Anxiety, Worry and Fear: Quantifying the Mind Using EKG Time Series Analysis

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

mDFA Detects Abnormality: From Heartbeat to Material Vibration

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

Isolated Crayfish Stretch Receptor Neuron Electrophysiology May Explain a Longstanding Mystery of Human Brain Functioning: Eureka Moment

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

Invisible Emotion, Anxiety and Fear: Quantifying the Mind Using EKG with mDFA

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 long-time 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

Monitoring Heart Health and Structural Health: mDFA Quantification

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

Quantifying Stress Using mDFA: Heartbeats Exhibit Stress/Fear/Anxiety in Animal Model and Humans

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

A Biomedical Computation Revealed that an Extra-Systolic Heartbeat Exhibits a Lower Scaling Exponent: DFA as a Beneficial Biomedical Tool

We made our own DFA (detrended fluctuation analysis) program. We applied it for checking characteristics for the heartbeat of various individuals. Healthy subjects showed a normal scaling exponent, which is near 1.0 (ranging 0.9 to 1.19 in our own temporary guideline). This is in agreement with the original report by Peng et al. long time ago. In the present study, we investigated the person who has an extra-systole heartbeat, and revealed that their arrhythmic heartbeat exhibited a low scaling exponent (around 0.7). Alternans, which is the heart beating in period-2 rhythms, exhibited a much low scaling exponent (around 0.6). We may conclude that if it would be possible to make a device that equips a DFA program, it might be useful to check the heart condition, and contribute not only in nonlinear physics but also in biomedical fields; especially as a device for health check, which is applicable for people who are spending an ordinary life, before they get seriously heart sick