Nonlinear Autoregressive Integrative (NARI) models. Nonlinearities are intended as quadratic and cubic functions of the past RR intervals according to the Wiener-Volterra representation54,55. Major improvements of our approach rely on the possibility of performing a regression on the derivative RR series based on an Inverse Gaussian (IG) probability structure37,38,39. The quadratic nonlinearities contribute to the complete emotional assessment through features coming from the instantaneous spectrum and bispectrum56,57. It is worthwhile noticing that our feature estimation is derived from an equivalent nth-order inputoutput Wiener-Volterra model54,55, thus allowing for the potential estimation of the nth-order polyspectra of the physiological signal60 (see Materials and Methods section for details). Moreover, by representing the RR series with cubic autoregressive functions, it is possible to perform a further instantaneous nonlinear assessment of the complex cardiovascular dynamics and estimate the dominant Lyapunov exponent at each moment in time61. Indices from a representative subject are shown in Fig. 5. Importantly, the NARI model as applied to the considered data provides excellent results in terms of goodness-of-fit and independence test, with KS distances never above 0.056. A comparison analysis was performed between the simple linear and NARI models considering the Sum of the Squared Distances (SSD) of the points outside the confidence interval of the autocorrelation plot (see Table 1). We report that nonlinear point-process models resulted in lower SSD on all the considered subjects. Further results reporting the number of points outside the confidence interval of the autocorrelation plot are shown in the Supporting Information. e24fc04721