ELECTRO-PHOTONIC IMAGING FOR DETECTING AN INTERVENTION (MEDITATION)

ELECTRO-PHOTONIC IMAGING FOR DETECTING AN INTERVENTION (MEDITATION)

Shiva Kumar K1*., Srinivasan T.M2., Guru Deo3., VenkataGirikumar PandNagendra H.R5
1,3,4Department of Bioenergy, SVYASA
2SVYASA Yoga University, Bengaluru
5SVYASA Yoga University
ARTICLE INFO ABSTRACT

Background: Electrophotonic Imaging (EPI) also known as Gas Discharge Visualization (GDV) is one
of the instrument to capture the internal activities based on the stimulation of photon and electron
emissions from the surface of the object.

Meditation is a family of complex emotional and attentional regulatory training mechanism and it
involves uninterrupted monitoring to capture subtle internal processes. Several instruments are used to
understand the impact of meditation by monitoring the brain waves online or by understanding the
activities in the Default Mode Network. The objective of this study is to use the EPI data to establish a
frame work for intervention recognition by training a neural network by capturing the subtler aspects
of meditation.

Methods: A single group pre-post intervention study was carried out on 51 adults (32 males and 19
females) at Pyramid Valley International, Bengaluru, India. Anapanasati a focused attention
meditation was given for 5 days. EPI data was captured before and after the intervention. The data was
analyzed using IBM SPSS Neural network software.

Results: Meditation was found to have a significant impact on EPI parameters. Neural network was
able to classify pre and post meditative population using EPI data with an accuracy ranging from 84%
to 100%. The receiver operating characteristics (ROC) was captured for each of the classification and
the area under the curve was close to unity.

Conclusion: Electrophonic Imaging combined with neural network works as a good framework for
intervention recognition.

Copyright © 2016 Shiva Kumar K et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use,
distribution, and reproduction in any medium, provided the original work is properly cited.

INTRODUCTION

Electrophotonic Imaging (EPI) is based on the stimulation of
photon and electron emissions from the surface of the object.
The stimulation is provided by transmitting short electrical 10-
microsecond pulses. The emitted particles accelerate in the
electromagnetic field, generating electronic avalanches on the
surface of the dielectric (glass) plate. The discharge causes
glow from the excitement of molecules in the surrounding gas,
and this glow is what is being measured by the EPI instrument.
Voltage pulses stimulate optoelectronic emission, while
intensifying this emission in the gas discharge, amplified by
the electric field created. (Korotkov, 2011)
The resultant Electrophotonic Image represents a spatially
distributed glow areas having varying brightness
characteristics. Computer analysis of it reveals general, local
and sector based details. (Alexandrova, Fedoseev, &
Korotkov, 2004)
The parameters that Gas Discharge Visualization (GDV)
provides are indicative of psycho-emotional and physiological
states. It provides information about the stress and normal
behavior of organs and organ system (Deshpande, Madappa, &
Korotkov, 2013)
The coronal discharge around a human fingertip using an EPI
instrument were used to study the effect of textiles on the
human body (Ciesielska, 2007).
Psycho-emotional condition is defined by our feelings and
thoughts. One of the main questions is what is contained in the
EPI data physical or psychical component. The researchers
showed that it is the mental state the quality of psychic energy
of man. (Anufrieva, Anufriev, Starchenko, & Timofeev, n.d.).
EPI technique has been used to monitor the patients by
comparing their normal Electrophotonic emissions before and
after surgeries (Kostyuk, Cole, Meghanathan, Isokpehi, &
Cohly, 2011).
EPI based analysis on degree of arterial hypertension
concluded that EPI could be used to screen patients of
hypertension with different levels of severity (Aleksandrova,
2009)

Key words:
Electrophotonic Imaging, Artificial
Neural Network, Anapanasati
Meditation, Multi-layer Perceptron

Article History:
International Journal of Current Medical And Pharmaceutical Research
Sympathetic and Parasympathetic activities can be extracted
from the EPI data. The quantitative difference between the two
systems is given out as a parameter called the Activation
Coefficient (AC) by the EPI software, it also gives Integral
Entropy (IE) which is a measure of deviation from functional
physiological state and psycho emotional bal
Kostyuk, Isokpehi, & Rajnarayanan, 2009)
Meditation is a unique state in which deep rest and increased
internal attention exist simultaneously. Meditation is a state in
which agroup of complex emotional and attentional regulator
training mechanisms coexist for the emotional balance and
overall well-being(Lutz, Slagter, Dunne, & Davidson, 2008)
There are two styles of meditation, one is, focused attention
meditation, entails the voluntary focusing of attention on a
chosen object. The other style, open monitoring meditation,
involves nonreactive monitoring of the content of experience
from moment to moment.(Lutz et al., 2008).
A cross sectional study on long term and short term
anapanasati meditators showed health related improvements in
EPI parameters.(Deo G, Kumar IR, Srinivasan TM, 2016)
One of the studies examined the dissociable neural effects of
anapanasati focused-attention meditation on blood oxygen
level dependent signals during cognitive performance with
continuous performance test and emotion processing task
et al., 2012).
The EPI technique may be a valuable clinical tool to assess
rapid responses in patients to examine the effectiveness of
energy medicine modalities and practices such as qigong.
(Rubik & Brooks, 2005)
Among the various meditation practices, the most fundamental
and widely studied form is concentrative or focused
meditation (FAM). FAM practitioners focus their entire
attention upon an object or a bodily sensation and, whenever
they are distracted by external stimuli or inner thoughts, they
bring their attention back to that object or sensation. The goal
is to achieve a clear (vivid) and unwavering (calm and stable)
state free from distraction.(Lee et al., 2012)
The meditation effects are subtle and any online monitoring
could have an effect on the overall outcome especially for
anapanasati intervention. An offline technique like EPI could
be appropriate.
The EPI data has a large number of parameters. They are
multidimensional and non-linear, which calls for a pattern
based approach. Artificial neural networks have been used in
the literature for bio-medical applications.
An artificial neural network (ANN) consists of a series of
interconnecting parallel nonlinear elements with limited
number of inputs and outputs(Wd Hong et al
Artificial Neural Network analysis is more successful than the
conventional statistical techniques in predicting clinical
outcomes when the relationship between variables that
determine the prognosis is complex, multidimensional and
non-linear (Wan-dong Hong, Ji, Wang, Chen, & Zhu, 2
The research on early prediction of diabetes using features of
EPI Images also concluded that data can be used to train neural
networks for classification of diseases for diagnosis.
2013).
Current Medical And Pharmaceutical Research, Vol. 2, Issue, x, pp.xxx
215
Sympathetic and Parasympathetic activities can be extracted
tive difference between the two
systems is given out as a parameter called the Activation
Coefficient (AC) by the EPI software, it also gives Integral
Entropy (IE) which is a measure of deviation from functional
physiological state and psycho emotional balance.(Cohly,
Meditation is a unique state in which deep rest and increased
Meditation is a state in
which agroup of complex emotional and attentional regulatory
training mechanisms coexist for the emotional balance and
(Lutz, Slagter, Dunne, & Davidson, 2008).
here are two styles of meditation, one is, focused attention
meditation, entails the voluntary focusing of attention on a
chosen object. The other style, open monitoring meditation,
involves nonreactive monitoring of the content of experience
A cross sectional study on long term and short term
anapanasati meditators showed health related improvements in
(Deo G, Kumar IR, Srinivasan TM, 2016)
One of the studies examined the dissociable neural effects of
attention meditation on blood oxygen
ignals during cognitive performance with
continuous performance test and emotion processing task(Lee
The EPI technique may be a valuable clinical tool to assess
rapid responses in patients to examine the effectiveness of
ractices such as qigong.
Among the various meditation practices, the most fundamental
and widely studied form is concentrative or focused-attention
FAM). FAM practitioners focus their entire
attention upon an object or a bodily sensation and, whenever
they are distracted by external stimuli or inner thoughts, they
bring their attention back to that object or sensation. The goal
vivid) and unwavering (calm and stable)
The meditation effects are subtle and any online monitoring
could have an effect on the overall outcome especially for
anapanasati intervention. An offline technique like EPI could
The EPI data has a large number of parameters. They are
linear, which calls for a pattern based approach.

Artificial neural networks have been used in
neural network (ANN) consists of a series of
interconnecting parallel nonlinear elements with limited
et al., 2013).
Artificial Neural Network analysis is more successful than the
conventional statistical techniques in predicting clinical
outcomes when the relationship between variables that
s complex, multidimensional and
dong Hong, Ji, Wang, Chen, & Zhu, 2011)
The research on early prediction of diabetes using features of
EPI Images also concluded that data can be used to train neural
networks for classification of diseases for diagnosis.(Priya,
There have been very few studies in capturing subtle effects in
an automated environment. This work uses the combination of
EPI data and artificial neural network for recognizing the
intervention (anapanasati meditation) and works as a
work for intervention recognition.

MATERIAL AND METHODS

Design: This is a single group pre
the Pyramid Valley Meditation center in Bangalore. Consent to
participate in the study was obtained in writing and authorized
tools were used for data collection and analysis of results. This
study was approved by the institutional ethical committee.
Samples: This study was carried out by voluntary recruitment
of 51 subjects consisting of 32 males and 19 females with a
written consent attending a Karnataka Dhyana Mahachakra
at Pyramid valley International Bengaluru, India. The pre
intervention records were termed as non
intervention records as Meditators.

Tool: An Electro Photonic Imaging
capture the data corresponding to 41 acupuncture points with a
total of 82 parameters for both handsand an additional
parameter corresponding to the overall energy called
Activation coefficient. Fig1 has the Electrophotonic Images of
the first 3 fingers from the thumb.
each of the fingers is divided into sectors as shown
number corresponding to the photo electronic emissions from
each sector is computed and given in the form of a spread
sheet by the EPI software.

Full text: 2016 Meditation Shiva Kumar

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