Early afterdepolarizations (EADs) are spontaneous oscillations in membrane potential that occur during the repolarization phase of the action potential. EADs can trigger ventricular arrhythmias, such as Torsades de Pointes, in patients with long QT syndromes. Understanding the theoretical mechanisms behind EAD generation and developing strategies to suppress them are crucial. In this study, we employed bifurcation analysis along with a new fast-slow decomposition method on the O'Hara model of human ventricular myocytes. Our goal was to examine how the calcium ion concentration in the network sarcoplasmic reticulum (NSR) influences the generation of EADs in the context of reduced rapid delayed rectifier K + current. Our findings identified nine distinct EAD states that coexist and can be controlled by slight adjustments to the NSR calcium ion concentration at a single time point.

Neural network of our brain is complex, but single-neuron physiology is still important to understand the higher brain function. While conducting electrophysiological experiments using the isolated crayfish stretch receptor neuron, a phenomenon which may explain a longstanding mystery of human brain functioning, Eureka moment, was found. In this article, we demonstrate electro-physiologically GABAergic inhibitory synapses contribute for “switching” and propose a novel idea that can explain how sudden switching occurs in the brain.

This study provides evidence that a time series analysis (modified detrended fluctuation analysis, mDFA) is practically distinguish happy- and stressed hearts. This endures that the scaling exponent (scaling index, SI, or alpha, α) can characterize the state of heartbeats. We learned from various challenges of case studies; for example, the Wolff–Parkinson–White syndrome yields a high SI (way surpass 2.0) while feeling sick condition, but the same heart exhibits a healthy SI (∼1.0) when the heartbeats return to normal. Meantime, a healthy SI (∼1.0) goes down to a low SI (0.7) when truly enjoying meal. It seems that SI can represent invisible internal world. The complex interaction between the cardiac rhythm and the autonomic brain command becomes perceptible by the SI. Our observations confirm the state of the heart is measurable quantitatively. A time series analysis of mDFA can help holistic understanding of the brain-heart axis.

Electrical activity occurs in the cell membrane of cardiomyocytes. This electrical activity forms the action potential that generates pumping of the heart. An abnormality in the action potential turns into arrhythmia, which may cause sudden death. Studies of arrhythmias using mathematical models are important to reduce the risk of sudden death. In this study, we investigate bifurcations related to the generation of early afterdepolarizations (EADs) in a mathematical model. We clarify the transition process from a normal state to a persistent EAD through a transient EAD while changing only one parameter (multiple of conductance of L-type calcium channel current) value. The dependence of the transient EAD generation on parameters is shown through bifurcation analysis in a [Na]i-parameterized system.

We study the intersection of double-flip (period-doubling) bifurcations in a parameter plane. We derive normal forms for discrete-time and continuous-time systems. Using these normal forms, we clarify the bifurcation structure around the flip-flip bifurcation point. We apply these analytical results to a system of coupled ventricular cell models. We determine the coexistence of in-phase and anti-phase two-periodic solutions. We make the simplest model for generating discordant alternans and clarify that two parameters (free concentration of potassium ions in the extracellular compartment and the conductance of the gap junction) play key roles in generating discordant alternans.

Pulsus alternans is a type of arrhythmia that shows alternate contraction forces of ventricular muscles and is a sign of cardiac complications. In this study, we investigate the parameter dependence of pulsus alternans generation using a mathematical crustacean cardiac cell model with automaticity. Specifically, we modified the parameter values in the model so that they resemble realistic waveforms of the membrane potential and then analyzed the modified model. We also conducted physiological experiment and observed pulsus alternans as irregular heartbeats. We reproduced the generation of pulsus alternans in the modified mathematical model. Numerical results showed that the conductance of the transient calcium current and the calcium-dependent potassium current for the small cell (pacemaker of crustacean heart) is the key to generating such pulsus alternans.

The cardio-vascular control system (CVCS) includes the heart, blood vessels, and neuronal/hormonal regulating systems. Ontogenetically and evolutionally, CVCS is designed, implemented and maintained by multi-cellular components. To endure proper operation as a mixture of different type of cells, CVCS functioning is automated with complex interaction with each other. When a certain state of CVCS becomes a malfunctioned state, physicians acknowledge that CVCS’s sickness might get started even the malfunctioned state is acute and temporary. However, it is not easy to quantify the state of CVCS. Using the scaling exponent (SI, scaling index), we have recently introduced a novel health technology to check CVCS’s state, which is “modified detrended fluctuation analysis (mDFA)”. mDFA-method simply calculates SI based on the electrocardiogram data. If our health wellness conditions are practically healthy, SI is nearly 1.0. If we bear a stressful condition, the value of SI decreases toward to 0.5. Intriguingly, if we would be at risk, for example, we are approaching unpredictable cessation of heart-pumping, we found that SI increase toward 1.5. This mDFA-rule is beneficial and applicable to “hearted” animals, from crustaceans to humans. Here we propose that mDFA can distinguish between healthiness and sickness of CVCS.

Modified detrended fluctuation analysis (mDFA) is a novel method to check abnormality of heartbeat which is developed recently by the author. mDFA can characterize any oscillation such as heartbeat by the scaling exponent (scaling index, SI). Healthy heartbeat shows SI = 1. Dying heart's SI sifts toward 0.5. Ischemic sick heart experimentally showed an SI way over 1.0 approaching 1.5. Random vibration, such as FM-radio noise and idling car-engine, shows SI = 0.5. Quietly running motor generates an SI almost equal to zero. Using mDFA, it is possible to check potential risk based on SI values. This chapter shows empirical results quantifying various signals from heartbeat to material vibration.

To quantify non-linear behaviorBehavior of physiologic systemSystem such as the cardiovascular control systemSystem , we first used lobsters because we are invertebrate neurobiologists. After finding that the lobsters can display its emotion by changing the pattern of heart-beating, we extended the method to human: The heartbeat represents momently varying emotional tensionTension . We show that this variation of the inner world is detectable and quantifiable using a long-time electrocardiogramElectrocardiogram (EKGEKG ). In each investigation, we captured 2000 heartbeats without missing a single beat. The heartbeat interval time series was analyzed by “modified detrended fluctuation analysisModified detrended fluctuation analysis (mDFAmDFA )” technique, which we have recently developed by our group. The mDFAmDFA calculates the scaling exponent (SI, scaling index). A normal healthy heartbeat exhibits an SI of around 1.0. The heartbeat recorded from subjects who have stress and anxietyAnxiety exhibited a lower SI. The values of SI changed one right after the other when circumstances and atmospheres surrounding subjects were changed. We report that the mDFAmDFA technique is a useful computation method for checking the mind and health.

We analyse the heartbeat interval time series in this chapter. Our time series analysis concepts and techniques have been reported previously, for example, in the Intech Book chapter. Here, we would like to introduce how it works by presenting typical examples. The techniques can distinguish between healthy, sick and stressful hearts. All data were obtained by us from natural heartbeat data. Therefore, we have notes behind data, especially about behavioural psychological observations. Results of analysis are the following: healthy hearts exhibit a healthy scaling exponent (SI), which is near 1.0, stressful hearts exhibit a lower SI, such as 0.7, dying heart’s SI approaches to 0.5, and so forth.

Fluctuation or variation of the heartbeat represents momently varying inner emotional tension. Can this psychological variations of the inner world, anxiety for example, is detectable and even quantifiable? Our answer to the question: Using a longtime electrocardiogram (EKG), we quantified them. We recorded EKGs by our own EKG amplifiers. The amplifier has a newly designed electric circuit, which enable us to record a stable EKG. The amplifier made it possible to record a perfect EKG where the EKG trace never jump-out from the PC monitor screen. Using this amplifier, we captured approximately 2000 heartbeats without missing a single beat. For the analysis of the EKGs, we used “modified detrended fluctuation analysis (mDFA)” technique, which we have recently developed by our group. The mDFA calculates the scaling exponent (SI, scaling index) from the time series data, i.e., the R-R interval time series data obtained from EKG. Detecting 2000 consecutive peaks, the mDFA can distinguish between a normal and an abnormal heart: a normal healthy heartbeat exhibits an SI of around 1.0, comparable to the fluctuations exemplified as the 1/f spectrum. The heartbeat recorded from subjects who have stress and anxiety exhibited a lower SI. Arrhythmic heartbeats and extra-systolic heartbeats both also exhibited a low SI ~0.7, for example. We propose that the mDFA technique is a useful computation method for checking health. The functional capabilities of various internal systems, such as the circulatory system and the autonomic nervous system, can be quantified by using mDFA.

The aim of this study was to make a method usable in an early detection of malfunction, e.g., abnormal vibration/fluctuation in recorded signals. We conducted experimentations of heart health and structural health monitoring. We collected natural world signals, e.g., heartbeat fluctuation and mechanical vibration. For the analysis, we used modified detrended fluctuation analysis (mDFA) method that we have made recently. mDFA calculated the scaling exponent (SI, the acronym SI is derived from the scaling indices) from the time series data, e.g., R-R interval time series obtained from electrocardiograms. In the present study, peaks were identified by our own method. In every single mDFA computation, we identified ∼2000 consecutive peaks from a data: “2000” was necessary number to conduct mDFA. mDFA was able to distinguish between normal and abnormal behaviors: Normal healthy hearts exhibited an SI around 1.0, which is a phenomena comparable to 1/f fluctuation. Job-related stressful hearts and extrasystolic hearts both exhibited a low SI such as 0.7. Normally running car’s vibration — recorded steering wheel vibration — exhibited an SI around 0.5, which is white noise like fluctuation. Normally spinning ball-bearings (BB) exhibited an SI around 0.1, which belongs to the anti-correlation phenomena. A malfunctioning BB showed an increased SI. At an SI value over 0.2, an inspector must check BB’s correct functioning. Here we propose that healthiness in various cyclic vibration behaviors can be quantitatively analyzed by mDFA.

The aim of this study was to make a method for an early detection of malfunction, e.g., abnormal vibration/fluctuation in recorded signals. We conducted experimentations of heart health and structural health monitoring. We collected natural signals, e.g., heartbeat fluctuation and mechanical vibration. For the analysis, we used modified detrended fluctuation analysis (mDFA) method that we have made recently. mDFA calculated the scaling exponent (SI) from the time series data, e.g., R-R interval time series obtained from electrocardiograms. In the present study, peaks were identified by our own method. In every single mDFA computation, we identified ~2000 consecutive peaks from a data: "2000" was necessary number to conduct mDFA. mDFA was able to distinguish between normal and abnormal behaviors: Normal healthy hearts exhibited an SI around 1.0, which is a phenomena comparable to 1/f fluctuation. Job-related stressful hearts and extrasystolic hearts both exhibited a low SI such as 0.7. Normally running car's vibration―recorded steering wheel vibration―exhibited an SI around 0.5, which is white noise like fluctuation. Normally spinning ball-bearings (BB) exhibited an SI around 0.1, which belongs to the anti-correlation phenomena. A malfunctioning BB showed an increased SI. At an SI value over 0.2, an inspector must check BB's correct functioning. Here we propose that healthiness in various cyclic vibration behaviors can be quantitatively analyzed by mDFA.

Electrical alternans is the alternating amplitude from beat to beat in the action potential of the cardiac cell. It has been associated with ventricular arrhythmias in many clinical studies; however, its dynamical mechanisms remain unknown. The reason is that we do not have realistic network models of the heart system. Recently, Yazawa clarified the network structure of the heart and the central nerve system in the crustacean heart. In this study, we construct a simple model of the heart system based on Yazawa's experimental data. Using this model, we clarify that two parameters (the conductance of sodium ions and free concentration of potassium ions in the extracellular compartment) play the key roles of generating alternans. In particular, we clarify that the inactivation gate of the time-independent potassium channel is the most important parameter. Moreover, interaction between the membrane potential and potassium ionic currents is significant for generating alternate rhythms. This result indicates that if the muscle cell has problems such as channelopathies, there is great risk of generating alternans.

Stress is a physiological reaction of an organism to an uncomfortable or unfamiliar physical or psychological stimulus. Stress-inducing stimuli trigger reflex behavior, which results from alteration in the activity of the autonomic nervous system (ANS) and hormones. Reflex behavior includes a heightened state of alertness and increased heart rate. Acute stress is a short-term response that lasts for seconds, minutes, or days. In this article, “stress” refers to acute stress.

“Stress” has not been fully defined in terms of neuroscience. But, it might be possible to quantify it, like body temperature. The aim of this study was to develop a method to quantify stress, fear and anxiety that has not been accomplished. In the present study, we present a method to quantify them using the biomedical vital information, i.e., the timing of heartbeat. Here electrocardiograms of both animal models and humans were analyzed by modified detrended fluctuation analysis (mDFA), which calculates a scaling exponent (SI) from the heartbeat interval time series. The SI was able to numerically distinguish between normal and abnormal hearts. SI values varied with heart conditions, i.e., healthy basal or stressful conditions. This study suggests that mDFA has potential as a practical method for the construction of a device for health management.