Two kinds of nerves, acceleratory and inhibitory cardio-regulator nerves, innervate the heart. They are known to discharge concurrently to maintain an equilibrium state of the body. The nerves are also known to change their frequency of discharge in a reflexive manner to meet the demand from the periphery; such as augmentation of oxygen supply or vice versa. Consequently, the heart exhibits dynamic change in its pumping rate and force of contraction. If the control system fails, the heart exhibits unhealthy state. However, assessment of healthy/unhealthy status is uneasy because we are not able to monitor the nerve activities by non-invasive methods. Therefore, we challenged to detect state of the heart without nerve-recordings. We used the detrended fluctuation analysis (DFA) applying to heartbeat interval time series because DFA has been believed that it can quantify the state of heart. We performed DFA on the EKGs (electrocardiograms) from various living organisms including humans. The objective of this research was to determine whether the analytical technology, DFA, could function as a useful method for the evaluation of the subject’s quality of cardiovascular-related illness and transition to and from a normal healthy state. We found that DFA could describe brain-heart interaction quantitatively: the scaling exponents of (1) healthy, (2) sick-type (such as stressful or arrhythmic states), and (3) unpredictable-death type (such as ischemic heart disease) were corresponded to individuals who exhibited, (1) nearly one, (2) less than one, and (3) greater than one, respectively. We conclude that scaling exponents could determine whether the subjects are under sick or healthy conditions on the basis of cardiac physiology.

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