On Some Possibilities of using Microwave Radiometry in the Analysis

of Fluctuation Processes in Brain Tissue

Vladimir S. Kublanov, Mikhail V. Babich and Anton Yu. Dolganov

Ural Federal University, Yekaterinburg, Russian Federation

Keywords: Microwave Radiation, Heart Rate Variability, Neuro-electrostimulation, Brain Tissue.

Abstract: The article presents pilot study of the fluctuation processes in the brain tissues. Experimental setup consists

of the simultaneous record of microwave radiation in frequency range (3.4-4.2) GHz and heart rate variability

signals. As the functional load the neuro-electrostimulation was used. The preliminary results have shown

that the changes of the fluctuation process in brain tissues during the neuro-electrostimulation depends on the

changes in the autonomic nervous system, as evaluated by heart rate variability analysis.

1 INTRODUCTION

Currently, all existing scientific ideas about the

structure and principles of the human brain can be

represented as follows (Nicholls, Martin, Wallace, &

Fuchs, 2001).

The brain is a multilevel multifunctional central

nervous system designed to receive, transmit, process

and store information coming from organs, systems

and the environment.

The main informational structural and functional

units of the central nervous system are nerve cells

(neurons) of the brain, which by connecting together

using a large number of synapses form neuronal

networks.

Morphofunctional systems of the brain based on

neural networks provide functional cortical

neurodynamic integration of various regions and brain

formations of the brain (hemispheres, lobes,

convolutions, etc.), which is manifested at the level of

the whole brain by general bioelectric activity,

oscillatory processes and magnetoencephalographic

manifestations of the brain. Neurodynamic integration

forms the neural network cognitive functions of the

cerebral cortex.

As you gain new knowledge about the brain,

paradoxical contradictions about its work appear. So in

the works of A.S. Bryukhovetsky (Bryukhovetskiy,

2015) claims that the existing dogma that neural

networks process information in the brain is erroneous.

It has been hypothesized that the main structural

element of information switching in the nervous tissue

of the human brain is not a neuron, but information-

commutative modules that form the vertical

architecture of the nervous tissue of the human brain in

the form of information lines and information

channels, as well as a horizontal architecture in the

form central, intermediate and peripheral information

commutative. The information medium in the human

brain may be the pia mater, and the system

administrator and software carrier may be the

arachnoid. The dimensions of the information field still

require definition and refinement. Perhaps it is limited

only by subarachnoid or subdural space.

Therefore, the study of the proposed phenomenon,

primarily in the experiment by attracting new

information-measuring methods, is undoubtedly

relevant.

In this paper, we consider some of the possibilities

of microwave radiothermography to solve this

problem.

2 MATERIALS AND METHODS

Pilot studies of electromagnetic radiation in brain

structures have been carried out, in which signals can

be formed in accordance with the hypothesis of

A.S. Bryukhovetsky. Five relatively healthy volunteer

subjects took part in the studies. Before the experiment,

each participant was informed about the progress of the

experiment and agreed to participate in the experiment.

During the study, microwave radiation (MR) and heart

rate variability (HRV) signals were recorded.

Kublanov, V., Babich, M. and Dolganov, A.

On Some Possibilities of using Microwave Radiometry in the Analysis of Fluctuation Processes in Brain Tissue.

DOI: 10.5220/0009377204170420

In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 1: BIODEVICES, pages 417-420

ISBN: 978-989-758-398-8; ISSN: 2184-4305

417

2.1 Experimental Setup

MR signals were recorded using an experimental MR8

radiometer. Radiometer Specifications:

 operating frequency range (3.4-4.2) GHz;

 passband of the low-pass filter 1 Hz;

 normalized fluctuation sensitivity of the radiometer

0.1 K.

The operating frequency range was chosen in order

to be able to obtain information about the brightness

temperature in the area of the soft and arachnoid

meninges.

A radiometer with an antenna was located on the

subject’s head. The antenna was located in the area of

the frontal lobe. The location corresponded to the F4

electrode in the international 10–20 EEG electrode

placement system.

To provide protection against interference, the

antenna was shielded with a metallic fabric. The

experiments were carried out with the lights off. There

were no mobile phones in the experiment room.

HRV signals were recorded using the

corresponding recording channel of the Eencephalan-

131-03 electroencephalograph-analyzer.

For neuro-electrostimulation, we used a device

approved for use in Russia - the sympathetic nervous

system activity corrector “SYMPATHOCOR-

01”(Kublanov, 2008). One of the clinical effects of this

device is to improve blood circulation in the vessels of

the brain.

2.2 Timeline of the Experiment

The experiment consisted of five successive steps.

At the first stage (F), the participants sat calmly at

rest without any functional load. The duration of the

stage was 20 minutes.

At the second stage (S1), the participants were

exposed to the “SYMPATHOCOR-01” device. The

target of stimulation is the ganglia of the sympathetic

nervous system. The duration of the stage was 5

minutes.

The third step (B) is a five-minute break, without

any functional load.

At the fourth stage (S2), the participants were

exposed to “SYMPATHOCOR-01” device. The target

of stimulation is the ganglia of the sympathetic nervous

system. The duration of the stage was 5 minutes.

The fifth stage (A) is the aftereffect. The duration

of the stage was 10 minutes.

It is worth noting that neuro-electrostimulation

causes interference on the ECG signal. Therefore, in

the future, HRV signals in steps S1 and S2 were not

analyzed.

2.3 Methods of Processing

Continuous wavelet analysis was chosen as the main

processing method. The processing of biosignals was

carried out in in-house software written in python. The

main libraries used were the NumPy library for general

mathematical transformations and the PyWT library

for numerical computation of the continuous wavelet

transform.

An eighth-order Gaussian wavelet was used as the

basic wavelet. (Addison, 2005). For the HRV signal,

the signal was preliminarily interpolated to a uniform

time grid. Interpolation was carried out using the linear

interpolation method. The grid pitch was 0.25 s.

For each biosignal, a wavelet analysis was

performed in certain time-frequency windows. The

following center frequencies of the spectral filters

(0.03, 0.02, 0.01, 0.006, 0.005) Hz were selected for

the MR signal. These frequencies correspond to

fluctuations with periods of 30 s, 50 s, 100 s, 150 s, 200

s, respectively.

For the HRV signal, spectral analysis was

performed in the ranges HF, LF, and VLF (Malik,

1996). In addition, two VLF subbands with central

frequencies of 0.01 and 0.02 Hz, which are associated

with a change in cognitive loads, were analyzed.

(Togo, Kiyono, Struzik, & Yamamoto, 2006).

As a result of using the wavelet transform, wavelet

spectrograms were obtained. An example of the

obtained spectrograms for the MR signal and the HRV

signal for one of the subjects is presented in Figures 1

and 2, respectively. Vertical black lines indicate the

boundaries of the stages of the experiment.

Figure 1: Wavelet-spectrogram of the MR signal.

Figure 2: Wavelet-spectrogram of the HRV signal.

NDNSNT 2020 - Special Session on Non-invasive Diagnosis and Neuro-stimulation in Neurorehabilitation Tasks

418

After obtaining wavelet spectrograms, the inverse

wavelet transform was carried out in the frequency

ranges of interest. Examples of signals after the inverse

wavelet transform are shown in Figures 3 and 4,

respectively.

Figure 3: Inverse Wavelet-transform of the MR signal.

Figure 4: Inverse Wavelet-transform of the HRV signal.

3 RESULTS

To assess changes in the nature of fluctuations, a

window estimate of the amplitude of the received

signals was performed. For this, the standard deviation

of the signals obtained as a result of applying the

inverse wavelet transform was estimated. The width of

the window in which the standard deviation was

estimated was 60 seconds. At the same time, the

window step was 30 seconds.

Tables 1 and 2 show the average values of the

fluctuation amplitude estimates for the MR and HRV

signals, respectively. In tables 1 and 2, column T

denotes frequency windows (and their corresponding

fluctuation periods).

In tables 1 and 2, bold marked significant changes

in the amplitude of the fluctuations.

It should be noted that for different subjects’ dif-

Table 1: MR Spectral Powers (K).

Stage of the Experiment

T F S1 B S2 A

Subject1

0.0068 ±

0.0022

0.0079 ±

0.0013

0.0097 ±

0.0047

0.0052 ±

0.0014

0.0063 ±

0.0015

0.006 ±

0.0014

0.0072 ±

0.0012

0.0072 ±

0.0027

0.0047 ±

0.0007

0.0062 ±

0.0019

100

0.003 ±

0.0011

0.0029 ±

0.0006

0.0022 ±

0.0006

0.0024 ±

0.0013

0.0025 ±

0.0008

150

0.0021 ±

0.0009

0.0031 ±

0.0008

0.0015 ±

0.0005

0.003 ±

0.0004

0.0021 ±

0.001

200

0.0013 ±

0.0006

0.0016 ±

0.0006

0.002 ±

0.0006

0.0017 ±

0.0003

0.0017 ±

0.0007

Subject2

0.0068 ±

0.0027

0.0055 ±

0.0009

0.0078 ±

0.0024

0.0070±

0.0015

0.0075 ±

0.0019

0.0064 ±

0.0017

0.0048 ±

0.0014

0.0052 ±

0.0032

0.0056 ±

0.0019

0.0062 ±

0.0012

100

0.0032 ±

0.0013

0.0031 ±

0.0011

0.0018 ±

0.0006

0.0027 ±

0.0024

0.0032 ±

0.0017

150

0.0027 ±

0.0011

0.0025 ±

0.0013

0.0019 ±

0.0009

0.0028 ±

0.0017

0.0029 ±

0.0015

200

0.0018 ±

0.0007

0.0021 ±

0.0009

0.0014 ±

0.0009

0.0018 ±

0.0006

0.0020 ±

0.0009

Subject3

0.0074 ±

0.0022

0.0049 ±

0.0011

0.0067 ±

0.0013

0.0083 ±

0.0027

0.0059 ±

0.0019

0.0065 ±

0.0021

0.0046 ±

0.0008

0.0075 ±

0.0017

0.0057 ±

0.002

0.0059 ±

0.0022

100

0.0034 ±

0.0013

0.0031 ±

0.0009

0.0029 ±

0.0015

0.0018 ±

0.0008

0.0024 ±

0.0007

150

0.0025 ±

0.0019

0.0023 ±

0.0005

0.0018 ±

0.0005

0.0013 ±

0.0007

0.0013 ±

0.0003

200

0.0017 ±

0.0012

0.0018 ±

0.0011

0.0017 ±

0.0009

0.0015 ±

0.0005

0.0009 ±

0.0004

Subject4

0.0074 ±

0.0024

0.0054 ±

0.0033

0.0059 ±

0.0019

0.0053 ±

0.001

0.0054 ±

0.0012

0.0054 ±

0.0018

0.0063 ±

0.0021

0.0071 ±

0.0025

0.0050 ±

0.0013

0.0053 ±

0.0014

100

0.003 ±

0.0015

0.0027 ±

0.0009

0.0033 ±

0.001

0.0026 ±

0.0007

0.0031 ±

0.0011

150

0.0031 ±

0.0014

0.0011 ±

0.0003

0.0026 ±

0.0009

0.0024 ±

0.0004

0.0028 ±

0.001

200

0.0019 ±

0.001

0.0006 ±

0.0003

0.0018 ±

0.0007

0.0016 ±

0.0005

0.0014 ±

0.0004

Subject5

0.0083 ±

0.0023

0.0082 ±

0.0024

0.0068 ±

0.0016

0.0071 ±

0.0021

0.0062 ±

0.0014

0.0062 ±

0.0015

0.0058 ±

0.0014

0.0059 ±

0.0012

0.0046 ±

0.0013

0.0075 ±

0.0025

100

0.0021 ±

0.001

0.002 ±

0.0004

0.0025 ±

0.0009

0.0023 ±

0.0006

0.0028 ±

0.001

150

0.0013 ±

0.0007

0.0026 ±

0.0006

0.0018 ±

0.0014

0.0021 ±

0.0012

0.0025 ±

0.0007

200

0.0013 ±

0.0005

0.0022 ±

0.0005

0.0018 ±

0.0009

0.0023 ±

0.0009

0.0014 ±

0.0006

ferent changes in the nature of fluctuations of MR

signals were observed depending on changes in the

HRV signal.

On Some Possibilities of using Microwave Radiometry in the Analysis of Fluctuation Processes in Brain Tissue

419

Table 2: HRV Spectral Powers (ms).

Stage of the Experiment

T F S1 B S2 A

Subject1

HF 8.5 ± 1.6 - 7.54 ± 0.69 - 8.26 ± 1.08

LF 17.38 ± 6.38 - 15.57 ± 5.52 - 19.82 ± 6.14

50 5.06 ± 2.43 - 4.8 ± 1.97 -

7.72 ± 2.84

100 4.85 ± 1.75 -

7.2 ± 2.6

- 4.98 ± 2.25

VLF 13.04 ± 5.15 - 13.85 ± 4.8 - 15.42 ± 4.3

Subject2

HF 3.36 ± 0.65 - 3.2 ± 0.54 - 3.53 ± 0.38

LF 4.03 ± 1.81 - 5.13 ± 2.25 - 5.75 ± 1.75

50 1.86 ± 0.57 -

3.04 ± 1.17 - 2.77 ± 1.25

100 1.35 ± 0.55 -

2.88 ± 1.46 - 2.3 ± 0.84

VLF 4.31 ± 1.85 - 7.75 ± 4.2 - 6.87 ± 3.27

Subject3

HF 5.03 ± 1.46 - 4.81 ± 0.55 -

12.45 ± 10.47

LF 9.94 ± 4.49 -

13.9 ± 4.86 - 22.46 ± 8.05

50 6.69 ± 3.39 - 6.03 ± 1.52 - 9.8 ± 4.13

100 3.32 ± 1.31 - 2.7 ± 0.98 - 5.94 ± 2.63

VLF 12.24 ± 6.7 - 11.68 ± 4.13 -

18.85 ± 7.84

Subject4

HF 13.55 ± 1.82 -

9.04 ± 1.1

- 12.17 ± 1.7

LF 25.55 ± 6.45 - 29.97 ± 6.33 - 26.44 ± 6.54

50 10.57 ± 3.9 - 12.06 ± 2.47 - 12.22 ± 5.55

100 6.74 ± 2.95 -

4.37 ± 2.2

- 8.43 ± 3.5

VLF 22.64 ± 8.96 - 20.15 ± 6.66 - 24.03 ± 11.65

Subject5

HF 2.63 ± 0.43 - 2.75 ± 0.43 - 2.95 ± 0.66

LF 4.98 ± 1.34 - 4.48 ± 1.22 - 3.83 ± 1.04

50 3.07 ± 1.15 - 2.68 ± 0.56 - 3.05 ± 1.44

100 3.29 ± 1.42 -

2.41 ± 1.17 - 1.07 ± 0.58

VLF 8.49 ± 3.2 - 6.69 ± 2.85 - 5.4 ± 2.35

So for the first subject, an increase in the amplitude

of oscillations was noted with periods of 100 seconds

during the break and with periods of 50 seconds during

the aftereffect for the HRV signal. At the same time,

significant changes were noted in the MR signal during

the break for periods of fluctuations of 100 and 150

seconds.

For the third subject, a significant increase in

fluctuations was observed in the LF range during the

break and during the aftereffect. For the MR signal, a

significant decrease in amplitude was noted for periods

of fluctuations of 150 seconds after the break.

For the fourth subject, there was a significant

decrease in fluctuations in the HF range and

fluctuations with a period of 100 seconds during a

break for the HRV signal. Moreover, in the MR

signals, a decrease in amplitude was noted during the

first stimulation for periods of fluctuations of 150 and

200 seconds. An increase in the amplitude of

fluctuations with a period of 50 seconds was also noted

during the first stimulation and interruption.

4 CONCLUSIONS

The article presents pilot study of the fluctuation

processes in the brain tissues. Experimental setup

consists of the simultaneous record of microwave

radiation in frequency range (3.4-4.2) GHz and heart

rate variability signals. As the functional load the

neuro-electrostimulation was used. The preliminary

results have shown that the changes of the fluctuation

process in brain tissues during the neuro-

electrostimulation depends on the changes in the

autonomic nervous system, as evaluated by heart rate

variability analysis.

ACKNOWLEDGEMENTS

The reported study was funded by RFBR according to

the research project № 18-29-02052 and supported by

Act 211 Government of the Russian Federation,

contract № 02.A03.21.0006.

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