Monitoring of ciliary arrhythmia

FIELD: medicine.

SUBSTANCE: invention relates to medical equipment, in particular, to systems of ECG monitoring, which trace indications of ciliary arrhythmia (CA) in real time. System of ciliary arrhythmia monitoring contains source of electrocardiogram data, extractor of P-wave signs, extractor of interval R-R signs, CA classifier, reacting to P-wave sign and interval R-R sign, which classifies cardiac rhythm as that with CA or without CA, display, reacting to CA classifier for displaying CA classification, and user's input for regulation of balance sensitivity/specificity of identification of rhythm with CA, with user's input additionally containing selection of type of patient population for automatic adjustment of nominal working parameters of identification of rhythm with CA for selected type of patient population.

EFFECT: invention will make it possible to simplify adjustment of nominal working parameters Of CA monitoring system for selected type of patient population.

14 cl, 11 dwg

 

This invention relates to systems for monitoring electrocardiogram (ECG) and, in particular, to monitoring systems ECG monitoring indication atrial fibrillation in real time.

Atrial fibrillation (AF) is an arrhythmia in which the Atria is not compressed in accordance with the rest of the heart for the effective discharge of blood. Atrium shaking or fibrillary wrong at a very high frequency, and therefore the ventricles are also reduced with the wrong frequency. Atrial "kick" with every heartbeat is lost, and the discharge efficiency of the heart is reduced. Because the Atria do not make significant reductions in atrial fibrillation, blood can stagnate in the atrium, and can form blood clots. Although atrial fibrillation by itself does not usually cause death, a large percentage of all strokes caused by blood clots formed during MA. MA occurs in approximately 0.4%-1,0% of the total population and annually affects more than 2 million people in the United States. The prevalence of AF increases with age, and up to 10% of the population aged over 80 years received a diagnosis of MA and at some point stroke. Stroke is the third largest cause of death in North America, second only to heart attacks and cancer. In addition, patients often exhibit symptoms, nab is emer, sudden episodes of palpitations and shortness of breath. Thus, atrial fibrillation is a serious condition that requires treatment.

Developed systems and algorithms to identify symptoms MA in the electrocardiograms of patients. Two such techniques are described, for example, in U.S. patents 6490479 and 6937887. However, the existing algorithms for the detection of AF is sometimes unable to distinguish AF from some other types of arrhythmia. This misclassification other irregular rhythms as MA leads to false alarms. Because the clinic staff must respond to each alarm and to prove it, it is desirable to reduce the number of false alarms as possible. Too many false alarms means more work for staff, which may lower the sensitivity of the detector to reduce the number of false alarms, which automatically leads to a decrease in its sensitivity to these alarms. For example, the monitoring system MA should include a method of distinguishing between the MA, which has an irregularly irregular rhythm and arrhythmia with a regularly irregular rhythm, for example, the atrial bigeminy that the known methods usually mistakenly recognize as MA.

Known algorithms for the detection of AF, in General, have only two solutions, in the sense that they classify the rhythm as with MA, or without MA. It is desirable not only to identify MA, but also to provide a measure of confidence that the identified rhythm is MA. This measure of trust would help hospital staff to assess the seriousness of the alarm MA.

Known algorithms to signal only the beginning of the episode, MA. Sometimes it is important to signal the end of the MA. For example, medical personnel interested in knowing when MA for patients connected to the cardioverter or taking medication. Thus, it is desirable to monitor MA signaled medical personnel not only about the beginning of the episode MA, but about the end of the episode MA.

There is currently no clear consensus on the optimal endpoints for determining response to therapy MA. Therefore, the endpoints are required to respond adequately represent positive or negative therapeutic effect. Such endpoints should be able to prescribe therapeutic process in order to fully cure MA, for example, procedures excision of the left atrium, as well as methods of treatment aimed at reducing symptomatic episodes of AF and improved quality of life, for example, farmakol the policy treatment. Monitoring system that reports real-time load MA would help medical staff to evaluate the need and effectiveness of different treatment methods.

In addition, many of the techniques of monitoring MA do not allow to consider the type of monitoring MA, what is needed for a particular stage of the disease. For example, patients with chronic or permanent AF, patients, recently attached to the aid, and patients with paroxysmal AF may require different monitoring. Depending on whether you want constant information about the change of rhythm or only the long-term trend, the alerter monitor can be configured in accordance with the needs of the individual patient.

According to the principles of the present invention, the monitoring system atrial fibrillation reveals MA and reports to the load current in real-time. In the illustrative monitoring system described below, the classifier heartbeat selects those heart reduction from the input ECG signal, which meet the criteria used for the detection of AF. Selected heart contractions are combined to generate a sample of a reduction in P-wave. Analysis of the model allows us to identify the characteristic P-wave, and the sign of R-R interval is also identified. The characteristic vector is calculated using, for less the th least one characteristic P-wave and the characteristic interval R-R Classifier, which is a set of rules used for classification of the vector of feature or as having MA or without MA. Classification MA checked using the second set of rules for the correction of incorrect classification. The invention, in an optional order, can provide a measure of confidence of the detection of AF. The system can calculate and report in real time the load MA at any time you set the frequency and duration of episodes of MA. The system can adapt its monitoring MA to patient, taking into account patient characteristics that determine monitoring requirements.

In the drawings:

figure 1 - block diagram of the main components of the monitoring system ECG.

figure 2 - block diagram of the input stage of the ECG system.

figure 3 - block diagram of a processing module of a typical monitoring system ECG.

figure 4 - data processing electrocardiogram to ensure sample ECG for the totality of heartbeats.

figure 5 - measurement of various parameters of the electrocardiogram.

6 is a system for the identification and analysis of atrial fibrillation, constructed according to principles of the present invention.

7 - measure the position of the P-wave.

Fig is a measure of the morphology of the P wave.

Fig.9 - curve receiver sensitivity for reg the alignment sensitivity of detection of AF.

figa - regularly irregular intervals R-R.

figw - irregularly irregular intervals R-R.

11 - meter with hysteresis, which can be used to reduce false alarms.

Figure 1 shows a block diagram of the main components of the monitoring system ECG suitable for use according to the present invention. Provided by the set of electrodes 20 for connection to the patient's skin. Typically, the electrodes are disposable conductors on the surface of which is deposited a conductive adhesive gel, sticking to the skin. Each conductor has a latch or clip that is clipped or clamped to the wire electrode ECG system. The electrodes 20 are connected to the module 22 to obtain an ECG, which pre-conversion of the signals received at the electrodes. The signals of the electrodes is coming to the module 26 processing ECG, usually through the device 24 electrical insulation, which protects the patient from electric shock, and protects the system ECG when the patient, for example, is in the process of defibrillation. For electrical insulation are commonly used optical isolators. Then the processed ECG information is displayed or printed in the ECG report using the output device 28.

Figure 2 depicts the module receiving 22. Detect the crystals of electrodes, which typically have an amplitude of a few millivolts, are amplified by amplifiers, which are also usually equipped with high voltage protection defibrillation pulses. Amplified signals are filtered and then converted into digital signals by analog-to-digital converters. Digital signals are ECG processing under the control of the CPU 34. A large part of specialized electronics module receiving can be implemented in the form of a dedicated integrated circuit (ASIC).

Figure 3 shows a block diagram of an analysis module of a typical monitoring system ECG. The detector 42 of rhythm pulses identifies and discards electrical spikes and other electrical anomalies generated by the pacemaker for patients who wear it. The detector 44 detects QRS dominant electric pulse waveforms. Segments Q-R-S normal electrocardiogram limit the main electrical pulse ECG, i.e. the impulse that stimulates the contraction of the left ventricle. The limitation of the QRS complex is the basis for the identification of weak perturbations of the ECG, which uses a segmentator 46 cardiogram. Segmentator ECG delimits the full sequence of segments of the ECG, including P-wave and the segment from Q to U electrocardiogram. When the each waveform is completely limited, the classifier 48 heart rate compares each new reduction from the previous heartbeats and classifies heart contractions as normal (right) for a specific person or abnormal (irregular). Classification of cardiac contractions allows the analyzer 52 average heart beat to specify the characteristics of a normal heartbeat, and the amplitude and duration of segments averaged heart beat measured at step 54. Classification of heartbeats and two measurements of P-wave are used to determine the cardiac rhythm on the stage 56. Figs.4 and 5 show the effect of this processing of the electrocardiogram. On the left of figure 4 shows a set of 60 waveform of the heartbeat. Although this drawing shows the signals of six leads, in the constructed embodiment, is used only three main ECG leads. The classifier 48 heart rate compares various characteristics of the heartbeat and classifies some cardiac contractions as normal (N*,0). For example, all heart reduction of leads V5 and V6 in this example is classified as normal. The other four contain lead heartbeat, demonstrating the characteristics of a premature ventricular contraction (PVC,1; Vis,1). At step 62, the system ECG objectivemarketer normal heart rate, excludes characteristics of abnormal heartbeat, heart aligns reduction in time and averages them to obtain the averaged heart beat. Waveforms at step 64 illustrate waveforms averaged heart beat for six derivations shown in this example. According to figure 5 waveform 64 average heartbeat of the six leads are measured with respect to the various characteristics shown in step 66, for example, amplitudes and durations of the P-wave 70, Q-waves, R-waves and T-waves, and Milanovich intervals, for example, QRS duration and interval P-Q. it is Shown that the measurements recorded in the measurement table 68 for the six leads in this example.

ECG waves and their measurement can be sent on a standalone workstation with service generate report to create a report about the electrocardiogram of the patient. However, most monitoring systems ECG, for example, the monitoring system Philips IntelliVueŽ defibrillator/monitoring system Philips MRx have built packages for ECG monitoring. According to the principles of the present invention, the monitoring system ECG includes the identification and analysis of atrial fibrillation is shown in the block diagram of figure 6. The classifier 48 heartbeat transmits information of the sample P-wave and information R-R interval n is the processor 82 extraction of characteristics of P-wave and the processor 84 of the extraction characteristics of R-R. Atrial fibrillation, thus, is detected based on the combination of R-R interval and the characteristics of P-waves. In the constructed embodiment, the sample P-wave is compiled from the selected heart rate in the time window. P-waves together heart rate used to calculate the sum of absolute values of differences in the signals of P-waves in all leads of the selected heart rate in the time window. This calculated sample P-wave is then used in the preferred embodiment, the extraction position of the characteristic P-wave and morphological characteristic P-wave. The preferred positional characteristic P-wave is a measure of the deviation from the mean interval P-Q, which represents the time interval from the peak activity of P wave to beginning of QRS complex, as shown in Fig.7. Suitable intervals, which can be used represent the interval of time from peak P-wave 70 to the (negative) peak of the Q-wave 72, referred to as interval P-Q figure 7. Another measure is from time to time P-wave 70 before the time of the R wave 74. For normal sinus rhythm change interval P-Q is very small. In terms of MA change interval P-Q will be larger because the sample P-wave will not show consistently identifiable P-wave. Preference is sustained fashion morphological characteristic P-wave is the likeness of P-waves from sample to sample. Characteristics of P-wave, which can be used in the morphological analysis, include the peak amplitude P-waves, its duration in time, angle, or area relative to the baseline, which is shown by the shaded area under the P-wave 70 on Fig. For normal sinus rhythm characteristics of the P-wave will be very little change from sample to sample. In the presence of MA match the characteristics of the sample will be weak.

The preferred measure of R-R interval for the detection of AF is the regularity of the heartbeat. Regular R-R interval is characteristic of a normal sinus rhythm, and irregular R-R intervals-are characteristic of the MA. To assess the regularity of the R-R intervals-you can use a Markov model using data interval R-R.

The module 80 noise assessment is used to calculate estimates of noise artifacts in the ECG signal. The preferred measure of noise is the sum of the second derivatives of the signal samples in the field P-wave measured for each heart beat. This measure comes to the processor 82 extraction of characteristics of P-wave and the processor 84 of the extraction characteristics of R-R to suppress the extraction of characteristics in conditions of high noise.

When the noise level is low enough for the extraction of characteristics extracted characteristics of P-waves and R-R interval are declassification 90 MA. Extracted characteristics are combined classifier for the formation of the characteristic vector, which is used to classify the rhythm as either having MA or without MA. For example, the above-mentioned characteristics of P-waves and R-R interval can be combined to identify MA according to

MA = [(abnormal rhythm) And (no P-wave OR an irregular interval P-R OR a bad coincidence samples P-wave)].

In the constructed embodiment, the classifier also provides a measure of trust of its determination. Expert data electrocardiograms of patients with known rhythms with AF and without MA come on extractors 82 and 84 signs and classifier 90 that allows you to observe the reaction of extractors on known conditions of rhythm. Each extracted characteristic unverified rhythm can then be evaluated relative to these known conditions, and to determine the likelihood with which each feature is a characteristic of the MA. Joint evaluations are presented to the operator to ensure that measures the confidence of the results of the classification performed by the classifier 90.

The use of such expert data known populations of patients for "learning" system allows you to achieve a compromise between sensitivity and specificity for different system configurations corresponding to different is alazem patients. Figure 9 shows the ranges of the operating characteristics of the receiver, for which you can pre-configure the system for the detection of atrial fibrillation. When sensitivity and her additional 1-specificity balanced, the curve is neutral, which demonstrates the curve 300. If the system is configured to have an increased sensitivity to MA (examples below), it will demonstrate a working characteristic is shifted in the direction of sensitivity, as shown by curve 302, and if the system is configured to have a high specificity, the working curve will go, as shown by curve 304. In addition, by training the system on known populations of patients, custom controls can be simplified, allowing the user to select a population of patients, instead of to go into detail, setting numerous parameters. The choice of a specific population of patients, for example, "after the pacemaker" or "chronic MA, may cause automatic adjustment of the system rated working parameters desired for this particular population. For example, patients with chronic or permanent MA may not need the alarm, and only the determination of the tendency of frequency and rhythm, and the calculation load MA. Patients withdrawn from the state of atrial fibrillation with POM is using a pacemaker, need sensitive detection of MA that medical personnel would immediately know when the patient will reappear MA. Patients cardiac surgery also fall into this category of patients requiring sensitive detection of MA for immediate notification of the medical staff about the change of rhythm, proving the occurrence of MA or leaving MA. For patients with a history of paroxysmal AF, which are characterized by the appearance of MA and the output of the MA, preferably less sensitive detection. If you want to know about the change of rhythm, the alarm should be as precise as possible, in a compromise with sensitivity to lower frequency of false alarms. A short flash of the MA does not play a big role for this group. Adjusting the balance of sensitivity/specificity, preferably by simply selecting the type of patient population, the monitor MA, meets the present invention can be configured in relation to how long and how often he must see episodes of MA before generates a signal "the beginning of man, and likewise, how long and how often he must see the episodes without MA before generates a signal "the end of man. In addition to managing the minimum duration and frequency of the rhythm, similar to control system settings can be used to provide high sensitivity is entrusted identify MA (with a decrease in specificity and increased frequency of false alarms) or highly specific detection of MA (with reduced sensitivity).

To reduce the possibility of false alarms, the example shown in Fig.6, includes a corrector 92 for re-classification of those vectors characteristic that the classifier 90 were mistakenly classified as MA. Optionally, you can use a variety of criteria for re-classification. For example, if you have a good P-wave is determined based on a characteristic or characteristics of the P-wave vector of the sign should be classified as "no MA" regardless of the sign of the interval R-R. This allows to avoid classification of irregular rhythms with the correct P-waves as MA. Another possibility is to check the regularity of the intervals R-R-R, so-called double intervals R-R Interval, R-R, which themselves are wrong, but regularly repeated, can be a characteristic of the atrial bigeminy not MA, whereas irregularly repeating the wrong interval R-R are characteristic of MA. In the example shown in figa, wrong interval R-R R-R1and R-R2shown between R-waves 100 and 102 and R-waves 102 and 104, respectively. But this abnormal rhythm regular, repeated, because the next interval is the interval R-R1between R-waves 104 and 106. The analysis of the interval R-R-R to R-R1and R-R2will reveal this regular irregularity. This occurs on a regular basis is the action scene the wrong rhythm can be a characteristic of the atrial bigeminy, and not MA. However, fig.10b shows three different interval R-R: R-R1R-R2and R-R3, between consecutive R-waves 100-107. Such irregular irregularity is detected by analyzing the interval R-R-R, making this arrhythmia can be classified as MA.

To further reduce false alarms implemented counter episodes MA with built-in hysteresis. Hysteresis allows to suppress the alarm of short episodes of erroneous results, and it can be used to modify the sensitivity and specificity of detection of AF. Figure 11 shows a single counter (you can use the symmetric counters)that is configured so that the sensitivity at the beginning of the episode MA was less than at the end of the episode MA. This counter will be more definitely indicate to the user about the end of the episode MA that may play a role in clinical diagnosis or therapy or medicine. In the example shown figure 11, the upper step function 200 shows the intervals of the analysis of multiple cardiac contractions that occur at time plotted on the x-axis of the drawing and are classified as either with AF or without MA, which is indicated on the y-axis of the drawing. The counter is initialized in this example, at the level of -4, which shows the beginning of stupine the counter 202, which graphically shows the increase of the counter. Each interval classified as MA, leads to a stepwise increase of the counter (e.g., time 2), and each interval classified as the absence of MA leads to a stepwise reduction of the counter (for example, point 3). When the value of the counter reaches zero threshold at time 9, it increases to a maximum value of +2 in this example, which remains until the intervals without MA will not decrease. It starts at time 12, occurs when the interval without MA. The next interval without MA at time 13 returns the counter back to zero, and at this point, the counter is immediately reset to the initial level-4. The signal is generated whenever the value of the counter is greater than zero, and therefore the alarm management 204 enters the on state at time 9 and returns to the off state at time 13. You can see that alarm management is less sensitive to episodes of AF that turn the alarm on (the MA), and is more sensitive to intervals without MA, which means the alarm off status (end MA). Different initial configuration and thresholds for counter will lead to different values of hysteresis and, therefore, different sensitivity and specificity of the t in granting MA signal or a status report to the user.

The output of the corrector 92 reports revealed rhythms with MA, as shown in Fig.6, which can be classifications MA/without MA" function 200, the output limiting false alarms, for example, function 204 control alarm, or any other controlled deciding MA, implemented by the user. Identified rhythms with MA, which includes the initial and the final episodes of MA, come on calculator 94 MA load, is shown in Fig.6 for messages about MA load to the user. Calculated load MA is a statistical calculation that represents the frequency and duration of episodes of MA. Load MA can additionally or alternatively be expressed as a certain percentage of the previous period of time (e.g. the last 24 hours or full-time monitoring), during which the patient's heart was in atrial fibrillation. Load MA is displayed on the display or the printer 28 and may be displayed numerically, graphically, in the form of a trend graph or combinations thereof. Doctors can use the load MA or trend MA load for the purpose of further treatment of the patient or prescription drugs in his.

1. Monitoring system for atrial fibrillation (AF)containing:
data source electrocardiogram,
extract the signs of the P-wave,
extractor signs of R-R interval,
the classifier MA, responsive to the sign of the P-wave and the characteristic of R-R interval, which classifies the cardiac rhythm as MA or without MA
a display, responsive to the classifier MA to display the classification MA, and
user input to adjust balance sensitivity/specificity of detection rate with MA, with user input further comprises selecting the type of patient population for automatically adjusting the operating parameters of the detection rate with MA for the selected type of patient population.

2. Monitoring system for atrial fibrillation (AF) according to claim 1, in which the source data of the electrocardiogram contains the classifier heartbeat.

3. Monitoring system for atrial fibrillation (AF) according to claim 2, in which the classifier heartbeat creates a sample of the P-wave.

4. Monitoring system for atrial fibrillation (AF) according to claim 1, in which the extractor signs of the P-wave creates a positional characteristic of the P-wave and morphological characteristic of the P-wave.

5. Monitoring system for atrial fibrillation (AF) according to claim 4, in which the classifier MA responds to positional characteristic of the P-wave morphological characteristic of the P-wave and the characteristic of R-R interval for classifying a cardiac rhythm as MA or without MA.

6. Monitoring system for atrial fibrillation (AF) by A4, in which positional characteristic of the P-wave contains the interval P-Q or P-R.

7. Monitoring system for atrial fibrillation (AF) according to claim 1, additionally containing load calculator MA, responsive to cardiac rhythms classified as MA or without MA, to develop measures load MA representing the frequency and duration of MA.

8. Monitoring system for atrial fibrillation (AF) according to claim 1, additionally containing corrector, responsive to the classification MA to reduce the occurrence of false alarms.

9. Monitoring system for atrial fibrillation (AF) according to claim 8, in which the corrector is capable of measuring irregular R-R intervals.

10. Monitoring system for atrial fibrillation (AF) according to claim 8, in which the corrector is able to identify, at short intervals, MA.

11. Monitoring system for atrial fibrillation (AF) according to claim 10, in which the offset is additionally able to suppress alarms for short intervals MA using hysteresis.

12. Monitoring system for atrial fibrillation (AF) according to claim 1, in which the operation of the extractor signs of the P-wave and extractor signs of R-R interval includes the assessment of noise.

13. Monitoring system for atrial fibrillation (AF) according to claim 7, in which the load calculator MA reports the percentage of time during which the heart rate showed atrial Ari is MIA.

14. Monitoring system for atrial fibrillation (AF) according to claim 7, in which the load calculator MA reports the frequency and/or duration of the detected episodes of rhythm with MA.



 

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1 ex, 2 dwg

FIELD: medicine.

SUBSTANCE: invention relates to medicine, namely to cardiology. Electrocardiogram is registered. In 1s ECG segments spectral power of teta-rhythm(4-7 Hz), alfa rhythm(8-12 Hz), beta-rhythm (13-17 Hz) and gamma-rhythm (18-40 Hz) is determined. Stages of ventricle fibrillations are determined. Beta-fibrillation is characterised by prevailing of beta-rhythm with power 40-70%, teta-fibrillation - by prevailing of teta-rhythm with power 40-70%, dysrhythmia - at the first place teta-, alfa- or beta-rhythm with power less than 40% and at the second - any rhythm with power less or equal to 30%. If power of teta-, alfa- or beta-rhythm is higher than 70% or in electrocardiogram section at the first place with respect to power is gamma-rhythm, absence of ventricles fibrillation is diagnosed.

EFFECT: method allows to determine stages of ventricle fibrillation and increase sensibility and specificity of fibrillation diagnostics.

2 dwg, 2 tbl, 5 ex

FIELD: medicine.

SUBSTANCE: invention relates to field of medicine, namely to cardiology and disease prevention. Before and after treatment by means of tetrapolar chest rheography coefficient of vasomyocardial ratios of central hemodynamics (CVMRCH) is determined and echocardiography with calculation of index of left ventricle myocardium weight and 24-hour Holter monitoring with determination of myocardium ischemia duration, circadium index, total integral of segment ST displacement are performed. Integral index (II) is calculated by formula: II= TID/(DI*CI*IIMHR), where IIMHR - integral index of myocardial-hemodynamic ratios, IIMHR= ILVMW / CVMRCH, CVMRCH - coefficient of vasomyocardial ratios if central hemodynamics in per cent. CVMRCH =(SV+SI+HI)*100%/SPR, SV - stroke volume in ml; SI -stroke index in ml/ m2; HI -heart index in l/min/ m2; SPR - specific peripheral resistance in din*sec cm-5/m; ILVMW - index of left ventricle myocardium weight in g/ m2; DI -duration of painless ischemia in min/24-hours; CI - circadium index; TID - total integral of segment ST displacement in mcV*min. If II value grows, treatment is estimated as efficient, if II decreases - progressing of coronary insufficiency.

EFFECT: method extends arsenal of means for determining change of adaptative-compensatory possibilities of organism in CHD patients with painless myocardium ischemia.

2 ex

FIELD: medicine.

SUBSTANCE: method involves recording and analyzing electrocardiogram with PQ, QRS, QT intervals estimation and deviation from reference values being detected. P, Q, R, S, T waves taken from basic I, II, III and amplified aVR, aVL, aVF leads are additionally studied. The deviations showing more than 25% increase or reduction in amplitude and/or polarity, and/or wave shape changes are detected. Five or more deviations being observed, myocardial dystrophy is to be diagnosed. The following values are taken as reference values for P, Q, R, S, T waves. PI=(0.04±0.008)H; PII=(0.18±0.01)H; PIII=(0.15±0.05)H; QI=(0.15±0.03)H; QII=(0)H; QIII=(0)H;RI=(0.15±0.02)H; RII=(0.33±0.05)H; RIII=(0.35±0.05)H; SI=(0.18±0.04)H; SII=(1.40±0.08)H; SIII=(1.43±0.07)H; TI=(0.075±0.013)H; TII=(0.52±0.05)H; TIII=(0.51±0.05)H, where H is the amplitude corresponding to control distortion at voltage of 1 mV; PI, PIII, QI, QII, RI, RII, SI; SII, SIII, TI, TII, TIII are the P, Q, R, S, T-wave amplitudes in healthy animal, respectively, in I, II, and III basic leads.

EFFECT: high reliability of diagnosis.

7 dwg, 2 tbl

FIELD: medicine.

SUBSTANCE: method involves treating gastrointestinal tract without drugs, cleaning organism with enema and lavage procedure, per os introducing a set of nutrient substrates and proper microflora metabolism regulators as a complex of bran and vegetable additives being carriers of micro- and macroelements in the following ingredient proportion taken in weight parts. Bran - 72-86, microelements carriers like green tea, sea kale, shelf fungus, agar-agar, wartwort - 2-3, macroelements carriers like sedge grass, nettle, balm - 1.5-2.5; microbiologic fermentation regulators like cardamom, chili, black pepper, red pepper, nutmeg - 1.0-2.0, fermentation initiator (pastry texturizing agent) - 0.2-0.5. Single lavage procedure with appropriate preparation or cleaning vegetable protein enema of 1.5-2 l is applied before introducing nutrient substrates. A course of own microflora cultivation in gastrointestinal tract is carried out by introducing nutrient substrates set and daily volume of liquid in the amount of not less than 30 ml/kg of body weight and 10-20 mg of lactulose.

EFFECT: enhanced effectiveness of treatment; minimized enema-mediated treatment.

3 cl, 3 dwg

The invention relates to medicine, in particular orthopedics

FIELD: medicine.

SUBSTANCE: method involves treating gastrointestinal tract without drugs, cleaning organism with enema and lavage procedure, per os introducing a set of nutrient substrates and proper microflora metabolism regulators as a complex of bran and vegetable additives being carriers of micro- and macroelements in the following ingredient proportion taken in weight parts. Bran - 72-86, microelements carriers like green tea, sea kale, shelf fungus, agar-agar, wartwort - 2-3, macroelements carriers like sedge grass, nettle, balm - 1.5-2.5; microbiologic fermentation regulators like cardamom, chili, black pepper, red pepper, nutmeg - 1.0-2.0, fermentation initiator (pastry texturizing agent) - 0.2-0.5. Single lavage procedure with appropriate preparation or cleaning vegetable protein enema of 1.5-2 l is applied before introducing nutrient substrates. A course of own microflora cultivation in gastrointestinal tract is carried out by introducing nutrient substrates set and daily volume of liquid in the amount of not less than 30 ml/kg of body weight and 10-20 mg of lactulose.

EFFECT: enhanced effectiveness of treatment; minimized enema-mediated treatment.

3 cl, 3 dwg

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