Toru Yazawa

By Toru Yazawa and Yukio Shimoda

Part of the book: Biomedical Engineering

By Toru Yazawa

Part of the book: Advances in Bioengineering

By Toru Yazawa

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.

Part of the book: Time Series Analysis and Applications

By Toru Yazawa and Shinji Omata

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.

Part of the book: Noise and Vibration Control

By Toru Yazawa

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.

Part of the book: Pertinent and Traditional Approaches Towards Fishery

By Toru Yazawa

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.

Part of the book: Time Series Analysis - Recent Advances, New Perspectives and Applications [Working title]