Method of early encephalographic diagnostics of parkinson disease

FIELD: medicine.

SUBSTANCE: invention relates to medicine. Electroencephalogram (EEG) is registered in background mode, spectrograms are calculated by means of wavelet conversion with Morlet mother function. Frequency ranges of leading EEG rhythms are determined by finding values of coordinate minimums by frequency of envelope projections of wavelet spectrograms on "amplitude-frequency" coordinate plane. In frequency ranges times Ti of spectrogram peak appearance are determined by values of positions of maximums on envelope projection of wavelet spectrograms. Frequencies Fi and amplitudes Ai of peaks of spectrograms, which correspond to values of times of their appearance in each frequency range, are determined. For each discretisation window with ΔT, ΔF parameters, obtained by fragmentation of duration T and frequency range F of EEG registration, values ΣAi of sums of amplitudes of spectrogram peaks are calculated. If frequency increases, range of frequencies of peaks expands, interhemispheric asymmetry of electric activity is detected, early stage of Parkinson disease is diagnosed.

EFFECT: method makes it possible to increase reliability of determination of early stage of PD.

4 cl, 9 dwg, 1 ex

 

The invention relates to medicine and can be used in the diagnosis in neurology by electrophysiological means.

Neurodegenerative diseases are related to a severe and incurable diseases caused by pathological misalignment of various brain structures. Timely and early diagnosis can improve the quality of life of patients. Describes an invention aimed at diagnosing Parkinson's disease (hereafter BP) using provocative pharmacological test and subsequent diagnosis of disturbances of tone, which allows to identify the latent phase of the BP (EN 2318437 C1, Ugryumov, 10.03.2006), including diagnosis by physiological signals.

Encephalographic equipment is well designed tool for the analysis of neurodegenerative diseases, including PD.

Thus, the application (WO 2004066832 (A2), CARBONCINI et al., 12.08.2004) described the method and its implementation in the form of algorithmic handling the electromyographic (EMG) signals based on wavelet cross-correlation analysis. To calculate the wavelet cross-spectrograms used complex wavelet Morlaix. Integrating local wavelet cross-spectrograms time and frequency gives the full cross-correlation energy, which is the index of the m BP, quantity that characterizes the stage of severity of the disease.

EEG studies (EEG) is known as one of the most promising tools for the diagnosis of neurodegenerative diseases (see "Neurodegenerative diseases: fundamental and applied aspects / Edited. edit Mwhoa. - M.: Nauka, 2010, s.112-129). On the basis of time-frequency analysis, it is concluded that in the analysis of EEG of patients with BP separation at fixed frequency bands, theta, alpha, beta, etc. does not reflect the characteristics of the EEG changes in this pathology. You must look for another method of statistical evaluation of the frequency-temporal dynamics of EEG, which reflects the PSU disruption of the electrical activity of the brain. The creation of new assessment methods dynamically electrical activity in different areas of the cortex and automation developed and used estimation methods for time-frequency dynamics of EEG create the basis for a quantitative assessment of changes in different forms and stages of PD. This is especially important to search for reliable markers of early stages of disease development.

It is known that a characteristic feature of Parkinson's disease (BP) is usually recognized syndrome of disintegration, which is manifested at different system levels (movement disorders, autonomic, neurohumoral the th disintegration, emotional and mental disorders). I think that with the early forms of Parkinson's disease disorders at different levels of the nervous system are mostly functional neurodynamic character. This disintegration of the normal regulatory relationships can be reflected also in the dynamics of brain electrical activity (see, for example, Staffers D., Bosboom J.L.W., Dejen J.B., Wolters E.C., H.W. Berendse, Stam C.J. Slowing of oscillatory brain activity is a stable characteristic of PA's disease without dementia. Brain, 2007; 130: 1847-1860).

A known method of assessing the frequency-temporal dynamics of EEG using wavelet analysis, which revealed signs characterizing a physiological condition of the patient. Thus, in the invention (SA 2393412 (A1), GUY DUMONT et al., 12.01.2004) described a method and apparatus EEG monitoring hypnotic state of the patient during anesthesia for differentiating sleep States and predict the duration of an epileptic fit. The characteristic of the hypnotic state is determined by the presence of electrical activity in the gamma-band spectrograms of the electroencephalogram.

In the invention (WO 2006034024 (A2), CAUSEVIC et al., 30.03.2006) described a method for filtering of EEG signals using binary decomposition coefficients of the discrete complex wavelet transform. However, this solution only allows to improve the visual perception of the EEG, but not devozione to highlight signs of neurological disorders.

In the invention (WO 2005117693 (A1), GUTTAG et al., Facilities 15.12.2005) described a method and system for detection of seizures in epilepsy. The wavelet spectrum of the EEG signal is divided into frequency bands, each of which contains one or more eigenvectors. The onset of the attack is determined by the classification of these vectors on the basis of comparison with pre-defined characteristics of the waveform when fit and in its absence. Thus, in the invention (US 2011082381 (A1), UTHMAN et al., 07.04.2011) described a method for detection of seizure under absense epilepsy based on wavelet analysis of the frequency properties of seizure. However, these methods do not involve the diagnosis of BP.

In the invention (EN 2332160 C1, Turov and others, 27.08.2008) described a method of processing EEG. Spend processing signals using continuous wavelet transform. In advance of the specified time intervals are calculated frequency local minima wavelet spectrograms. The frequency ranges between these local minima are defined as physiologically relevant ranges for a given time interval. Thus, we determine the time dependence of physiologically relevant frequency ranges. However, this method is not given information about the search criteria for any of neurological disorders.

In the patent (US 7697979, Martinerie, et al., 13.04.2010) patented a method of monitoring the real-time synchronization of EEG signals as a method of diagnosis of brain diseases, in particular epilepsy, and the study of cognitive processes. The method consists in the calculation of phase synchronization of EEG signals in parts of the brain using the following steps: 1) calculate the phase of the signal using the Hilbert transform, 2) calculation of the phase difference between pairs of signals, and 3) search time intervals in which the phase difference is statistically little changed compared with the phase difference of the synthesized random signals. The method most appropriate to the study and diagnosis of epilepsy. The method may be useful in the analysis of cognitive tests for the diagnosis of neurodegenerative diseases such as Parkinson's, Alzheimer's and others, but these patent issues are not considered.

In the above sources of information describes the different methods of study and identifying neurologic disease based on EEG analysis, including the use of wavelet transformations, however, are not informed about the possibility of detection of BP in the early stages. In the publications of the authors shows only the ability to diagnose early stages of Parkinson's disease, however, the essence of the method of selection of parameters of wavelet spectrograms not disclosed. Thus, article Aveba, Hudcova, Mscorlib, Wowhow. "TIME-FREQUENCY ANALYSIS of BRAIN ELECTRICAL ACTIVITY IN PATIENTS WITH DISEASE of the PARKS IS SONA (BP)" (information from 07.12.2010, final conference IWND and NF RAS for 2010, http://www.ihna.ru/news/?ELEMENT_ID=2842) reported that using wavelet transform found significant disorganization of the dominant EEG rhythm, especially in the later stages of the disease. EEG at PSU consists of separate peaks, unstable frequency, occur randomly in time. On the 1st stage of the disease in EEG there is a General shift towards high frequencies, 2 and 3 stages BP is characterized by a General slowing of the frequency. At all stages of the disease was discovered interhemispheric EEG asymmetry in the Central and parietal regions of the cerebral cortex.

In the work Wowhow, Hudsonica. "DEVELOPMENT AND EXPERIMENTAL VERIFICATION of METHODS for the isolation of MARKERS of EARLY PARKINSON's disease IN ELECTROENCEPHALOGRAPHIC evaluation of PATIENTS", Proc. of Fundamental science for medicine. Abstracts of presentations at conferences and seminars on scientific directions of the Program in 2008. M: Firm "Word", 2008, s, - the closest analogue, was used wavelet transform Morlaix. Have found new approaches and algorithms for EEG analysis. Was also developed and used a multi-scale correlation analysis.

Using the wavelet transform was found that for patients with BP are characterized by a pronounced asymmetry and instability EEG low (Delta, theta, Alf is), and high (beta 1, beta 2) the frequency range of the EEG. These features manifest themselves already at the very early stages of the disease with unilateral tremor (stage 1 on a scale Hyun-Yar) and remain typical in the later stages.

However, in the near equivalent of missing information on the allocation of the quantitative characteristics of the wavelet spectrograms, which can act as markers of early Parkinson's disease.

The present invention is directed to BP diagnosis at an early stage (stages 1-3 on a scale Hyun-Yar) by new quantitative characteristics of the wavelet spectrograms.

Patent-pending method for early diagnosis of Parkinson's disease is recording the electroencephalogram (EEG) in the background, the calculation of the spectrograms by means of the wavelet transform with the parent function of Morlaix and the analysis of their frequency-time characteristics.

Differences method are that determine frequency bands leading rhythms of electrical activity of the brain by finding the coordinates of the minima of the frequency envelope of projections wavelet spectrograms on a plane coordinate amplitude-frequency". Further, in the above-mentioned frequency bands determine the time Tithe occurrence of peaks of the spectrograms for the values of the position of the maxima in the envelope of the projection of the wavelet spectrograms on a plane is oordinate "amplitude-time", then determine the frequency Fiand amplitude Aithe peaks of the spectrograms corresponding to the above values of the times of their occurrence in each of the identified frequency bands. For each window the discretization parameters ∆ T, ∆ F obtained by dividing a duration T and frequency band F of EEG recordings, calculate values of ∑Aithe sums of the amplitudes of the peaks of the spectrograms corresponding to the detected coordinate values of the peaks of Fiand Tion the coordinate plane "time-frequency". With increasing frequency the dominant rhythm, which is normally 9-11 Hz, and/or broadening of the frequency range of the peaks, and/or the detection of inter-hemispheric asymmetry of the electrical activity diagnosed with early stage Parkinson's disease.

The method can be characterized by the fact that before computing the values of ∑Aithe peaks of the spectrograms are sorted in a series of ascending amplitude, and when calculating values ∑Aiconsider only peaks that make up the first 93-95% of the sorted row.

The method can be characterized by the fact that the duration of EEG recordings is T=200-300 sec, and the frequency range from Fmin=1 Hz and Fmax=25 Hz, where FminFmax- the minimum and maximum frequencies, respectively.

The method can be characterized also by the fact that the parameters of the mentioned window sampling accounted for the given time ΔT=(0,05-1,00)T, as the frequency is ΔF=(0,02-0,03)Fmax.

The technical result consists in increasing the reliability of determination of the early stage of PD and reducing the complexity of the processing and analysis of EEG by eliminating the step of allocating time intervals EEG without artifacts, previously run by the neurophysiologist.

The invention is illustrated in the drawings, where:

figure 1 - block diagram of the algorithm of the proposed method;

2, 3 - wavelet spectrogram of the signal of the Central discharge C3 healthy subject and the patient on the 1-St stage of PD, respectively;

figure 4 - frequency ranges of the peaks of the wavelet spectrograms;

figure 5 - position on the time-frequency plane local maxima of the amplitude of the wavelet spectrograms;

6 - position of the local maxima of the amplitude (LMA) on the plane of time-frequency: a) for a healthy subject; b) for a patient on the 1-St stage of PD;

7 is a histogram of the sums of the amplitudes of local maxima: (a) for a healthy subject; b) for a patient on the 1-St stage of PD;

Fig - dynamic histogram of the frequencies corresponding to the maximum values of the amplitudes of the peaks in each time series window sampling, for the discharge of C3: a) a healthy subject; b) patient 1-th stage of PD;

Fig.9 - same as figure 7, but for the discharge of C4: a) a healthy subject; b) patient 1-th stage of PD.

The way Zack is udaetsya registration electroencephalogram (EEG) in the background with the recording time T=200-300 sec in the frequency range from F max=1 Hz and Fmax=25 Hz, the calculation of the wavelet spectrograms EEG using wavelet transform with the parent function of Morlaix and the analysis of frequency-time characteristics of these spectrograms. Define frequency ranges leading rhythms of electrical activity of the brain by finding the values of the frequencies of the minima envelope projections wavelet spectrograms on a plane coordinate amplitude-frequency". In these frequency ranges to determine the times of occurrence of the peaks of the spectrograms for the values of the position of the maxima in the envelope of the projection of the wavelet spectrograms on a plane coordinate amplitude-time". To determine the frequency Fiand amplitude Aithe peaks of the spectrograms corresponding to the above values of the times of their occurrence in all frequency ranges. Next plane "time-frequency" (0-T Fmin-Fmax) is divided into Windows of the discretization parameters (ΔT, ΔF). Window options, it is advisable to choose a time ΔT=(0,05-1,00)T (sec), and the frequency is ΔF=(0,02-0,03)Fmax(Hz). Then in each window is calculated ∑Ai- the sum of the amplitudes of the peaks of the spectrograms corresponding to the detected coordinate values of Fiand Tiand build a histogram of the distribution of sums ∑Aifrom the frequency.

With increasing frequency the dominant rhythm, which is normally 9-11 Hz, and/or broadening of the range is Ascot peaks, and/or the detection of inter-hemispheric asymmetry of the electrical activity diagnosed with early stage Parkinson's disease.

The block diagram of the algorithm patentable method is presented in figure 1. The method includes the following operations.

1. Creating a list of EEG recordings of patients for treatment (EEG sheet).

2. Pre-processing of the EEG signal in each channel filter high frequencies larger than 1 Hz.

3. The implementation of the complex wavelet transform with the parent function Morlaix with obtaining the wavelet spectrograms.

4. The calculation of the magnitude square of the complex wavelet coefficients.

5. The construction of the projection wavelet spectrograms and calculating the coordinates of its envelope.

6. Median filtering the envelope of the projection of the spectrogram on the plane coordinate amplitude-frequency.

7. Finding the frequency bands of the existence of peaks in the wavelet spectrograms.

8. The construction of the projection wavelet spectrograms on a plane coordinate amplitude-time" in each of the frequency bands and finding the position on the time axis of the local maxima of this projection.

9. Finding the frequency-time coordinates of the local maxima of the amplitude of all frequency ranges.

10. Remove artifacts and normalization. The artifacts are taken 3-5% of the peak with the highest amplitude of the total number of peaks. Far is our analysis of these artifacts are not taken into account. Is then normalized peak amplitude.

11. Partitioning the time-frequency plane (0-T Fmin-Fmax) on the window (ΔT, ΔF), where ΔT=(0,05-1,00)T (sec), ΔF=(0,02-0,03)Fmax(Hz).

12. The calculation of the sums of the amplitudes of the local maxima (peaks) of the wavelet spectrograms on the coordinate plane "time-frequency" in each time-frequency window.

13. Building a dynamic histogram of the distribution of sums of the amplitudes of the local maxima (peaks) of the spectrograms in the selected channel.

14. Building a dynamic histogram of the distribution of sums of the amplitudes of the local maxima (peaks) of the spectrograms in all channels.

15. Visual assessment of the deviation and the variation in frequency dynamic histograms across all channels in a patient in relation to the similar parameters of the healthy subject. The results of this assessment are diagnosed with early stage Parkinson's disease. The proposed method of EEG analysis allows you to enter and apply different measures to quantify the deviation and the variation in frequency dynamic histograms.

An example implementation. According to the method of standard EEG examinations were recorded EEG with standard arrangement of the electrodes 10×20 with the reference electrode on the ears in a calm relaxed state of the test with your eyes closed for 5 minutes. The sampling frequency of the signal e is astroenterology was 500 Hz. The method consists in the analysis of the distribution of the amplitude of the extrema of the wavelet transform in frequency and time. For EEG analysis of patients diagnosed with BP greatest interest channels C3 and C4, responsible for motor functions. The frequency range of the signal was 1-25 Hz, the signal duration is 240 seconds.

Examples of fragments of EEG recordings from the Central leads NW for a healthy test 27 years and the patient 29 years on the 1st stage of PD is shown in figure 2, 3. It is evident that the spectrogram of the EEG of the patient is characterized by a large scatter peaks in frequency. It should be noted that in 1 second there are several peaks in the wavelet spectrograms. This substantiates the expediency of selecting the minimum size of the time window partitioning the time-frequency plane. At T=240 s, ΔT=0,T=12 sec, and therefore, at frequencies of ~10 Hz in the box are more than 20-30 peaks.

Figure 4 shows the allocation of frequency bands ranges wavelet spectrograms. Identify all local minima of FMIN1FMIN2... FMINKin the frequency range from 1 to 25 Hz. The result is a three frequency range: 1 to 1.7 Hz, 1.7 to 4.4 Hz and from 4.4 Hz to 25 Hz. To find the coordinates of the extrema of the amplitude spectrum of each frequency band on the plane "frequency-time" is its projection on the plane (time-to-amplitude". ZV is camping each of the local maxima determines the position of interest to us extrema of the amplitudes of the wavelet spectrograms of time. The result of identification of extrema on the plane "time-frequency" are shown by dots and arrows.

The result is two coordinates of the extrema and the values of their amplitudes. 7, (a) presents the position of the local maxima of the amplitude (LMA) on the plane, "time-frequency" healthy subject, and figure 7,b) is the patient on the 1-St stage of PD. Crosses marked LMA assignment of C3, circles - lead C4. The sizes of crosses and circles is proportional to the amplitude of the LMA.

Then determine the frequency and amplitude of the peaks of the spectrograms corresponding to the above values of the times of their occurrence. Next, calculate the values of the sums of the amplitudes of the peaks of the spectrograms on the coordinate plane "time-frequency" in each window. The sampling window time is 0,05-0,07 from the recording duration, and frequency - 0,002-0,003 from the analyzed frequency range, such as 0.1 Hz. So, the above mentioned window sampling time for recording in the range of 200-300 seconds may be 14-18 sec, in particular 16 seconds to 240 seconds of recording, and the frequency - 0,3-0,7 Hz.

The results of determining the sums of the amplitudes of the peaks of the spectrograms on the coordinate plane "time-frequency" in these boxes shown in Fig, 9. On Fig shows the histogram corresponding to the maximum values of the amplitudes of the peaks in each time series window sampling, for the discharge of C3: a) gorovyy subject, b) patient 1-th stage of PD. Figure 9 is the same as on Fig, but for the discharge of C4: a) a healthy subject, (b) patient 1-th stage of PD.

It is seen that in a healthy person, the extrema are distributed in a narrow range of frequencies from 9 to 11 Hz. At an early stage of Parkinson's disease is an increase in the frequency of the dominant rhythm in the left hemisphere, the broadening of the frequency range, and is manifested hemispheric asymmetry electrical activity.

Thus, the dynamics of the histogram of the sums of the amplitudes of the peaks in each time series window sample from the patient relative to the similar parameters of the healthy subject should be diagnosed early stage Parkinson's disease.

1. Method for early diagnosis of Parkinson's disease, which consists in recording the electroencephalogram (EEG) in the background, the calculation of the spectrograms by means of the wavelet transform with the parent function of Morlaix and the analysis of their frequency-time characteristics, characterized in that
define frequency ranges leading rhythms of electrical activity of the brain by finding the coordinates of the minima of the frequency envelope of projections wavelet spectrograms on a plane coordinate amplitude-frequency",
further, in the above-mentioned frequency bands determine the time Tithe occurrence of peaks of the spectrograms for the values of the maxima positions on gebaude projection of the wavelet spectrograms on a plane coordinate amplitude-time",
then determine the frequency Fiand amplitude Aithe peaks of the spectrograms corresponding to the above values of the times of their occurrence in each of the identified frequency bands,
for each window the discretization parameters ∆ T, ∆ F obtained by dividing a duration T and frequency band F of EEG recordings, calculate values of ∑Aithe sums of the amplitudes of the peaks of the spectrograms corresponding to the detected coordinate values of Fiand Tion the coordinate plane "time-frequency",
and at higher frequencies the dominant rhythm, which is normally 9-11 Hz, the broadening of the frequency range of the peaks, the detection of inter-hemispheric asymmetry of the electrical activity diagnosed with early stage Parkinson's disease.

2. The method according to claim 1, wherein before calculating values of ∑Aithe peaks of the spectrograms are sorted in a series of ascending amplitude, and when calculating values ∑Aiconsider only peaks that make up the first 93-95% of the sorted row.

3. The method according to claim 1, characterized in that the duration of EEG recordings is T=200-300, and the frequency range from Fmin=1 Hz and Fmax=25 Hz, where FminFmax- the minimum and maximum frequencies, respectively.

4. The method according to claim 1, characterized in that the parameters of the mentioned window sampling are time ΔT=(0,05-1,00)T, and h is the frequency - ΔF=(0,02-0,03)Fmax.



 

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5 dwg, 1 ex, 1 tbl

FIELD: medicine, neurology.

SUBSTANCE: one should establish neurological status, bioelectric cerebral activity, availability of perinatal and ORL pathology in patients, establish their gradations and numerical values followed by calculation of prognostic coefficients F1 and F2 by the following formulas: F1=-31,42+1,49·a1-2,44·a2+0,2·а3+1,63·a4+0,62·а5+3,75·a6+1,8·а7-3,23·a8-0,8·а9-1,32·а10+3,26·а11+8,92·a12-2,0·a13+3,88·а14+1,79·a15+0,83·a16-2,78·a17; F2=-27,58+1,43·a1+3,31·а2+0,08·а3+3,05·а4-0,27·а5+2,69·а6+3,11·а7-6,47·a8-6,55·a9+1,99·а10+5,25·а11+7,07·a12-0,47·a13+0,13·a14+4,04·a15-1,0·a16-1,14·а17, correspondingly, where a1 - patient's age, a2 - studying either at the hospital or polyclinic, a3 - duration of stationary treatment (in days), a4 - unconscious period, a5 - terms of hospitalization since the moment of light close craniocerebral trauma, a6 - smoking, a7 - alcohol misuse, a8 - arterial hypertension, a9 - amnesia, a10 - close craniocerebral trauma in anamnesis, a11 - psychoemotional tension, a12 - meteolability, a13 - cervical osteochondrosis, a14 - ORL pathology, a15 - availability of perinatal trauma in anamnesis with pronounced hypertension-hydrocephalic syndrome, a16 - availability of paroxysmal activity, a17 - availability and manifestation value of dysfunction of diencephalic structures. At F1 ≥ F2 on should predict the development of remote aftereffects in young people due to evaluating premorbid background of a patients at the moment of trauma.

EFFECT: higher reliability of prediction.

2 ex, 1 tbl

FIELD: medicine; medical engineering.

SUBSTANCE: method involves doing multi-channel recording of electroencephalogram and carrying out functional tests. Recording and storing rheoencephalograms is carried out additionally with multi-channel recording of electroencephalogram synchronously and in real time mode in carotid and vertebral arteries. Electroencephalograms and rheoencephalograms are visualized in single window with single time axis. Functional brain state is evaluated from synchronous changes of electroencephalograms, rheoencephalograms and electrocardiograms in response to functional test. The device has electrode unit 1 for recording bioelectric brain activity signals, electrode unit 2 for recording electric cardiac activity signals, current and potential electrode unit 3 for recording rheosignals, leads commutator 4, current rheosignal oscillator 5, synchronous rheosignal detector 6, multi-channel bioelectric brain activity signals amplifier 7, electrophysiological signal amplifier 8, demultiplexer 9, multi-channel rheosignal amplifier 10, multi-channel analog-to-digital converter 11, micro-computer 12 having galvanically isolated input/output port and personal computer 13 of standard configuration.

EFFECT: enhanced effectiveness of differential diagnosis-making.

11 cl, 6 dwg

FIELD: medicine; medical engineering.

SUBSTANCE: method involves recording multichannel electroencephalogram, electrocardiogram record and carrying out functional test and computer analysis of electrophysiological signals synchronously with multichannel record of electroencephalogram and electrocardiogram in real time mode. Superslow brain activity is recorded, carotid and spinal artery pools rheoelectroencephalogram is recorded and photopletysmogram of fingers and/or toes is built and subelectrode resistance of electrodes for recording bioelectrical cerebral activity is measured. Physiological values of bioelectrical cerebral activity are calculated and visualized in integrated cardiac cycle time scale as absolute and relative values of alpha-activity, pathological slow wave activity in delta and theta wave bandwidth. Cerebral metabolism activity dynamics level values are calculated and visualized at constant potential level. Heart beat rate is determined from electrocardiogram, pulsating blood-filling of cerebral blood vessels are determined from rheological indices data. Peripheral blood vessel resistance level, peripheral blood vessel tonus are determined as peripheral photoplethysmogram pulsation amplitude, large blood vessel tonus is determined from pulse wave propagation time data beginning from Q-tooth signal of electrocardiogram to the beginning of systolic wave of peripheral photoplethysmogram. Postcapillary venular blood vessels tonus is determined from constant photoplethysmogram component. Functional brain state is determined from dynamic changes of physiological values before during and after the functional test. Device for evaluating functional brain state has in series connected multichannel analog-to-digital converter, microcomputer having galvanically isolated input/output ports and PC of standard configuration and electrode unit for reading bioelectric cerebral activity signals connected to multichannel bioelectric cerebral activity signals amplifier. Current and potential electrode unit for recording rheosignals, multichannel rheosignals amplifier, current rheosignals generator and synchronous rheosignals detector are available. The device additionally has two-frequency high precision current generator, master input of which is connected to microcomputer. The first output group is connected to working electrodes and the second one is connected to reference electrodes of electrode unit for reading bioelectrical cerebral activity signals. Lead switch is available with its first input group being connected to potential electrodes of current and potential electrodes unit for recording rheosignals. The second group of inputs is connected to outputs of current rheosignals oscillator. The first group of outputs is connected to current electrodes of current and potential electrodes unit for recording rheosignals. The second group of outputs is connected to inputs of synchronous detector of rheosignals. Demultiplexer input is connected to output of synchronous detector of rheosignals and its outputs are connected to multichannel rheosignals amplifier inputs. Outputs of multichannel bioelectrical cerebral activity signals amplifier, multichannel rheosignals amplifier and electrophysiological signal amplifier are connected to corresponding inputs of multichannel analog-to-digital converter. Microcomputer outputs are connected to control input of lead switch, control input of multichannel demultiplexer, control input of multichannel analog-to-digital converter and synchronization inputs of current rheosignals oscillator and synchronous detector of rheosignals. To measure subelectrode resistance, a signal from narrow bandwidth current generator of frequency f1 exceeding the upper frequency fup of signals under recording is supplied. A signal from narrow bandwidth current generator of frequency f2≠ f1>fup is supplied to reference electrode. Voltages are selected and measured at output of each amplifier with frequencies of f1, f2 - Uf1 and Uf2 using narrow bandwidth filtering. Subelectrode resistance of each working electrode is determined from formula Zj=Ujf1 :(Jf1xKj), where Zj is the subelectrode resistance of j-th electrode, Ujf1 is the voltage at output from j-th amplifier with frequency of f1, Kj is the amplification coefficient of the j-th amplifier. Subelectrode resistance of reference electrode is determined from formula ZA=Ujf2 :(Jf2xKj), where ZA is the subelectrode resistance of reference electrode, Ujf2 is the voltage at output from j-th amplifier with frequency of f2, Jf2 is the voltage of narrow bandwidth current oscillator with frequency of f2.

EFFECT: wide range of functional applications.

15 cl, 10 dwg

FIELD: medicine, psychiatry.

SUBSTANCE: one should conduct EEG-testing to detect total value of the indices of spectral power or percentage spectral power of delta- and teta-rhythms due to spectrometric technique in frontal, parietal, central and temporal areas both before and during emotional-negative loading when visual emotionally negative stimuli are presented followed by their imaginary reproduction. In case of higher indices to visual stimuli being above 15% against the background one should diagnose epilepsy. The method enables to increase the number of diagnostic means, increase accuracy and objectivity in predicting epilepsy with polymorphic paroxysms at dissociation of clinical and EEG-values.

EFFECT: higher efficiency of diagnostics.

1 ex, 1 tbl

FIELD: medicine, neurophysiology.

SUBSTANCE: one should carry out EEG survey to detect spectrometrically the index of full range if alpha-rhythm both before and after therapy. Moreover, power index of full range of alpha-rhythm and the index of 9-10 Hz-strip's spectral power should be detected in occipital cerebral areas. One should calculate the value of the ratio of the index of 9-10 Hz-strip's spectral power to the index of full range of alpha-rhythm and at the increase of this value by 20% against the background it is possible to evaluate positive result of therapy. The method increases the number of diagnostic means applied in evaluating therapeutic efficiency in the field of neurophysiology.

EFFECT: higher efficiency of evaluation.

1 ex

FIELD: medicine, neurology.

SUBSTANCE: method involves carrying out the standard vascular and nootropic therapy. Diazepam is administrated under EEG control with the infusion rate that is calculated by the following formula: y = 0.0015x - 0.025 wherein y is the rate of diazepam administration, mg/h; x is an average EEG amplitude, mcV. Method provides enhancing the effectiveness of treatment of patients. Invention can be used for treatment of patients in critical severe period of ischemic insult.

EFFECT: enhanced effectiveness of treatment.

2 tbl, 1 dwg, 1 ex

FIELD: medicine.

SUBSTANCE: method involves selecting signals showing patient consciousness level and following evoked auditory potentials as responses to repeating acoustic stimuli, applying autoregression model with exogenous input signal and calculating AAI index showing anesthesia depth next to it.

EFFECT: quick tracing of unconscious to conscious state and vice versa; high accuracy of measurements.

9 cl, 3 dwg

FIELD: medicine; experimental and medicinal physiology.

SUBSTANCE: device can be used for controlling changes in functional condition of central nervous system. Device has receiving electrodes, unit for reading electroencephalograms out, analog-to-digital converter and inductor. Low noise amplifier, narrow band filter linear array which can be program-tuned, sample and store unit, online memory, microcontroller provided with controlled permanent storage, liquid-crystal indicator provided with external control unit are introduced into device additionally. Receiving electrodes are fastened to top part of patient's head. Outputs of electrodes are connected with narrow band filters linear array through electroencephalograph. Output of linear array is connected with input of input unit which has output connected with input of analog-to-digital converter. First bus of analog-to-digital converter is connected with online storage. Recording/reading bus of microcontroller is connected with control input of input unit and its starting bus is connected with address input of online storage. Third control bus is connected with narrow band filters linear array. Second control bus is connected with liquid-crystal indicator. Output bus is connected with inductor. External control (keyboard) of first control bus is connected with microcontroller. Output of online storage is connected with data input of microcontroller through 12-digit second data bus. Efficiency of influence is improved due to getting specific directed influence being based onto general technological transparency of processing of human brain's signals and strictly specific influence based on the condition of better stimulation.

EFFECT: increased efficiency.

3 cl, 1 dwg, 1 tbl

FIELD: medicine, neurology, professional pathology.

SUBSTANCE: one should carry out either biochemical blood testing and electroencephalography or SMIL test, or ultrasound dopplerography of the main cranial arteries, rheoencephalography (REG) to detect the volume of cerebral circulation and hypercapnic loading and their digital values. Then it is necessary to calculate diagnostic coefficients F by the following formulas: Fb/e=6.3-0.16·a1+0.12·a2-1·a3+0.2·a4, or FSMIL=9.6+0.16·a5-0.11·a6-0.14·a7+0.07·a8, or Fhem=48.6-0.04·a9+0.15·a10+13.7·a11-0.02·a12+24.7·a13, where Fb/e -diagnostic coefficient for biochemical blood testings and EEG; FSMIL - diagnostic coefficient for SMIL test; Fhem - diagnostic coefficient for hemodynamic testing; 6.3; 9.6 and 48.6 - constants; a1 - the level of vitamin C in blood; a2 - δ-index by EEG; a3 - atherogenicity index; a4 - the level of α-proteides in blood; a5 - scale 3 value by SMIL; a6 - scale K value by SMIL; a7 - scale 5 value by SMIL; a8 - scale 7 value by SMIL; a9 - the level of volumetric cerebral circulation; a10 - the value of linear circulatory rate along total carotid artery, a11 - the value of resistive index along total carotid artery; a12 - the value for the tonicity of cerebral vessels at carrying out hypercapnic sampling by REG; a13 - the value for the intensity of cerebral circulation in frontal-mastoid deviation by REG. At F value being above the constant one should diagnose toxic encephalopathy, at F value being below the constant - discirculatory encephalopathy due to applying informative values.

EFFECT: higher accuracy of diagnostics.

6 ex, 1 tbl

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