Evoked auditory potential monitoring

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

 

Background of the invention

This invention relates to a method for extracting signals that are indicative of the level of consciousness of the patient, including the monitoring of auditory evoked potentials (SVP)obtained from the patient in response to repetitive acoustic stimuli, highlighting the SVP in several, preferably more than 10 and less than 50, repetitions of the sound stimuli, using autoregressive modeling with exogenous input (ARX) and the index (AAI), showing the depth of anesthesia.

Assessment of depth of anaesthesia is usually based on clinical observations of physiological parameters such as blood pressure, heart rate, pupil size, etc. the Use of neuromuscular blocking agents used during General anesthesia leads to a distortion of clinical signs that usually indicate the presence of consciousness. The number of known cases of patients said that they were fully conscious during surgery, and in the worst case experienced pain and cardiac arrhythmias. Therefore, there is a need for method and device for estimating the depth of anesthesia. Already published a number of research results, where for the assessment of level of consciousness during General anesthesia was used auditory evoked potentials (SVP). The signal SVP, I had laudisa a subcomponent of the EEG signal, called sound stimuli, recorded using sklepowych electrodes, amplified and analyzed on the computer. SVP is a weak electrical signal, superimposed on the noise continuously withdrawn EEG, and therefore the selection signal SVP necessary improved signal processing. Signals SVP traditionally distinguished by averaging up to 1000 repetitions reactions to signals of excitation. This process is very long, its implementation takes several minutes, usually 2-3 minutes is excessive, if the anesthesiologist must use SVP signal as a prediction parameter adequate doses of anaesthetic substance.

From international patent application WO 98/10701 (PCT/GB97/02435) known control system and method of calculating the index of depth of anesthesia. The method of calculating the index, showing the depth of anesthesia based on the control SVP generated by the patient, and receiving the signal corresponding to the irregularities of the monitored signal SVP. The original signal SVP is divided into a sequence of segments or frames of a given length, each segment of the signal synchronized with the repetitive acoustic stimuli. Many of the n pieces of signal sequentially record and average to obtain the time-averaged period of the signal. Index aneste the AI calculated for the time average of the segment signal. Whenever taping a new series of segments of the signal, determine a new time-average period of the signal of the last n segments and for this time-averaged segment of the signal, calculating the index of anesthesia. Thus, the index is constantly being specified.

It was observed that when the patient loses consciousness, the amplitude of most of the peaks SVP reduced, and the time delay generally increases. These changes occur almost simultaneously and equally to all patients. Therefore, the appropriate index is the index, which reflects these changes.

To calculate the index was developed an empirical algorithm based on the summation of the square roots of the difference between every two consecutive points in the moving segment of the signal averaged over time. This index of auditory evoked potential is determined by the following equation:

where x1-x256are the sample points of the time-averaged frame, and k is a scale factor.

The SVP index is calculated for each of the filtered time-averaged segment of the signal, it is possible to construct the dependence of the index from time to display on a display screen or other display method.

When the patient is awake, the index is usually located within 80 to 90, while under anesthesia, it is usually in the range from 35 to 40.

The authors E.W.Jensen, P.Lindholm and S.W.Henneberg in the article "Autoregressive Modeling with Exogenous Input of Middle-Latency Auditory-Evoked Potentials to Measure Rapid Changes in Depth of Anesthesia", published in "Methods of Information in Medicine, 1996; 35:256-260, describe the method of system identification, autoregressive model with exogenous input (ARX) to obtain an estimate of the SVP in the transition from one segment of signal to another. The method has been clinically tested in 10 patients undergoing anesthesia Alfentanil and propofol. Measured time interval between the introduction of propofol and when the amplitude of the components of the Na-Pa was reduced to 25% of the initial amplitude. These measurements showed that during the observation of the transition from the conscious state to the unconscious, with the introduction of propofol (p<0,05), ARX-evaluation received much faster than moving time-averaged estimates of the SVP.

The aim of the invention is to improve this method of measurement so that would be considerably faster to ensure a safer outcome, resulting in reduced risk of treatment of a patient with incomplete anesthesia (e.g., during surgery).

When using the ARX modeling delay time reduced to 6 seconds.

The second objective of the invention is that is, to make the procedure of anesthesia is more effective (the effective time) and to reduce the workload of staff.

The third objective of the invention is to create a method for continuous monitoring of the level of consciousness.

These goals are provided when using the method described in claim 1 of the claims.

Additional useful features are provided by the characteristics disclosed in the dependent claims.

Using the proposed method provides the possibility of allocating a hovercraft with a small number of repetitions, usually only when 15 repetitions, which reduces the delay time of approximately 6 seconds. As the SVP is a very complex signal containing several peaks and troughs, it is desirable to Express the SVP by one number - ARX index, easily interpretable, but bearing the same information as SVP. This is possible using the proposed method. Usually ARX-index greater than 60, when the patient is awake, and decreases when the patient is under anesthesia. When the index falls below 28, usually loss of consciousness.

A device that implements the proposed method can work when there are only three surface electrodes, for example three proprietary surface electrodes, the result is:/p>

- Full update SVP within a few seconds.

- Significantly faster calculation of the index SVP compared with the method of the moving average.

- Significantly faster tracking transition to unconscious state and back.

- Consistent, accurate readings.

- The ability to optimally display the results for use in the operating room.

- Touch screen, convenient to operate and clean.

- Fully graphical display.

Using the proposed method it is possible to monitor the level of consciousness during General anesthesia regardless of the biological differences between patients in terms of their ability to transfer an anesthetic substance and sensitivity to them.

According to the invention, the calculations can be done on the computer, as described in item 8.

Brief description of drawings

Below the invention is described in more detail with reference to the drawings, in which:

figure 1 schematically depicts a variant of the system used for the implementation of the proposed method for selecting SVP (auditory evoked potentials) and to calculate the index,

figure 2 depicts a block diagram of the procedures necessary for granting SVP (auditory evoked potentials) and calculation of the index according to a variant embodiment of the invention,

and figure 3 depicts the POC scheme, illustrating generalized ARX-model (autoregressive with exogenous input), which is used according to the invention.

Detailed description the preferred option of carrying out the invention

Figure 1 schematically shows a system 1 for allocation SVP (auditory evoked potentials) and to calculate the index, which is a sign of the depth of anaesthesia of the patient.

Patient 2 is feeling the effects of repetitive sound signals, which are transmitted to him through headphones, head phone or similar device. These audio signals in the form of "clicking" noise signals of short duration (approximately 1-2 MS) served in both ears of the patient and cause the characteristic potentials (known as SVP or auditory evoked potentials in electroencephalography (EEG) of the patient's response. Headphones and equipment for sound signals, such as signal generator, not shown in figure 1.

The electrodes providing EEG signals (EEG signals), attached to the head of the patient 2. You can use the electrodes, for example scalpsie, in the amount of three or more. In the case of three electrodes, they are attached to the patient 2 in the following provisions: a positive electrode in the middle of the forehead, the reference electrode on the left side of the forehead and the negative electrode on the ADI ear, preferably in the region of the mastoid process of the temporal bone. You can use and other terms with similar results.

Electrodes attached to the leading from the patient to the cable that leads to the amplifier 3. The amplifier 3 is a measuring amplifier with a large common-mode rejection signal (KOSS). Amplified analog signal is converted into a digital signal using an analog-to-digital Converter (not shown) before you can enter the digital block 4 data processing. This digital unit can be in the form of a personal computer (PC), in particular, single-Board personal computer. The digital signal is analyzed and stored in the computer 4, which is programmed in accordance with algorithms to highlight SVP (auditory evoked potentials) and in accordance with algorithms to calculate the index. These cues SVP and calculation of the index described below with additional explanations.

SVP and the index displayed on the display 6, and using the input device 5 can send commands to the computer 4. This device 5 input may be a touch pad or touch screen, which can be combined with the display 6, as a result, you can give commands to a computer by touching the display.

Selected calculated index value, and SVP-Ignacy can be transferred to external devices (not shown) through a connector 7, which can be a connector for RS - 232. The external device may, for example, be a device for the administration of medications to the patient. Thus, the system can be used for controlling the amount of medication injected to the patient depending on the depth of anaesthesia. Other examples of external devices can be devices for examination or treatment of the patient, for example, the apparatus for monitoring respiratory system.

Procedure or function that is included in the method and the system, called A-line-monitor™ (AAI stands for A-line ARX Index), shown in the diagram in figure 2 in accordance with one embodiments of the invention.

The data corresponding to the signals from the patient is introduced into the processing unit 4 in the form of a sequence of unprocessed segments 11 of the signal. In the embodiment of the invention these segments 11 of the signal obtained when the sampling frequency of 900 Hz, and each segment of the signal consists of 70 samples, which gives the length of the segment 80 MS. Sound stimulus in the form of click used to receive signals, had a duration of 2 MS and an intensity exceeding 70 dB normal hearing.

Each segment of the first signal is processed identification algorithm 12 to decide whether to ignore this segment for further processing. There are two types of identification, the traditional algorithms.

First: if on a segment of the signal artifacts are present, the amplitude of the raw signal as a whole will greatly exceed the amplitude of the normal segments of the signal. The amplitude is expressed as a range of values permitted by the analog-digital Converter is, for example, 0-65534. 95% of the range of values of the EEG are 15000-55000 therefore, if the count is below 5000 or above 60000, the number of subsequent samples, for example 400 samples are discarded.

Second: in the time after saturation of the amplifier 3, the signal is shifted within normal limits, i.e. 15000-55000. However, in this period of time may be determined by clicking the artifact arising out of the sound stimulus. Therefore, is an algorithm that detects caused by clicking the artifact. Calculated the average difference of 10 times before it clicks. If the difference between the first reference on the interval signal n and the last reference on the interval signal n-1 is greater than, for example, 5 times than the previous calculated average difference, the number of subsequent samples, for example, the following 800 samples are discarded.

To improve the signal-to-noise (s/n)in the system before performing moving averaging time (SUV) and autoregressive model with exogenous input (ARX), included band-pass filter 13. The filter to is that in the variant of the invention has a bandwidth of from 16 to 150 Hz, can be a Butterworth filter of the fifth order, implemented in digital form.

ARX-model (autoregressive model with exogenous input), which facilitates quick selection SVP described with reference to figure 3, showing generalized ARX-model.

ARX-model is obtained by adding exogenous input to the autoregressive model (AR) to analyze digital signals. Therefore, ARX-model is defined by the following equation:

y(t)+a1y(t-1)+...+any(t-n)=b1u(t)+...+bmu(t-m+1)+e

where n is the order of the inverse of the coefficients (a1...an), a m - order direct coefficients (b1...bm). The output signal is y, u is the exogenous input, e is the error.

Figure 3 shows the AR - part 22, which is excited by white noise and is determined by the averaged EEG activity. Pre-averaging can be performed using the 15 segments of the signal.

Exogenous input u received in block 23, is a SVP, obtained by averaging the number of segments of the signal, for example, the last 256 segments. The output signal of the ARX model is an averaged number of the last accumulated signal segments (mainly 15), consisting of the averaged background EEG activity and SVP. When the coefficients of the model are defined, ARX-SVP p is obtain by IIR filtering exogenous input u.

When the model order is equal to, for example, five, ARX - equations have the form

Members containing the error e are omitted, and the equations are written in matrix form:

The above system of equations represents the set of linear equations. Therefore, Gaussian elimination or LU decomposition will not give a satisfactory result. A very powerful mechanism for solving overdetermined system is singular SVD-decomposition, which solves the problem in the sense of minimum mean square error (MCO). The singularity of the matrix means that the matrix does not have full rank, often occurs when the matrix consists of data, a priori exactly unknown. The singularity may occur if the equations there is an uncertainty. This is a paradox because on the one hand, the system is overridden (more equations than unknowns), and on the other hand it is uncertain, because too many equations are linear (or almost linear) combinations of each other. Singular SVD-decomposition not only diagnoses and solves the problem of singularity and gives meaningful numerical results, but also gives the solution algorithm minimum mean square error (MCO).

Order the OK model is determined by considering the error functions. Function errors expressed by the following equation:

where e(i)=y(i)-y^(i), a N - number of samples in one segment of the signal. Variables y(i) and y^(i) represent, respectively, the actual values and the values obtained in the prediction of pre-averaged segments of the signal, for example to 15 segments. Identification is checked by the test of Anderson on "whiteness" of the error E. the prediction. If the forecast error is "white" with a confidence level of 95%, then the identification is accepted. Optimal values of n and m are selected by minimizing the function end of the forecast errors (CPC), a certain Akaike (Akaike H. "Statistical predictor identification", Ann. Inst. Statist. Math., 1970; No. 22, pp.203-217):

COP=L·(N+n+1)/(N-n-1),

where n is the total number of coefficients of the ARX model.

COP reflects the need to minimize the error functions and the need to limit the number of parameters of the ARX model.

Order ARX model should ideally be calculated for each segment of the signal. It is a process that requires a lot of time, therefore, to satisfy the requirement of fast processing time, was chosen as the average model order equal to 5 for both reverse and direct factors. Obviously, you can choose other suitable number, preferably a number less than 10.

As shown in figure 2, with unit 15 output the second signal, which is the SVP received a sliding averaging time (SUV) calculation result by 256 segments of the signal with the same weight for each segment, is supplied to the display 6 (Fig 1), where it can be represented as SITCOV display 16. In addition, the output signal from the bandpass filter 13, i.e. the EEG signal is supplied to the display 6 (Fig 1), where it can be reproduced in the form of EEG - image 14.

The output signal from block 15 also serves on the ARX-model 18 together with the output signal from the output of block 17, which carries SUV to a smaller number of segments of the signal, preferably 15 segments. The output signal from the ARX-model 18, which, as explained above, is a SVP, highlighted by ARX, and which is designated as SVPARXalso served on the display unit 6 (figure 1), where it can be reproduced as SVPARXimage 19.

To determine the level of anesthesia is desirable SVPARXconvert to index. To do this, the output signal from the ARX model is applied to the unit 20 to calculate the index, the purpose of which is explained below.

The index, calculated by the block 20, is called the A-line ARX index (AAI). It can be displayed on the display unit 6 (figure 1), where it can be reproduced in the form AAI-(SVPARX)-index image 21. SVP consists of several peaks. Usually it is assumed that the amplitude peaks with C is the support of 10-100 MS, the corresponding Signaltechnik SVP (LSWP), decreases when the patient is under anesthesia, and at the same time, the delay of the peaks increases. The proposed variant of the index retains these two principles, in order not to lose information. In addition, to obtain a reliable index should be provided with the following conditions:

1. Justice for the greatest possible number of patients, regardless of the type of surgery and anesthetic substances.

2. Need a good dynamic properties during the transition from wakefulness to sleep, to distinguish changes of consciousness, caused by the noise.

SVP-index is calculated in the window SVP, which can exclude the beginning and end of the window. Preferably, if the window is the window SVP duration 20-80 MS, and changes in latency and amplitude in SVPs have the same weighting factor. The beginning of the window, corresponding to 20 MS, selected so as not to include CITSM (auditory evoked potentials, brain stem) and artifacts from the ear muscles, and the end of the window corresponding to 80 MS, selected so as not to include DNSBL (SVP higher latency). This is done because SITS and DLCBL poorly correlated with depth of anesthesia. The proposed version of AMS-index (AAI) in preliminary tests showed good discernment of patients in the conscious state and PA is antov, subjected to anesthesia. SVP-index (AAI) is determined to reflect the degree of carotid condition during anesthesia.

First, define y as:

where xi- timing of the segment of the signal, k1is a constant that is preferably more 0,0100 and less 0,0200, and which, in particular, can be chosen to be equal 0,0165, and where k2and k3are, respectively, the start and end times for summing selected so as not to include the start and end of the window SVP.

If the window SVP consists of 70 samples, k2may preferably be a 17, a k3can be 69.

AAI - index is defined as:

where k4, k5, k6, k7and p are constants.

Preferably, if 0,2500<k4<0,3000, and most preferably k4=0,2786.

Preferably, if 43,0000<k5<43,5000, and most preferably k5=43,2857.

Preferably, if 9,1000<k6<9,8000, and most preferably k6=9,3769.

Preferably, if a 0.25<k7<a 0.30, and most preferably k7=0,28.

Preferably, if 4≤R≤6, and most preferably R=5.

In the most preferred embodiment of the invention ARX-index is defined as:

The index is p is adelah from 0 to 99, moreover, a larger value indicates a higher level of consciousness.

The proposed device can essentially be implemented on a computer 4, for example, on a single-Board computer with a clock speed of 486 MHz, equipped with appropriate software (for example, using the programming language Borland Pascal), so that the algorithms or their parts (explained in the description and set forth in the claims) provide the necessary technical result, i.e. the calculation explained above AAI-index and its display. The proposed program can be stored on any known computer readable media, as a result it can easily be installed on the computer 4.

1. The method of selection signals, which are indicative of the level of consciousness of the patient, including the monitoring of auditory evoked potentials (SVP)obtained from the patient in response to repetitive acoustic stimuli, the splitting of the original SVP signal into a sequence of segments, the allocation of SVP signals in several, preferably more than 10 and less than 50, repetitions of the sound stimuli, using autoregressive modeling with exogenous input (ARX) and the AMS index AAI, reflecting the depth of anesthesia, characterized in that the index is defined as:

where k4, k5, k6, k7and R are constants;

xithe samples cut SVP signal;

k1is a constant that is preferably more 0,0100 and less 0,0200 and which, in particular, can be chosen to be equal 0,0165; k2and k3respectively the start and end times of summation, selected so as not to include the start and end of the SVP-window.

2. The method according to claim 1, characterized in that it further includes filtering SVP signals, preferably by means of a bandpass filter with bandwidth from 16 to 150 Hz, and preferably through a filter, made in the form of digital Butterworth filter of the fifth order.

3. The method according to claims 1 and 2, characterized in that in the calculation of the index using only the signals in the SVP-window with a duration of about 100 MS, preferably in a window of duration of 20-80 MS, not including the start and end of the segment signal.

4. The method according to claim 1, characterized in that the maximum number of samples in the SVP-box is 80, the preferred value of k2is 17, and the preferred value of k3equal to 69.

5. The method according to claim 4, characterized in that the specified index is defined as:

6. The method according to claim 1, characterized in that the k4, k5, k6, k7and p is determined as follows:

PR is doctitle, if 0,2500<k4<0,3000, and most preferably k4=0,2786;

preferably, if 43,0000<k5<43,5000, and most preferably k5=43,2857;

preferably, if 9,1000<k6<9,8000, and most preferably k6=9,3769;

preferably, if a 0.25<k7<a 0.30, and most preferably k7=0,28;

preferably, if 4≤p≤6 and most preferably p=5.

7. The method according to claim 1, characterized in that it includes obtaining segments of the signal at the sampling rate of about 700-1000 Hz, preferably about 900 Hz, and SVP-window with a maximum duration of 100 MS, and when calculating the index using only the signals within a window, preferably a window duration of 20-80 MS excluding start and end of the segment signal.

8. The method according to claim 1, characterized in that the calculated index AAI display on the display or issued in the form of a signal.

9. Computer readable medium, the medium containing a computer program designed to perform calculations according to any one of claims 1 to 7 when said program is run on the computer.



 

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2 cl

FIELD: medicine.

SUBSTANCE: method involves carrying out information treatment in the indirect psychotherapy framework. A session-lesson begins with sociogenesis, motives, origin and development mechanisms of drug addiction being explained. The order of stage-by-stage disease symptom manifestations is explained to the smallest details. Using the explained data, the students are convinced in fatal danger of drug addiction in terms of formal logic. Next to it, semantic content of premises is represented as video film that is used for introducing drug information into subconscious level by means of audiovisual means. Suggestive treatment is periodically carried out. It is directed to prohibition of drug consumption. After the video film being ended, acupressure lessons restricted to three biologically active points of Yin-tang, Tou-wei, Feng-chi are taught to the students. Prohibition of drug consumption is suggested. The students are assured that the self-massage treatment like this would give them confidence of their strength in complex life situations.

EFFECT: enhanced effectiveness of prophylaxis.

2 cl, 1 tbl

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, 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, 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; 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; 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, 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, neurology, psychopathology, neurosurgery, neurophysiology, experimental neurobiology.

SUBSTANCE: one should simultaneously register electroencephalogram (EEG) to detect the level of constant potential (LCP). At LCP negativization and increased EEG power one should detect depolarizational activation of neurons and enhanced metabolism. At LCP negativization and decreased EEG power - depolarized inhibition of neurons and metabolism suppression. At LCP positivation and increased EEG power - either repolarized or hyperpolarized activation of neurons and enhanced metabolism. At LCP positivation and decreased EEG power - hyperpolarized suppression of neurons and decreased metabolism of nervous tissue. The method enables to correctly detect therapeutic tactics due to simultaneous LCP and EEG registration that enables to differentiate transition from one functional and metabolic state into another.

EFFECT: higher accuracy of diagnostics.

5 dwg, 1 ex, 1 tbl

The invention relates to medicine, in particular to neurosurgery, and can be used for the diagnosis of brain edema is at its focal lesions

The invention relates to medicine, namely to neurology and psychiatry

FIELD: medicine, cardiology.

SUBSTANCE: in children and teenagers one should carry out variation pulsometry to register the values obtained in the mode of patient's free activity during the day. One should evaluate variability values of cardiac rhythm - rMSSD pNN50 and variability of arterial pressure. In case of decreased rMSSD, pNN50 and daily index of arterial pressure and increased coefficient of arterial pressure variation one should diagnose pre-clinical stage of diabetic autonomic cardiovascular neuropathy despite disease duration.

EFFECT: higher efficiency of pre-clinical diagnostics.

2 ex, 2 tbl

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