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Analysis of the effect of filtering elements on EEG and EMG recorded with high sensitivity channel based on nanoelectrodes.

Introduction

The psycho-physiological status of a person affects many aspects of life, its quality, well-being, further morphosis, and as a consequence, the lifespan [1]. At the same time, in addition to the ordinary consequences of the human activity, such as stresses, large, and long-term static exercises, the state of a person is affected by the dynamics of life, diseases of the musculoskeletal system and the nervous system. These factors eventually cause new pathologies and significantly reduce the quality of life. To maintain good health, to improve the body, and to increase the quality of life, it is necessary to improve the existing methods and technologies for diagnostics of the human body and create new ones to better understand functioning of the body, to identify signs of potential abnormalities in the early stages, or to identify the predisposition to diseases [2].

Filters in the system are extremely important when developing medical measuring devices. Filters and extraction of a certain channel are essential due to a large number of artifacts at high frequencies, which significantly reduce the signal quality and can cause misinterpretation of the data [3,4]. This can seriously affect the results of the study of the body or the functioning of the device conjugated with the measuring network, which can damage the device and cause inadequate response to stimulus or inaccurate medical diagnosis.

On the other hand, filters can become a negative aspect in the research, removing the information necessary for the fundamental research and development of new methods for studying. Modern development of the technology for manufacturing new materials, increasing the sensitivity and speed of measuring devices and their miniaturization upend stereotypes about the electrophysiology [5]. New sensitivity thresholds achieved for sensors allow a different evaluation of the electrical activity of the human biological systems.

Materials and Methods

Nanoelectrodes developed in Tomsk Polytechnic University are enabled to measure the values of the order of 100-200 nV [6-8]. The resolution achieved offers the challenge of a deep, practical, fundamental study of the electrical activity of the systems such as the musculoskeletal system, relationship between the peripheral nervous system and central nervous system, and the cardiovascular system. Studies at this level can lead to radical changes in the diagnosis of diseases and indepth understanding of the electrophysiology of the body.

A new level of resolution in the electrophysiological study requires the analysis of the effect of filtering elements on the bioelectric activity of the signal recorded with nanoelectrodes without filters.

Results and Discussion

Electroencephalography (EEG) signal filter influence analysis

To analyze this problem, we have investigated the effect of the filtering element on the signal obtained with devices without filters. The investigations were carried out for the data on EEG and electromyographic (EMG) studies.

The effect of filters was computer simulated with specially developed software which simulates the Fourier filters. For the electroencephalogram, the filter parameters were chosen by the band limit of the basic brain rhythms: alpha, beta, gamma, theta, and delta rhythm [9,10].

Alpha rhythm is predominant at rest. It is well recorded in the posterior areas of the brain at rest, in the dark, and with eyes closed. The rhythm frequency virtually does not change for a long period. The rhythm is recorded within the frequency range from 8 to 13 Hz, and the amplitude of the potential oscillations ranges from 30 to 150 [micro]V [9,10].

Beta rhythm manifests itself in the anterior areas of the brain, it occurs at concentration, when solving mental problems and visual stimulation. The rhythm is recorded within the frequency range from 14 to 30 Hz, and the amplitude of the potential oscillations ranges from 10 to 30 [micro]V. In case of brain pathologies, both amplitude and frequency of the rhythm change significantly [9,10].

Theta rhythm is recorded preferably in the parietal and temporal lobes of the brain, and it appears when falling asleep. The amplitude burst of the theta rhythm indicates brain pathology. The rhythm is recorded within the frequency range from 4 to 7 Hz, and the amplitude of the potential oscillations varies from 100 to 150 [micro]V [9,10].

Delta rhythm is recorded in the occipital area of the brain during deep sleep or anesthesia. The local manifestation of the rhythm in the waking state indicates the presence of focal cortical lesion. The rhythm is recorded within the frequency range from 0.5 to 3 Hz, and the amplitude of the potential oscillations varies from 150 to 200 [micro]V [9,10].

The Fourier filters with the passbands corresponding to the ranges of alpha, beta, delta, and theta rhythms have been designed [4,11,12]. EEGs and EMGs were recorded using the developed highly sensitive EEG based on nanoelectrodes [6-8]. The distinctive feature of this EEG is the lack of filtering elements, i.e., the received signal is not distorted by filtration. Figure 1 shows the EEG with a predominant beta rhythm. The patient in this study was subjected to stressful influences.

Figures 2-5 show the spectra of the EEG signal and the filter passbands for alpha, beta, delta, and theta rhythms.

The result of the effect of the Fourier filter with a passband from 13 to 35 Hz corresponding to the beta rhythm range is shown in Figure 6. Figure 7 shows a fragment of recording of the effect of the Fourier filter with a passband from 13 to 35 Hz corresponding to the beta rhythm range.

The analysis of the converted and initial signals indicates a strong information distortion. A zero-frequency component of the signal (Figure 7) and the data on the low frequency processes in the human brain disappear after filtering. For example, the initial EEG of a patient subjected to acute stress shows a potential jump (Figure 1), while this information is not available on the filtered signal. The results of the effects of filters with alpha, beta, and delta rhythm ranges are presented in Figure 8.

According to the research results, the filters cause significant distortions in the low frequency ranges, and the data on the low frequency processes occurring in the brain is almost completely removed by the filters.

EMG signal filter influence analysis

A similar experiment has been conducted to study the EMG signal of one of the channels during the study of muscle activity of the shoulder girdle of the right hand (Figure 9). The signal is recorded without input filtering elements used in conventional EMGs.

The frequency range of the EMG signal of the skeletal muscle functioning varies in accordance with the type of stimulation, movements, muscle tension, and muscle type. The total frequency range of the signal is limited to 1 kHz. In practice, the limitations vary in the range from 50 to 500 Hz and from 3 to 10,000 Hz [13].

The Fourier filter with a passband from 3 to 150 Hz is used to filter the signal. The upper frequency of the passband has been chosen in accordance with the characteristics of the EMGs used in practice. Figure 10 shows the EMG of the anterior fascicle of the deltoid muscle after the exposure to the bandpass filter of (3-150) Hz and the EMG spectrum.

According to the research results, the filters cause significant distortions in the EMG signal, and the data on the low frequency processes occurring in the brain is almost completely removed by the filters.

Conclusion

The performed analysis allows us to conclude that the use of filtering elements in the high resolution apparatus, in particular, for EEG and EMG studies, has a negative effect on the obtained data. Filtering of the first harmonics of a signal causes great distortion of the waveform and reduces the signal energy. The amplitude distortions of the signal vary from 2 to 15% at the expense of time delays and bandpass flatness for various types of filters.

Acknowledgment

The research was financially supported by the state program "Science" # 936 "Development of medical nanosensors and hardware-software complex for noninvasive micropotential registration during human biopotential real-time measuring in wide frequency band without signal averaging and filtration for electrophysiological research and artificial limb control".

The unique identifier of the contract: RFMEF157814X0032.

References

[1.] World Health Organization (2010) Global Recommendations on Physical Activity for Health. Geneva: WHO Press, p. 58.

[2.] Nichiporuk IA (2013) Features of the psychophysiological status of men with different levels of vestibular-autonomic resistance and their interrelation with etiology and pathogenesis of motion sickness. Human Physiology 5(39). Date Views 23.07.2014 (Available online: springer. com/static/pdf/900/art%253A1 0.1134%252 FS0362119713050101.pdf?auth66=1409216808_ d38aa568efb664ccfe6b9487edf67d06&ext=.pdf).

[3.] Schenk C, Tietze U, Gamm E (2008) Electronic Circuits: Handbook for Design and Application. Springer.

[4.] Analog Devices Inc. (2008) Basic Linear Design Handbook, Zumbahlen H (Ed.). Newnes, p. 960.

[5.] Ortiz-Catalan M, Branemark R, Hakansson B, Delbeke J (2012) On the viability of implantable electrodes for natural control of artificial limbs: review and discussion, BioMedical Engineering OnLine 11: 1-24.

[6.] Avdeeva DK, Lezhnina IA, Pen'kov PG, Rybalka SA, Uvarov AA (2011) Experimental studies of high sensitivity channel on nanoelectrodes for measuring human biopotential. Diagnostics Testing 11: 24-28.

[7.] Turushev NV, Grigor'ev MG, Avdeeva DK (2014) Device for human locomotor apparatus electroneuromyography. Journal of Radioelectronics 4: 1-6.

[8.] Avdeeva DK, Kazakov VYu, Kim VL, Pen'kov PG, Sadovnikov YuG (2011) Apparatus for nanoelectrodes to assess the physical and psycho-emotional state of students. Diagnostics Testing 13: 3-6.

[9.] Rusinov VS (1987) Biopotentials of the Human brain (Mathematical Analysis) Moscow: Medicine, p. 256.

[10.] Niedermeyer E, da Silva LS (2004) Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. Philadelphia: Lippincott Williams & Wilkins, p. 1283.

[11.] Hamming RW (2013) Digital Filters. New Jersey: Courier Dover Publications, p. 296.

[12.] LabView Measurements Manual (Date Views 24.07.2014 ni.com).

[13.] Pokrovskij VM, Korot'ko GF (2010) Human Physiology: Tutorial for Medical Students. Moscow: Medicine, p. 250.

Published: 2nd Nov 2014

Diana Konstantinovna Avdeeva, Nikita Vladimirovich Turushev, Maxim Leonidovich Ivanov, Mariya Aleksandrovna Yuzhakova, Michael Georgievich Grigoriev *

National Research Tomsk Polytechnic University, 30 Lenin Avenue, 634034, Tomsk, Russian Federation.

* Corresponding author: National Research Tomsk Polytechnic University, 30 Lenin Avenue, 634034 Tomsk,

Russian Federation.
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Title Annotation:Research Article
Author:Avdeeva, Diana Konstantinovna; Turushev, Nikita Vladimirovich; Ivanov, Maxim Leonidovich; Yuzhakova,
Publication:Biology and Medicine
Article Type:Report
Date:Jul 1, 2014
Words:1686
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