Energy Work
Energy Work includes: Healing Touch, Jin Shin Jyutsu, Polarity Therapy, Reiki and Therapeutic Touch. These modalities can be found elsewhere on this site.
Energy interventions like Healing Touch, Therapeutic Touch, Reiki, Touch for Health, Qi Gong and others are becoming more widely used in combination with
conventional medical treatment modalities. However that are some in the community that expect that the treatments provided by a caring individual produce mostly
psychological comfort with no real clinical intervention. This perception is not correct and it implies there are two basic concepts that are necessary to convey to clients, medical professionals and the community are:
What is an energy treatment, and how does it work?
A reasonable explanation that frequently appears might be: An energy treatment is a conscious, intentional process of directing energy through the hands of the practitioner to the client to facilitate the
healing process. However, this type of explanation is not totally satisfying or convincing even though it is reasonably accurate. A better physical explanation might eliminate some of the
misunderstanding and criticism of voodoo medicine.
A model for a scientific basis of the physiological changes developed by a hands on energy treatment can be extracted from acupuncture research. In acupuncture, healing is stimulated by the insertion
of fine needles at special points on meridians that are usually activated with a tiny current. This current stimulates the flow of Qi or pulses of electrical energy that travel along the meridians and
neurological pathways to the cells. Pomeranz1 showed that this current stimulates the release of endorphins, and the secretion of hormones, serotonin and other chemicals at the cellular level. This
chemical change produces effects like relaxation and reduction of pain.
The effects of Acupuncture are well established. An NIH panel recently reviewed over 200 research papers and concluded that acupuncture helps relieve post-operative nausea and vomiting,
post-operative dental pain, and nausea and vomiting following chemo-therapy2. In addition, the panel concluded that acupuncture was a suitable part of the treatment plan for drug and alcohol addiction, stroke rehabilitation, headache, menstrual cramps, tennis elbow, general muscle pain, osteoarthritis, low back pain, carpal tunnel syndrome, and asthma.
It is reasonable to expect these results should also apply to hands on energy treatments. When a practitioner centers to do a treatment, there is a mind-body connection where the mental processes stimulate the body's bioelectrical field. The bioelectrical flow corresponds to pulses of electrical charges that produce chemical changes in the practitioner's body, but these pulses also create a magnetic
field. Maxwell's Law3, a well documented effect in physics, states that the flow of electrical charges creates both an electrical field and a magnetic field, and Maxwell's equations show how these effects are related. Thus the human energy system is a bioelectromagnetic field4. The flow felt between a person's two hands is a biomagnetic field flow. The aura is a subtle biomagnetic field.
During a treatment the practitioner's biomagnetic field interacts with the client's biomagnetic field and changes occur in the client's electrical field. This produces a change in the client's chemical balance at the cellular level, chemicals are released and physiological changes result. The cell's structure and function are changed. This process can be summarized in the following diagram:
Drugs and food produce changes at the cellular level by directly changing the chemical balance. An emotional trauma impacts the body through bioelectrical changes that are stimulated by the thought
process.
Energy treatments are not magic. The effect of the modality is similar to acupuncture. It can be thought of as a bioelectromagnetic massage to stimulate bioelectromagnetic and physiological changes
in the client at the cellular level to promote healing. Educational programs teach the practitioner how to prepare and manage their own bioelectromagnetic field to create changes in the bioelectromagnetic field of the client.
????????????????
1 Pomeranz, B., Scientific Basis of Acupuncture. Stux and Pomeranz eds., Acupuncture: Textbook and Atlas, Springer Verlag, Berlin, 1986
2 Acupuncture: Chinese Folk Medicine or Legitimate Medical Treatment," Tufts University Health & Nutrition eLetter, New York, V 16.4, June 1998
3 Paul, R. C., K. W. Whites and S. A Nasar, Introduction to Electromagnet Fields, WCB/McGraw-Hill, 3rd ed., Cambridge Massachusetts, 1998
4 Tiller, W. A., Science and Human Transformation, Subtle Energies, Intentionality and Consciousness, Pavior Publishing, Walnut Creek California, 1997.
???????????????-
Source: Donald Stouffer, PhD, CHTP, Professor of Aerospace Engineering, University of Cincinnati OH USA. Copyright © 1997-2002; reprinted with permission. First appeared as"Why Does
Healing Touch Work?" in the Healing Touch eLetter, V9 No 1, Copyright © 1997-2002, Colorado Center for Healing Touch, Lakewood CO USA, (303)989-0581.
An Integrative Approach
One integrative approach toward balance, self-healing and an improved quality of life combines therapies that aid in the discovery and balance of the total self. Whether a new or experienced seeker of
Complementary Healthcare, you can have a personalized treatment approach which addresses issues in the spiritual, emotional and physical realms.
In a safe, caring and non-judgmental environment, each person can be nurtured with a strategy that might include personal consultation, classroom teaching, and biofield (hands-on) therapies. These
specific mind/body customized interventions relate to energetics, meditation, nutritional counseling, psychotherapy, intuitive assessment, spiritual guidance and movement therapies.
In-Sights uses this approach, and each person is encouraged to appreciate the interconnectedness of body, mind and spirit. As the inward process evolves, individuals can enthusiastically create a gentle
loving awareness of the self. The In-Sights approach creates a unique and empowering bridge to higher reality.
Source: Darleen Miller, BS, CRM and Loni Tesch, MSW, Centennial CO. 

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Parameters 1, %1, 10, %10, CW and %CW indicate that
- there is a similar negative effect of mobile phones on the human BEM in groups with mobile phone and in placebo group;
- results in control group are similar to those in group with the shield of Minnie Hein, indicating that shield influences the human BEM in a way that neglects the effect of mobile phones;
- the shield of Milan Mlad*enovic has positive effect - not only that it neutralises the effect of cellular phones, but it also strengthens the human BEM;
- parameters 3R-6 and 1R-4 imply similar findings: placebo protection has no effect, cellular phones negatively affect the human BEM, both shields are effective, and besides the shield of M. Mlad*enovic has an amplifying effect.
2.3 Statistical analysis
To estimate whether the parameters' changes were significant, we used Student's t-test. The significance level a was chosen to be 0,05. The majority of these tests have indicated insignificant changes. The probable cause is in too big standard deviations of parameters' values. This problem could be alleviated with greater number of people in
each group. Significance has mostly manifested inside group of people, wearing cellular phones, equipped with the shield of M. Mlad*enovic. The BEM of subjects, belonging to this group, have been significantly increased.
T-tests between different classes were also performed in order to find out, whether the differences between values among all possible pairs of classes were great enough to consider results significant enough in comparison to their deviation. Most of these tests also showed insignificant changes between 5*4/2=10 pairs of groups. But in spite of that, results of parameters 10, 1R-4, 3R-6 and CW indicate that:
1. the control group and group with the shield of Minnie Hein are similar - there are no significant changes between them;
2. the placebo group had worse BEM than the control group - indicated by significant changes of parameter 1R-4 (and partially by almost significant parameters 3R-6, 10 and CW);
3. the control group and group with the shield of Milan Mlad*enovic are similar - there are no significant changes between them;
4. the group with cellular phone had worse BEM than the control group - indicated by significant change in parameter 3R-6 (and partially by almost significant 1R-4 and 10);
5. the placebo group had worse BEM than group with the shield of Minnie Hein - partially indicated by almost significant change in parameters 10 and 1R-4 (and slightly by indicated significance of CW);
6. the group with the shield of Minnie Hein is similar to the group with the shield of Milan Mlad*enovic - there are no significant changes between them;
7. the group with telephone had worse BEM than group with the shield of Minnie Hein - indicated by almost significant change in parameter 3R-6 (this test showed the least significance);
8. the placebo group had worse BEM than the group with the shield of Milan Mlad*enovic - indicated by significant changes in 1R-4, 3R-6 and CW (and partially by almost significant change in 10)
9. the group with telephone is similar to the placebo group - there are no significant changes between them;
10. the group with telephone had worse BEM than group wearing the shield of Milan Mlad*enovic - indicated by significant change in 3R-6 (and partially by almost significant changes in 1R-4 and CW, and slightly by indicated significance of 10)
2.4 Analysis of merged groups
To alleviate an unwanted effect of too great deviation in calculated results, we decided to merge groups/classes that were related. Herewith we reduced the number of classes to three and gained greater number of subjects in
new group composed of these previous groups number of people W - people without telephone control group
(this group remained the same)
17
T - people with telephone
people with telephone
+ placebo group
35
 S - people with shielded telephone
people with shield of Minnie Hein
+ people with shield of M. Mlad*enovi?
 32
two classes. Table 2.2 shows the structure of new classes and the number of examples in each class.
We processed the data in three groups in the same way as before. Although Student's t-tests inside particular class showed no significant changes, there were some
significant differences, showed by t-tests between 3 pairs of classes. Significant changes of parameters 10, 1R-4, 3R-6 and CW indicate that:
- groups S and W are similar, because there are no significant differences;
- group T had worse BEM then W - indicated by significant change in parameters 1R-4 and 3R-6 (and partially by almost significant 10 and CW);
- group T had worse BEM than group S - implied by significant change in parameters 1R-4 and 3R-6 (and partially by almost significant 10 and CW).
3. Relation between energetic diagnoses and GDV images
We recorded coronas of all ten fingertips of 110 persons for whose the extrasense healer provided the energetic diagnosis. We used machine learning to interpret the GDV coronas in order to verify three hypothesis: (a) the GDV images contain useful information about the patient, (b) the map of organs on coronas of 10 fingers does make sense, and (c) the extrasense healer is able to see by himself (with his natural senses) the energetic disorders in the human body. The results support all three hypotheses.
3.1 The measurements and preparation of data
The first stage of the research involved recording coronas of all ten fingertips of patients. Parameters calculated from coronas (GDV images), were then used as attributes for machine learning algorithm. We recorded 150 patients, but due to technical problems only 110 cases were useful. Recording was made with Crown TV camera. It's a digital camera connected to a computer. It captures corona image directly into a bitmap image, which is very suitable for image analysis.
We calculated a set of parameters from each corona image. Each training instance was presented with a set of 634 attributes (parameters), which is too many attributes for only 110 cases. Evidently this large set of attributes had to be reduced in order to expect some positive result from machine learning. Namely with altogether 634 attributes and only 110 cases there is a high probability that certain irrelevant attributes will seem to be very relevant just by chance. Attribute reduction is explained later in this article for each experiment separately.
On the other hand, we also needed the classification class for each case (patient). Here we engaged an extrasense healer. We recorded his observations on audio tapes.
The observations were made for all organs of the whole body. The diagnosis for one patient contains the description of the state for 65 organs/parts of organs/glands (e.g. small brain, stomach, left and right kidney, thyroid gland, etc.), 8 parts of the body (e.g. arms, head, neck, legs etc.), 8 physical/psychical functions (e.g. respiration, digestion, concentration, sleeping etc.), and 17 possible diagnoses in terms of classical medicine (e.g. rheumatism, cold, headache, hameorrhoids etc.). For each organ and part of the
body the state can be either OK, energetic blockage, strong energetic blockage, incorrect function, no function or damaged. This gives 7 classes, which is too much for our learning conditions (634 attributes, 110 cases). Therefore we decided to distingwish only between 2 classes. The first class is 'no blockage' (OK) but all other classes were joined into second class, called 'blockage'.
3.2 Machine learning of diagnoses
We performed 5 experiments:
(A) The first experiment: 239 attributes, 110 cases
We used all 110 cases. We had to somehow reduce the number of attributes. Here we excluded a large subset of attributes that represent a part of corona sector area and seem pretty irrelevant at first sight. 239 attributes remained. Further on we decided to run our experiment ten times on 10 diagnoses (learning problems) that were best according to their class distributon. We used C5.0 learning algorithm for building decision trees, a descendant of C4.5 (Quinlan, 1993). We used 10-fold cross validation and calculated the average accuracy of ten decision trees. The results showed about 10% improvement of accuracy compared to the default classifier (that classifies all instances into the majority class) for five diagnoses: duodenum, throat, blood circulation, neck, and cervical spine.
(B) The second experiment: 79 attributes, 110 cases
Here we tried to additionaly reduce the number of attributes. Knowing (according to fingertip corona map) that each diagnosis is directly connected to some sector of fingertip corona, we used only attributes that measure areas of corona sectors (79 attributes). All other experiment conditions remained unchanged. The result was rater similar to the result of the first experiment.
(C) The third experiment: 79 attributes, 71 cases
Here we focused on the quality of the data. We excluded patients with generally weak coronas. This cases show general lack of energy, but do not provide enough
information about specific organs (Korotkov, 1998). Only 71 cases remained useful. All other experiment conditions remain unchanged from the second experiment. The
result showed larger improvemnts of accuracy from previous experiments, especialy for two diagnoses: taste (20% improvement) and duodenum(15% improvement).
(D) The fourth and fifth experiment: 2 to 10 atributes, 71 cases
To additionaly reduce the set of attributes, we used only attributes that are relevant for some diagnose according to medical doctor's opinion. The set of parameters greatly decreased (from 2 to 5 attributes per diagnosis in the fourth experiment and up to 10 attributes in the fifth experiment). Other conditions remained unchanged. The results didn't show any improvement in accuracy.
3.3 Estimating the quality of attributes
The analysis of the trees that were generated in the third experiment (which gave best results) shows interesting match with medical doctor's opinion. Namely, for this stage of the study we used a map of relations between diagnoses and attributes, which was supplied by an independent medical doctor. Also an interesting phenomena could be noticed. That is, the best classification results were achieved where the root (best) attribute matches with doctors selection. For example: All 10 trees generated in 10-fold cross validation for diagnose duodenum, had a 'duodenum attribute' in root. There is less than 2.6% of chance that this is just a coincidence. Also all 10 trees generated for
diagnose taste contain 'lymph attribute' in root, which is also relevant according to doctor's opinion.
Further on we estimated the quality of attributes. We used the Gain Ratio estimate (Quinlan, 1993). All parameters for estimation were the same as in the third experiment (71 cases, 79 attributes). We decided to compare the attributes, proposed by the medical doctor, with 10 best estimated attributes. There was a significant match with doctor's opinion in 6 out of 10 learning problems. Here are some examples. A set of 10 best estimated attributes for diagnose duodenum contains 'duodenum attribute' in the first
place and 'lymph attribute' in the third place. Both are relevant to duodenum according to doctor's opinion. When estimating parameters for taste, all 3 parameters from doctor's selection are among 10 best estimated attributes. The probability of coincidence is here less than 6%. The diagnosis 'heart' had 'heart attribute' in the first place. The probability of coincidence is less than 5.1%. The diagnosis 'lungs' diagnose had 'throat attribute' as best estimated. Little brain and blood circulation diagnoses had 2 attributes from doctor's selecton among 10 best estimated.
And finally, we performed the estimation on the whole set of 634 attributes. The results here confirmed results from previous estimates. We describe some exmples.
Estimation for 'duodenum' diagnosis gave 2 relevant attributes among 10 best estimated. Coincidence probability is less than 0.4%. Estimation for 'taste' diagnosis gave also 2 relevant attributes among 10 best estimated. Coincidence probability is less than 0.6%. 'Heart' diagnosis gave 'heart attribute' as best estimated. Coincidence probability is less than 0.7%. Finally, 'little brain', 'lungs', and 'liver' diagnoses had one relevant attribute among 10 best estimated.
3.4 Conclusions and further work
Machine learning experiments show that our numeric parameters, calculated on corona images, aren't sufficient for exact diagnosis. Two basic reasons seems to be
inaccurate recording of corona images and too little training instances (patients). In spite of all, it also turned out that corona images contain useful information for
diagnostics. Namely, for all diagnoses we managed to increase the classification accuracy for at least 10% according to default classifier (that classifies all instances into the majority class).
Attribute estimation and tree analysis show that we had better success with machine learning where the set of most informative attributes matches with medical doctor's selection of attributes for that specific diagnosis.
We can conclude that the corona images contain useful information for diagnostics, but there is a problem with 'extracting' this infomation. Namely, the whole process of data capturing is very sensitive to noise. And after all, it is very difficult to select a small set of informative attributes from a large set of noisy parameters, especially when you have such a small training set.
4. Recording coronas of grapevine berries
The aim of the study was to determine, whether Kirlian camera can record any useful information by recording coronas of berries. We used nine sorts of grapevines, two
reedvines for each sort (healthy and infected by different viruses), obtained from plants of Biotechnical Faculty in Ljubljana. We recorded 20 berries for each reedvine. We used only 14 basic numeric attributes (see Section 3, the absolute area of corona was excluded due to different sizes of berries of different sorts).
We used two machine learning algorithms in order to distinguish different sorts and infected from noninfected reedvines from numerical description of coronas of their berries.
The naive Bayesian classifier assumes the conditional independence of attributes given the class and calculates for each new instance the probability of each class
(Kononenko, 1993). Assistant-R builds decision trees and uses a non-myopic algorithm ReliefF for the estimation of the quality of attributes (Kononenko et al., 1997). We measured the classification accuracy and the information score (Kononenko and Bratko, 1991). The latter measure eliminates the influence of prior probabilities and appropriately treats probabilistic answers of the classifier.
We tried to solve various problems:
(a) distinguishing infected 'Pinela' from noninfected 'Pinela', 2 classes, 30 examples in each class;
(b) distinguishing 'Malvazija' without symptoms and 'Malvazija' with symptoms of phytoplasma; 2 classes, 20 examples in each class;
(c) distinguishing all nine sorts of grapevines, 40 examples in each class;
(d) Volovnik'+'Zweigeld' (not infected with GLRaV viruses) and 'Sladkocrn'+ 'Klarnica' (infected with GLRaV viruses); 2 classes, 80 examples in each class;
(e) distinguishing two cultivars: 'Volovnik' and 'Zweigeld', 2 classes, 40 examples in each class.
For each problem we randomly split the set of all examples in 70% for training and 30% for testing. This process was repeated 10 times and average results and standard deviations for the naïve Bayesian classifier are presented in Table 4.1. Results for Assistant-R are similar.
Table 4.1: Results of the naïve Bayesian classifier in different classification problems for grapevine data.
problem
prior pr. (%)
class. accuracy (%)
inf. score (bit)
nine cultivars
11.1
35.7 ± 3.1
1.09 ± 0.07
 ‘Volovnik’ : ‘Zweigeld’
50
77.5 ± 9.2
0.45 ± 0.15
infected : non-infected ‘Pinela’
50
70.0 ± 11.1
0.30 ± 0.13
infected : non-infected with GLRaV
50
71.0 ± 5.5
0.35 ± 0.06
‘Malvazija’ with : without phytoplasma
 50
88.3 ± 8.0
0.73 ± 0.16
In all tests, the classification accuracy is significantly higher than the prior probability of the classification. For example, in the case of all nine cultivars, the classification
accuracy is 35.7%. Since all nine classes are of the same size, a prior probability for each class is 1/9=11.1%, which is more than three times lower than the classification
accuracy. Because of this, the information score is very high. The classification is quite successful also in the cases of classification of grape berries according to their sanitary status. In these cases, the prior probability is 50% while the classification accuracy ranges between 70% and 88.3% which is indeed unexpectedly high.
5. Drinking water from ordinary and 'energetic' glass K2000
We performed an experiment with drinking water from ordinary glass and so called 'energetic' glass K2000, which is somehow coded with positive information/energy. K2000 was invented by Vili Poznik from Celje, Slovenia. He uses orgon technology (methodology) in order to encode information into glass.
We recorded each of 34 volunteers three times in three days: without drinking water, 15 minutes after drinking water from ordinary glass, and 15 minutes after drinking water from energetic glass K2000. The persons didn't know which glass is ordinary and which is energetic. We used tap water and the water was left 15 minutes in the glass before it was consumed. For each person we recorded coronas of all ten fingertips. We calculated 15 basic parameters for coronas of each finger and we averaged their values over all ten fingers. We used the following parameters:
1.. Absolute area of corona.
2.. Noise, deleted from the picture (depends on the first setting in the program).
3.. Form coefficient.
4.. Fractal dimension.
5, 6.. Brightness coefficient and deviation.
7.. Number of separated fragments in the image.
8, 9.. Average area of fragments and its deviation.
10.. Relative area of corona
11.. Relative coefficient of glow inside the inner oval.
12-15.. Relative coefficient of image glow for 25, 50, 75 nad 100% area (from the whole area)
We calculated average values and standard deviations for each parameter and for each glass: the difference between the value after drinking water from the given glass minus the value before drinking the water (see Table 2). The results indicate that water from K2000 increases the coronas (parameters 1, 8 and 10-15) and decreases the fragmentation (parameter 7), while that from ordinary glass slightly decreases the coronas and, to the lower extend than K2000, decreases the fragmentation.
To evaluate the significance of differences between the glasses we used the paired one-tailed t-test. We calculated the differences and st. deviations between the values of parameters of two glasses. The differences together with t-values and significance levels are given in Table 5.1. With the exception of parameter 7, parameters 1,8 and10-15 show significant differences (significance level greater than 0.99)
Table 5.1: Statistical analysis for drinking water from two glasses
parameter
average
difference
standard deviation (s)
t = r/s * sqrt(n)
significance level
           1
                    856,61
                                           1199,00
                                                              4,17
                                                                            >0,99994
           2
                    284,60
                                            614,84
                                                              2,70
                                                                              0,9931
           3
                     15,02
                                             42,27
                                                              2,07
                                                                             >0,9596
           4
                      0,14
                                              0,48
                                                              1,76
                                                                              0,9216
           5
                      0,61
                                              4,36
                                                              0,82
                                                                              0,5878
           6
                      -0,67
                                              4,34
                                                             -0,90
                                                                              0,6319
           7
                      -1,49
                                              4,74
                                                             -1,83
                                                                              0,9328
           8
                    563,74
                                            944,61
                                                              3,48
                                                                            >0,99933
           9
                     13,08
                                             44,94
                                                              1,70
                                                                              0,9109
          10
                      0,21
                                              0,35
                                                              3,45
                                                                            >0,99933
          11
                      0,02
                                              0,03
                                                              3,49
                                                                            >0,99933
          12
                      0,08
                                              0,14
                                                              3,46
                                                                            >0,99933
          13
                      0,09
                                              0,15
                                                              3,55
                                                                            >0,99953
          14
                      0,07
                                              0,14
                                                              3,11
                                                                            >0,99806
          15
                      0,03
                                              0,05
                                                              2,82
                                                                             >0,9949

For machine learning analysis we used C4.5 system for building decision trees (Quinlan, 1993). We wanted to distinguish ordinary glass from K2000. We had 68 examples and we performed two experiments: using all 15 attributes and using only attributes 1,7,8, and 10. The average classification accuracy, obtained by 10-fold cross validation, was 76.2%, when all atrtibutes were available, and 81.0%, with four selected attributes. In the latter case, most of the times the decision tree contained only attribute 8 (average area of fragments).
Why Does Healing Touch Work?
by Donald Stouffer, PhD, CHTP
Professor of Aerospace Engineering, University of Cincinnati
The two basic concepts that are necessary to convey to clients, medical professionals and the community are: What is Healing Touch and how does it work A reasonable explanation might be: Healing Touch is a conscious, intentional process of directing energy through the hands of the practitioner to the client to
facilitate the healing process. However, this type of explanation is not totally satisfying or convincing even though it is reasonably accurate. A better physical
explanation might eliminate some of the misunderstanding and criticism of voodoo medicine.
A model for a scientific basis of the physiological changes developed by a Healing Touch treatment can be extracted from acupuncture research. In
acupuncture, healing is stimulated by the insertion of fine needles at special points on meridians that are usually activated with a tiny current. This current
stimulates the flow of Qi or pulses of electrical energy that travel along the meridians and neurological pathways to the cells. Pomeranz (1) showed that this
current stimulates the release of endorphins, and the secretion of hormones, serotonin and other chemicals at the cellular level. This chemical change produces
effects like relaxation and reduction of pain.
The effects of acupuncture are well established. A NIH panel recently reviewed over 200 research papers and concluded that acupuncture helps relieve
post-operative nausea and vomiting, post-operative dental pain, and nausea and vomiting following chemo-therapy (2). In addition, the panel concluded that
acupuncture was a suitable part of the treatment plan for drug and alcohol addiction, stroke rehabilitation, headache, menstrual cramps, tennis elbow, general
muscle pain, osteoarthritis, low back pain, carpal tunnel syndrome, and asthma.
It is reasonable to expect these results should also apply to Healing Touch. When a practitioner "centers" to do a Healing Touch treatment, there is a
mind-body connection where the mental processes stimulate the body's bioelectrical field. The bioelectrical flow corresponds to pulses of electrical charges that
produce chemical changes in the practitioner's body, but these pulses also create a magnetic field. Maxwell's Law (3), a well documented effect in physics,
states that the flow of electrical charges creates both an electrical field and a magnetic field, and Maxwell's equations show how these effects are related. Thus
the human energy system is a bioelectromagnetic field (4). The flow felt between a person's two hands is a biomagnetic field flow. The aura is a subtle
biomagnetic field.
During a treatment the practitioner's biomagnetic field interacts with the client's biomagnetic field and changes occur in the client's electrical field. This
produces a change in the client's chemical balance at the cellular level, chemicals are released and physiological changes result. The cell's structure and function
are changed. This process can be summarized in the following diagram:
Practitioners
Magnetic
Field
<->
Clients
Magnetic
Field
 <->
Electrical
Field
<->
 Chemical
Balance
<->
 Cell
Structure
& Function
Drugs and food produce changes at the cellular level by directly changing the chemical balance. An emotional trauma impacts the body through bioelectrical
changes that are stimulated by the thought process.
Healing Touch is not magic. The effect of the modality is similar to acupuncture. It can be thought of as a bioelectromagnetic massage to stimulate
bioelectromagnetic and physiological changes in the client at the cellular level to promote healing. The Healing Touch program teaches how to prepare and
manage the practitioner's bioelectromagnetic field to create change in the bioelectromagnetic field of the client.
(1) Pomeranz, B., Scientific Basis of Acupuncture. Stux and Pomeranz eds., Acupuncture: Textbook and Atlas, Springer Verlag, Berlin, 1986
(2) Acupuncture: Chinese Folk Medicine or Legitimate Medical Treatment, Tufts University Health & Nutrition Letter, New York, V 16.4, June 1998
(3) Paul, R. C., K. W. Whites and S. A Nasar, Introduction to Electromagnet Fields, WCB/McGraw-Hill, 3rd ed., Cambridge Massachusetts, 1998
(4) Tiller, W. A., Science and Human Transformation, Subtle Energies, Intentionality and Consciousness, Pavior Publishing, Walnut Creek California, 1997.
Visualization of Human Bioelectromagnetic Field
English abstract
With the developement of technology it has become possible to scientificaly study some aspects of the aura
(bioelectromagnetic field) phenomenon. We are using Kirlian effect also known as Gas Discharge Visualization (GDV)
technique to gather auras of person's fingers. Since we are developing an expert system for diagnosis from GDV
images using the machine learning techniques which proved to be successful also in classical medicine, we have to
obtain numerical information from aquisited images. This article describes the computer vision methods applied to
GDV images in order to get the aura of the whole body, which is the first step towards mentioned expert system.
Concepts of Homeostasis
E. F. Block IV
Abstract
The usual Concepts of Homeostasis are focused upon the mechanisms of circulation, respiration, digestion, excretion, mineral/water balance and bioenergetics. The nervous system and the hormone system are described as regulating, via feedback control, the various tissues in response to changes in the internal milieu of the body. Homeostasis is described as a function of the individual in response to its environment and role in the ecosystem. A great deal is said about physiology (cellular, tissue and organ), some about seasonal variation (adaptations to climate changes and reproduction) and some about the nervous/hormonal control systems. However, nothing is said about the underlying source for the generation of homeostasis in the body. It is my thesis that the source of the generation of homeostasis is the dynamic relationship between the result of evolutionary exitent DNA expression according to changes in time of the energetic  relationships of all the matter which comprises the physical body matrix and the response of the physical body matrix as an evolved system by/to fluctuations in the Solar System Interplanetary Electromagnetic Field Matrix (SSIEFM). It is disruptions in the ability of the physical body matrix to respond to changes in the SSIEFM which Bioelectromagnetic Medicine needs to address by theraputic means. Treat disruptions in the physical body electromagnetic matrix and thebody matrix realigns itself with the SSIEFM to display optimum dynamic health.
Thus disruptions in physiology as disease is the result of disruptions in the physical body electromagnetic field matrix within the overall SSIEFM. Which mean that altered physiology is the symptom and not the cause of disease.
Bioelectromagnetic Medicine treats the disruptions of the dynamically fluctuating electromagnetic field components of the human primate body to bring them into alignment with the potential of homeostatic mechanisms to maintain optimum energy flow through its aggregation of matter within the environment the person inhabits.
Introduction
The discussion will be in several parts. Background information leading to an understanding of the means for the manipluation of the bioelectromagnetic fields of humans will be presented first. Then follows a discussion which will elucidate the means by which the human primate animal is thought to maintain homeostasis. Next will be a discussion of the causes of disruptions in bioelectromagnetic field components of the body as the origin of physiological derangements and disease as disruptions in homeostasis. And finally, new ways of thinking about homeostasis in terms of a dynamically fluctuating electromagnetic field organized in space which is able to be manipulated by therapies of
Bioelectromagnetic Medicine.
Discussion
A Physicist is one whom studies the fundamental properties of matter and energy.
A Chemist is one whom studies the behavior of matter as energy dissipates through it. A Biologist is one whom studies matter organized to maintain energy flow through it. These are oversimplifications but nevertheless true. Living systems are aggregations of matter organized to maintain energy flow through the aggregation with a guided purpose. That purpose is to perpetuate and refine the ability to maintain energy flow through the aggregation.
Living systems have evolved the means of maintaining energy flow through their particular aggregation within and as part of the dynamically fluctuating electromagnetic universe. Living systems as aggregations of matter have dynamically fluctuating electromagnetic fields which reflect the organization of the aggregation within the local space/time continuum.
The means which has evolved to perpetuate the particular aggregations and control systems of living organisms is what Biologists refer to as genetics. Gene expression is what enables the organism the maintain energy flow. The genetic system of any organism has evolved to perpetuate the organism as a species and to maintain the organism within the environment it inhabits. Gene expression then is the ultimate origin of homeostasis in living systems.
Organic evolution through the ages has produced an amazing diversity of living organisms. Our interest is in the human primate with all its organ systems working in harmony. The scalar organization of the human body is as follows: quarks, nucleons and leptons, atoms and energy on the physical level; molecules, atomic bonds, moving electrons & protons, photons and energy dissipation on the chemical level; cells, tissues, organs, organ systems, the organism, populations, the ecosystem and energy concentration & flow through the organic systems on the biological level. All living systems have evolved mechanisms to control & deal with the particular requirements of the environment in which they are found in order to acquire, utilize and direct energy flow through the existent system. The control systems for human homeostatic systems are likewise scalar. The physical and chemical scales are those of matter and the fundamental properties of matter. The biological scale with the human animal starts with the cell, the fundamental unit of life. All life comes from preexisting life. However, each cell has in the nucleus its genetic complement which is the result of evolution over many generations throughout the history of the Earth. The human genetic code controls the development of the fertilized zygote to adult organism. This development takes place within the backdrop of the GMF and SSIEFM which influences the developing bioelectromagnetic fields of the embryo as well as the growth of cells into tissues, tissues into organs and organs into the organism as a whole.
Apart from cellular control by genetic expression, the means that the human animal genetic system has developed for overall control of the organism are the nervous and hormonal systems. These two systems control by negative feedback the circulatory, respiratory, digestive, excretory and reproductive systems of the body which are geared to maintaining the internal environment of the body at optimum for the cells living within the organism. All homeostasis is geared to this end - maintaining the optimum internal environment for cellular life within the body!
The hypothalamus is the neurosecretory organ of the brain that determines the set points for the range of internal environmental values for blood glucose, tissue oxygen and carbon dioxide, body temperature, blood volume, blood pressure, blood calcium, blood sodium, blood pH, etc. As the human animal is terrestrial, the kidney has long loops of Henle for urine concentration and sodium retention.
The hypothalamus is involved with the coordination of osmoregulation via the pituitary gland for anti-diuretic hormone and nervous stimulation of the adrenal medulla for mineralcorticoids, as an example. The other hormonal glands and the liver also contribute to homeostasis. The point is this: all physiology is geared to maintaining homeostasis. All normal behavior is geared to assisting in homeostasis in some manner and more importantly in reproduction.
What tunes the hypothalamus to changes in the dynamic fluctuations of the GMF and SSIEFM? One hormonal gland is known to be tuned to changes in diurnal cycles, the pineal gland. The Pineal-Hypothalamic-Pituitary Axis (PHPA) determines both the nervous and hormonal coordination of physiological and behavioral events in time for the entire body. It is thought that cellular events are cued by fluctuations in the GMF and SSIEFM via rhythmic changes in geneexpression. It is thought that the organism as an integrated entity is cued by the PHPA.
What then of the overall bioelectromagnetic field of the human body, sometimes termed as the aura. That it exists is not in doubt. How it exists and can be characterized is now an intense area of research. Electroacupuncture seems to be the most efficient means of obtaining reliable data for research efforts.
Electroacupuncture according to Dr. Voll (EAV) is now the standard by which all research efforts are promulgated.
The theories of Chinese Medicine, in relation to the energy flows of the body (acupuncture), and Homeopathy are being substantiated and refomulated in light  of new findings. It is possible to detect derangements in the energetic flows of the body using various techniques of EAV. Bioelectromagnetic Medicine is moving from being a fringe element into being the mainstream in modern medicine.
Please read previous volume issues and references for a detailed account of the theoretical basis of bioresonance therapy.
What is known to cause a disruption in the bioelectromagnetic fields of humans can be catagorized as follows.
1.aberrant fluctuations in the GMF caused by underground water flows, mineral veins and magmatic intrusions known as geopathic stress
2.deleterious mutations in the genetic code which affects the organization in the aggregation of organic matter into a body and the resultant produced
non-resonant bioelectromagnetic field
3.alternating electric current and all the machinery which utilizes A.C. current for power known as EMFs
4.pathogenic microorganisms
5.parasites
6.malnutrition
7.physical trauma
It is much like trying to determine which came first - the chicken or the egg.
However, it has been found that there is always a change in energy relationships before a physiological change can be recognized. The nature of that change varies.
With the Bicom device, it is possible to first detect aberrations and then neutralize them completely within a few seconds. Physiological changes follow within a few minutes to hours in regaining homeostasis. Many of the above cited causes for disruption are chronic stressors. Aleviation of symptoms can be accomplished simply by removing the stressor or the person from the vicinity of the stressor. The
Bicom device is a tool to determine the nature of the chronic stressor as well as a therapeutic means to bring the body back to homeostasis. Obviously, the body which is not able to live and function in our increasingly stressful environment will succomb to some chronic ailment according to the inherent weaknesses of their genetic code.
Conclusion
Energy flow in the human primate animal is able to be detected, catagorized and manipulated on many levels. Those energy flows which are an expression of the basic homeostatic mechanisms of the coordinated organ systems of the body are ultimately the result of gene expression. Gene expression is responsible for the organization in space of the aggregation of matter which comprises the organic body. This aggregation of matter has an overall bioelectromagnetic field which is in resonance with the Geomagnetic Field (GMF) of the Earth and also with that of the Solar System Interplanetary Electromagnetic Field Matrix (SSIEFM). All of these fields are in dynamic equilibrium with the smaller in resonance with the larger. Disruptions in the capacity for maintaining resonant energy flow through  the matter aggregation of the human body are responsible for the eventual expression of disease in humans. Bioelectromagnetic Medicine has the means to assist the human body to realign itself within the greater GMF and SSIEMF in order to return to dynamic equilibrium. The body has the means to heal itself if it is not overstressed in some manner, this is the essential role of homeostasis. It is the current role of those involved in Bioelectromagnetic Medicine to discover the basic means to provide therapeutic relief and to bring the body into harmony and thus
eliminate the diseased condition.
Reference
Brugemann, H., Bioresonance And Multiresonance Therapy (BRT), 1993, Haug
 International, Brussels
New, forward-looking forms of therapy with ultrafine body signals and environmental signals: Documentation and Practice
Edited by Hans Brugemann with contributions by
1.H. Brugemann, Gauting
2.B. Kohler, Freiburg
3.W. Ludwig, Horb
4.H. W. Mittlehauser, Landstuhl
5.F. A. Popp, Kaiserslautern
6.P. Schumacher, Innsbruck
Part 1
1.Chapter One - Introduction to bioresonance therapy
2.Chapter Two - Contributions to the practical application of bioresonance therapy
3.Chapter Three - The basic principles of multiresonance therapyPart 2
1.Chapter One - New avenues in medicine
2.Chapter Two - The fundamentals of bioresonance therapy
"Fundamental Elements of the Field Components of the Human Aura"
Abstract
The fundamental elements of the adult human aura(Bioelectromagnetic field) may be described as three dynamically vectored
energy flows. The primary energy flow in dynamic equilibrium is vectored in the long axis of the body from head to base of the
spine through 7 nodal points. There are two secondary energy flows also in dynamic equilibrium. One is the bimodal vectored flow
from the 7 nodal points to the left and right sides of the body and the other is the bimodal vectored flow from the 7 nodal points to
the front and back of the body. Depending upon the maturity of the individual, the overall resulting field may be pear-shaped,
ellipsoid or spherical.
INTRODUCTION
The human "Aura" as a phenomenon capable of being described is subject to controversy and superstition. Where is found a
description of the "aura" in any context? Chinese Medicine describes the meridians of Acupuncture but nothing more elemental.
Bioelectromagnetic Medicine is still trying to define itself much less the morphogenic field of the physical body. The only
comprehensive description of the composition of the "aura" comes from the tradition of Yoga.
One of the 8 branches of Yoga is called Raja or Royal Yoga. Raja Yoga is the Yoga of prolonged and intensive meditation upon the
physical body, the mind and the spirit. In this case, it is the physical body in which we are interested. Raja Yoga describes 7
"Chakras" as transformer points for the energies which allow the human body to exist. I will refer to these "Chakras" as nodal
points. The two entwined snakes about the staff of Hermes in Hellenic tradition was taken by the medical profession as its symbol.
The points where the snakes cross each other are nodal points. The snakes themselves are the right-hand and left-hand energy flows
about the central core flow between the nodal points. These are the same as the Ida, Pingala and Sushumna of the Yogic tradition.
As the Yogic tradition predates that of the Hellenic, it is to be assumed that the philosophers of Greece were trained in the teachings
of Yoga and adapted them to their own likeness.
DISCUSSION
As we know from physics, there are two ancillary field/energy flows to any main field/energy flow which are 90 degrees to each
other and perpendicular to the main field/energy flow. The Sushumna of Yoga is the main field/energy nodal flow which is in
dynamic equilibrium in a vectored flow from head to base of the spine and vice versa. One of the two ancillary field/energy flows is
the Ida and Pingala of Yoga, the left-hand(Clockwise) and right-hand(Counter-Clockwise) vectored field/energy flows in dynamic
equilibrium. By dynamic equilibrium I mean that the two opposing field/energy flows are mostly in balance, but dynamically so
through the nodal points. The second ancillary field/energy flow is from front to back and vice versa also in dynamic equilibrium.
These are the three elementary components of the human morphogenic field or "Aura".
Thus, we have 7 nodal points in a liner array with two ancillary components 90 degrees to each other and perpendicular to the
linear array. How then do we get the pear-shaped, ellipsoid or spherical shape of the described human Aura? Please bear with me in
the following analysis. It is my experience that almost no-one is open fully in the #7 nodal point. This would mean that the
field/energy being emitted here is not as strong as that in the lower 6 and thus the pear-shape. When the soul is said to depart the
body, the break in the nodal linear array takes place between the 2nd and 3rd nodes. This connection is very strong in the living
person and also contributes to the pear-shape. The vast majority of living persons have the pear-shaped aura.
The ellipsoid and spherical shaped aura are rare. I will use the spherical shape, as it is the most regular shape, in the example that
follows. The 7 nodal points will be labeled as -3, -2, -1, 0, +1, +2, +3. The left-hand and right-hand labeled as -3,0, +3 and the
front-to-back as -3,0,+3 through the 0 node, both -2,0,+2 through the -2 and +2 nodes and -1, 0, +1 through the -3 and +3 nodes. This
will yield a spherical shape. The other shapes are derived from the various combinations of field/energy strengths.
Thus we might say that the aura is the triple integral:
-3S+3 dx -3S+3 dy -3S+3 dz, for the spherical shape.
Much work needs to be done on the nature of the dynamical equilibrium of the vectored components, the characteristics of the
vectors and the origin of the nodal points.

 

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