Journal of
Systemics, Cybernetics and Informatics
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ISSN: 1690-4524 (Online)


Peer Reviewed Journal via three different mandatory reviewing processes, since 2006, and, from September 2020, a fourth mandatory peer-editing has been added.

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Published by
The International Institute of Informatics and Cybernetics


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Academia.edu
(A Community of about 40.000.000 Academics)


Honorary Editorial Advisory Board's Chair
William Lesso (1931-2015)

Editor-in-Chief
Nagib C. Callaos


Sponsored by
The International Institute of
Informatics and Systemics

www.iiis.org
 

Editorial Advisory Board

Quality Assurance

Editors

Journal's Reviewers
Call for Special Articles
 

Description and Aims

Submission of Articles

Areas and Subareas

Information to Contributors

Editorial Peer Review Methodology

Integrating Reviewing Processes


Utilization of Artificial Intelligence by Students in Interdisciplinary Field of Biomedical Engineering
Shigehiro Hashimoto
(pages: 1-5)

Transdisciplinary Applications of Data Visualization and Data Mining Techniques as Represented for Human Diseases
Richard S. Segall
(pages: 6-15)

Beyond Status Quo: Why is Transdisciplinary Communication Instrumental in Innovation?
James Lipuma, Cristo Leon
(pages: 16-20)

How We Can Locate Validatable Foundations of Life Themes
Jeremy Horne
(pages: 21-32)

Bringing Discipline into Transdisciplinary Communications -The ISO 56000 Family of Innovation Standards-
Rick Fernandez, William Swart
(pages: 33-39)

To AI Is Human: How AI Tools with Their Imperfections Enhance Learning
Martin Cwiakala
(pages: 40-46)

Knowledge, Learning and Transdisciplinary Communication in the Evolution of the Contemporary World
Rita Micarelli, Giorgio Pizziolo
(pages: 47-52)

Human Complexity vs. Machine Linearity: Tug-of-War Between Two Realities Coexisting in Precarious Balance
Paolo Barile, Clara Bassano, Paolo Piciocchi
(pages: 53-62)

A Cybernetic Metric Approach to Course Preparation
Russell Jay Hendel
(pages: 63-70)

The Impact of Artificial Intelligence on Education
John Jenq
(pages: 71-76)

Bridging the Gap: Harnessing the Power of Machine Learning and Big Data for Media Research
Li-jing Arthur Chang
(pages: 77-84)

Image Processing, Computer Vision, Data Visualization, and Data Mining for Transdisciplinary Visual Communication: What Are the Differences and Which Should or Could You Use?
Richard S. Segall
(pages: 85-92)

Identification – The Essence of Education
Jeremy Horne
(pages: 93-99)

The Greek-Roman Theatre in the Mediterranean Area
Maria Rosaria D’acierno Canonici Cammino
(pages: 100-108)

Examination of AI and Conventional Teaching Approaches in Cultivating Critical Thinking Skills in High School Students
Luis Castillo
(pages: 109-112)

Thoughts, Labyrinths, and Torii
Maurício Vieira Kritz
(pages: 113-119)

Can Two Human Intelligences (HIs or Noes) and Two Artificial Intelligences (AIs) Get Involved in Interlinguistic Communication? – A Transdisciplinary Quest
Ekaterini Nikolarea
(pages: 120-128)


 

Abstracts

 


ABSTRACT


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

Toru Yazawa


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.

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