Scientific Research

An Endeavor to Illustrate an Objective Evidence for the Action of Homeopathic Medicines by Measuring Physiological Variability in Human Body Temperature

Homeopathic medicines of 200C potency are applied orally to human subjects. ‘Physiological variability in temperature’ from skin of forearm is measured with the help of temperature data logger and water/soil temperature sensor. Temperature readings are taken at an interval of 1 second for 5 minutes. Time series spectral analysis is performed by using Statistical processing software. The statistical procedures like Auto Regressive Spectrum (ARS) and Parametric prediction and reconstruction (PPR) are used to study the change in temperature variability. There is marked change in temperature variability before and after applied homeopathic medicine.

Key words: Homeopathic medicine, Physiological variability in temperature, temperature data logger, water/soil sensor, AR Spectrum, Parametric Reconstruction and prediction

Aims and objectives:


1.      To show an objective evidence for the
action of Homeopathic medicine by measuring physiological variability
in human body temperature.


1.      To draw signature for individual Homeopathic

2.      To find out a unified method to select
homeopathic medicine for diseased individuals.

3.      To introduce objective measures into Homeopathic
system of medicine like data loggers and sensors to measure physiological
variability for selection of Homeopathic medicine.

Introduction and review:

Homeopathy is a well-described, scientifically based system of approaching
health and disease. "Scientific" because the insights
are based on reproducible experiments. "Well-described"
because from these observations a number of precise basic fundamental
rules became evident, first among them the "similarity principle.12
"But lack of objective evidence for the action of Homeopathic
medicine bestow an opportunity for skeptics to comment ridiculously.

Although homeopathy is only 200 years old, it is now practiced in a variety
of ways, most of which bear little resemblance to what Hahnemann
taught. In the public mind the word "homeopathy" has
become so vague that for some it means only an "alternative
medicine" and for others a combination of homeopathic medicines
that you buy in the health food store, one mixture for allergies,
another for headache, etc12. There by it needs a unified
method to select similar homeopathic medicine for a patient’s
disturbed energy field.

Our earth by virtue of a hidden invisible energy, carries the moon
around her in twenty eight days and several hours, and the moon
alternately, in definite fixed hours (deducting certain differences
which occur with the full and new moon) raises our northern seas
to flood tide and again correspondingly lowers them to ebb.3

According to the homeopathic way of thinking, a disease originates from
a disturbance of the patient’s "vital force." This is
the life force energy that sustains life. As the origin of disease
occurs on this energetic level, the homeopathic remedy has also
to be on this level.12 The current study provides objective
evidence for the action of homeopathic medicine and also deals
with identification of similar homeopathic medicine for the patient’s
disturbed life force by means of objective measures, “Data loggers
and Sensors”.

This study facilitates in the drawing signatures for individual Homeopathic
medicines. This study also gives you an idea about approach, to
develop a common method to select accurate Homeopathic medicine
for diseased individuals.

In order to recognize the behavior of homeopathic medicines, which
are applied orally, it is attempted to study the natural regulatory
mechanisms in the Human body as well as in nature.

In this regard Biorhythms, “occurrence of cyclical biological events”,
have supported the hypothesis that “Homeopathic medicines produce
cyclical change in the human body”.  Extension of this initiative
guide to the study of the function of the Hypothalamus “the seat
of biorhythms”.

The hypothalamus is responsible for certain metabolic processes and other
activities of the Autonomic Nervous System. It synthesizes and
secretes neuro-hormones, often called hypothalamic-releasing hormones,
and these in turn stimulate or inhibit the secretion of pituitary
hormones. The hypothalamus controls body temperature, hunger,
thirst, fatigue, and circadian cycles

The tiny dose of Homoeopathic Medicines to some extent
move up the thermoregulatory set point located in the Hypothalamus
of living beings naturally required for the rise in body temperature
in order to deactivate the microbes (Disease Forces).13

The effects of tiny doses of Homoeopathic Medicines simulate the Biologic
Activities of Pyrogenic Cytokines like IL-1, TNFa, AND IL-6 and
INFs. The potentised dose of similar Homeopathic Medicines (just
like PGE2) is the most potent of fever-producing means, without
inflicting any injurious effect to the sick living being when
prescribed on symptom similarity and in infinitesimal low doses.13

So one of the regulatory functions of the Hypothalamus, ‘Thermoregulation
in human subjects’ is taken as the primary subject to understand
the action of Homeopathic medicine. The parameter “Physiological
variability in human body temperature
” (Vital Heat) is selected
for the study.

The history of the study of variations in the physiological parameters
goes back to 18th century, when Stephen Holes documented
beat to beat variability. He related spontaneous fluctuations
in the peripheral blood flow to adjustment in the continuous circulation
by the thermoregulatory system.2

Thermoregulation is the ability of an organism to keep its body temperature
within certain boundaries, even when the surrounding temperature
is very different. This process is one aspect of homeostasis:
a dynamic state of stability between an animal’s internal environment
and its external environment.10

The organism that thermoregulates is one that keeps its core body
temperature within certain limits. It was not until the introduction
of thermometers that any exact data on the temperature of animals
could be obtained. It was then found that local differences were
present, since heat production and heat loss vary considerably
in different parts of the body, although the circulation of the
blood tends to bring about a mean temperature of the internal
parts. Hence it is important to identify the parts of the body
that most closely reflect the temperature of the internal organs.
Also, for such results to be comparable, the measurements must
be conducted under comparable conditions. The rectum has traditionally
been considered to reflect most accurately the temperature of
internal parts, or in some cases of sex or species, the vagina,
uterus or bladder. Occasionally the temperature of the urine as
it leaves the urethra may be of use. More usually the temperature
is taken in the mouth, axilla, ear or groin.10

The body owns a regulatory system that keeps body temperature within a
close range. The centers of this system are located in the hypothalamus.
Temperature sensors are found in the preoptic and anterior hypothalamic
nuclei. More sensors are contained in the skin and in a
few deep tissues in the body. The temperature is regulated by
a nervous feedback mechanism. The signals from the sensors are
evaluated in the posterior hypothalamus.5

In this study, temperature is measured from the skin of the forearm.  As
this study doesn’t require exact values, the variability in temperature
is measured from skin. “It is supposed that Homeopathic medicines
produce change in temperature variability”.

Materials and methods:

1. Equipment:

A.      Temperature Data logger,

B.     Water/Soil Temperature Sensors ,

C.     PC & Green line data logging software.

D.     Human Subjects.

E.     Homeopathic Medicines of 200C Dilution.

F.      Statistical Processing Software for Data

2. Temperature Datalogger Specifications:

Measurement range: -40  to 1580 F

Accuracy: + or – 0.63 0F ( from 320  to
1220 F)

Resolution : 0.050 F at 770 F

Response time in airflow of 1 m/s :  6 minutes

Operating temperature: -40  to 1580 F(logging),
320  to 1220 F(Launching/Readout)

Memory: 64 bytes

Weight: 46g(1.6 oz)

3. Water/Soil Temperature Sensor Specifications:

Measurement range: -400 to 1220 F
in water, -400 to 2120 F in air.

Accuracy: + or – 0.450 at 650 F

Resolution: 0.050 F at 680 F

Response time in airflow of 1 m/s:  3 minutes, 30 seconds in water.

Operating range: sensor tip and cable immersion in fresh water
up to +1220 F.

Material: Housing stainless steel sensor tip.

Probe dimensions: 0.5cm X 2.5cm

4. Methodology:

The experiment was conducted with Human subjects. Freshly medicated 6-9
Homeopathic pills were applied orally half an hour prior to the
acquired temperature readings. Temperature readings were obtained
from the skin of the forearm of the subjects. A water soil sensor
was connected to the skin by means of cellophane tape. Readings
were taken in the sitting posture. Subjects were instructed not
to move while obtaining readings. Method of connecting data logger
and sensor was shown in Fig 2.

Temperature readings were taken at an interval of 1 second for 5 minutes.
Each data file of a subject contains 300 readings. After obtained
readings, data can be exported to an excel file. Each data file
contains 300 temperature reading points i.e. Temperature variability

5.      Protocol:

Two types of protocols are followed in these experiments.

    5.1. With Placebo control: Subjects are divided into the medicine
group and the placebo group. Homeopathic medicine is applied
to one group and placebo to another group. E.g.: Exp No:1, Exp
No:2, Exp no:6, & Exp no:7.

    5.2. Without Placebo control: Subjects are not divided
into groups.  Reading are taken from the same subjects before
and after applied homeopathic medicine. E,g,: Exp No 3, Exp
No:4, Exp no:5, Exp no:8 & Exp No:9.

6. Statistical Analysis:

The temperature variability data is processed with statistical processing
software. The statistical procedures Auto regressive spectrum
(AR Spectrum) and Parametric Reconstruction and Prediction
(PRP) are used for the data analysis. With these procedures
the time series data can be converted into frequency domain

In statistical signal processing and physics, the spectral density,
power spectral density (PSD), or energy spectral
(ESD), is a positive real function of a frequency
variable associated with a stationary stochastic process, or
a deterministic function of time, which has dimensions of power
per Hz, or energy per Hz. It is often called simply the spectrum
of the signal. Intuitively, the spectral density captures the
frequency content of a stochastic process and helps identify

In physics, the signal is usually a wave, such as an electromagnetic wave,
random vibration, or an acoustic wave. The spectral density
of the wave, when multiplied by an appropriate factor, will
give the power carried by the wave, per unit frequency, known
as the power spectral density (PSD) of the signal. Power
spectral density is commonly expressed in watts per hertz (W/Hz)
or dBm/Hz.14

For voltage signals, it is customary to use units of V2Hz−1
for PSD, and V2sHz−1 for ESD or

For random vibration analysis, units of g2Hz−1
are sometimes used for acceleration spectral density.14

6.1 Autoregressive model:


The notation AR(p) refers to the autoregressive model of order p.
The AR(p) model is defined as

 X_t = c + sum_{i=1}^p varphi_i X_{t-i}+ varepsilon_t ,

where varphi_1, ldots, varphi_pare the parameters of the model,
c is a constant and varepsilon_t is white noise. The constant term is omitted by many authors
for simplicity.

An autoregressive
model can thus be viewed as the output of an all-pole infinite
impulse response filter whose input is white noise.

Some constraints are necessary on the values of the parameters
of this model in order that the model remains wide-sense stationary.8
For example, processes in the AR(1) model with |φ1|
≥ 1 are not stationary. More generally, for an AR(p)
model to be wide-sense stationary, the roots of the polynomial textstyle z^p - sum_{i=1}^p varphi_i z^{p-i} must lie within the unit
circle, i.e., each root zi must satisfy | zi
| < 1.

Parametric Interpolation and Prediction:

Parametric Interpolation and Prediction is a powerful composite
algorithm that generates a parametric (sinusoids or damped sinusoids)
model of the signal. The algorithm has three stages. In the
first stage, an AR, Prony, Eigenanalysis, or Fourier procedure
is used to estimate the frequencies and component count. In
the second stage a linear fit is made to determine the amplitudes
and phases. These are the starting estimates for the third stage,
the non-linear optimization. This option combines all of the
steps into a single integrated procedure for interpolation and

Non-Linear Optimization Plot:

following is the non-linear optimization graph for data consisting
of three sinusoids and noise. The three component functions
are shown in the Y-axis plot. The Y2 plot contains the fitted
curve and the data that were fitted.

3: Example graph of parametric prediction.


The following
are results of the experiments conducted with various Homeopathic

No: 1

with Gelsemium 200C:

In case of experiment with Gelsemium, there were 8 subjects, 4
days, and two subjects per day.  This experiment was conducted with
2 sensors.

4 : Gelsemium 200C and Placebo Readings, original temperature
variability graphs.

In the following figure:5 there are two columns: left side are
Gelsemium 200C subjects, right are Placebo subjects. Placebo control
is used in this experiment.

Day 1:

Sensor 1 –
subject 1 – Gelsemium 200 – time 07.40.14 am – 08.00.00am

Sensor 2 –
subject 2 – placebo – time 07.40.14 am – 08.00.00am

Day 2:

Sensor 1 –
subject 3 – Gelsemium 200 – time 07.40.14 am – 08.00.00am

Sensor 2 – subject 4 – placebo – time 07.40.14 am – 08.00.00am

5: AR Spectrum Gelsemium and placebo given subjects.

Day 3:

Sensor 2 –
subject 5 – Gelsemium 200 – time 07.40.14 am – 08.00.00am

Sensor 1 –
subject 6 – placebo – time 07.40.14 am – 08.00.00am

Day 4:

Sensor 2 –
subject 7 – Gelsemium 200 – time 07.40.14 am – 08.00.00am

Sensor 1 –
subject 8 – placebo – time 07.40.14 am – 08.00.00am

In this Experiment,
the skin temperature is recorded form the skin of the forearm
for 10 minutes at an interval of 2 seconds.

In case of
Gelsemium subjects – observe, peaks in medium frequency are
at the same frequency level. But amplitude is different in case
of sensor 2 Gelsemium subjects (first column last two graphs).


with Sulphur 200C:

case of experiment with Sulphur 200C, there are 6 subjects,
4 days and two subjects per day. This experiment also used 2

6:  Temperature variability graphs of Sulphur 200C and Placebo

1st day:

Sensor 1 –
subject 1 – Sulphur 200 – time 8.00.00am – 8.10.00am

Sensor 2 –
subject 2 – placebo – time 8.00.00am – 8.10.00am

2nd day:

Sensor 1 –
subject 3 – Sulphur 200 – time 8.00.00am – 8.10.00am

Sensor 2 –
subject 4 – placebo – time 8.00.00am – 8.10.00am

7: AR Spectrum for Sulphur 200C and Placebo subjects.

day 3:

Sensor 1 –
subject 5 – Sulphur 200 – time 8.00.00am – 8.10.00am

Sensor 2 –
subject 2 – placebo – time 8.00.00am – 8.10.00am

Day 4:

Sensor 1 –
subject 6 -Sulphur 200 – time 8.00.00am – 8.10.00am

Sensor 2 –
subject 2 – placebo – time 8.00.00am – 8.10.00am

In this experiment
skin temperature was recorded from the forearm for 10 minutes
at an interval of 1 second.

Figure: 7
showing 2 columns of graphs. The left side column is Sulphur
given subjects. The right side is of placebo given subjects.
The medium frequency (marked) shows peaks at the same frequency
level, amplitude also does not vary much.

In case of
the experiment with Gelsemium, placebo subjects applied alcohol
mixed sugar pills.

In case of
experiment with Sulphur, placebo subjects applied just sugar

Note: alcohol
also produces peaks in medium frequency but not at a fixed frequency
level as the medicine produced. Observe the above graphs. (Fig:
5, Fig: 7)

Parametric reconstruction and prediction graph for the above Sulphur
4 subjects: This graph shows 2 persons (Subject 2 and Subject 4)
responded well to Sulphur 200C.

    Figure 8: Parametric Spectrum for Sulphur 200C given

About the author

Devendra Kumar Munta

Leave a Comment