WO1997022296A1 - System and method for testing the function of the autonomic nervous system - Google Patents

Abstract

A system and method for testing the function of the autonomic nervous system of human body is disclosed. The method comprises the following steps: stimulating the sympathetic nervous system or the parasympathetic nervous system; analysing the R wave of ECG signal sectionally according to different stimulating; analysing it's power spectrum using Auto-Regressive (AR) model so as to obtain the quantitative analysing result of the intensity difference of the excitability between the sympathetic nervous system and the parasympathetic nervous system of the tested body.

Description

 Autonomic nervous system function test system and method thereof

. Field of surgery

 The present invention relates to a sputum and method for testing human cerebral pedigrees, and more particularly to a function for sympathetic and sympathetic nerves by spectral analysis of heart rate variability The method of detecting and analyzing the status of the sympathetic and sympathetic nerves.

Background technique

 Among the human body, including the sympathetic nerves and the sympathetic nerves of the autonomic nerves? t, like the control in the cloud control system, control the internal physiological and biochemical activities of the human body -f), adjust the activities of the involuntary S official, and then achieve the balance of the internal environment of the body. Therefore, the detection of 吋 吋 神经 劝 劝 劝 糸 糸 糸 糸 糸 糸 糸 糸 糸 糸 糸 糸 糸 糸 糸 糸 糸 糸 糸 糸 糸 糸 糸 糸 糸 糸 糸 糸 糸 糸 糸 糸 糸 糸 糸 糸 糸 糸 糸 糸 糸 糸 糸 糸 糸

 In the control system of the human resting sheep, the sympathetic nerve center located in the medulla oblongata and the sympathetic side of the fr'iM and its nerve fibers directly control the activity state of the cardiovascular system and some involuntary S officials. In general, patients with excessive sympathetic excitability may have symptoms such as rapid heartbeat, '-sweeing sweat, I diarrhea, facial flushing, and emotional irritability; and those who pay too much sympathetic excitability often have the opposite Braided. However, so far, there has not been a method or instrument in the medical field that can directly analyze and measure the sympathetic and sympathetic nerves, ie, the excitability, qualitative and quantitative analysis. a is a laboratory experiment in addition to 7 in the laboratory, it is impossible to test the u's sympathetic and sympathetic nerves. What is currently available is based on the above-mentioned mechanism, relying on doctors to ask the person who is suspected of having an autonomic nervous system to persuade the disorder, and then let the person to do a comprehensive inspection of the various bureaucratic fish. When it comes to removing all organs

3⁄4 fx change, it subjectively determined to be autonomous species of dysmenorrhea (called autonomic H). As for the sympathetic or sympathetic nerves, is it strong? It is only by pushing it. This makes the break period prolonged, the cost of the break is expensive, and this is in his: 'Go base <·:[;_± ί'ι- out; t break. Not ideal, People ίί] have realized that the speed of the human heart beating is controlled by the excitability of the sympathetic and sympathetic chanting. Sympathetic nerve excitability, will lead to 'speed faster: and even sympathetic nerve excitability, it will lead to slowing of heart rate. Therefore, the heart rate can be used as a window to detect and judge the functional state of the sympathetic and sympathetic nerves. In particular, in recent years, Heart Rate Variability (HRV) has become a target of great attention and research. The variability of heart rate (HR V ) is thought to be related to the physiological and rational state of the heart, and is a manifestation of the anti-FL χ λ and the heart disease of the cardiovascular system for internal and external environmental changes. Therefore, the analysis of heart rate variability, especially the analysis techniques developed in recent years, is to evaluate the physiological and pathological state of cardiovascular system, especially

;1 cardiovascular independent chanting system function, providing a non-invasive research method and important quantitative indicators.

 Early analysis of heart rate variability in the laboratory mainly used statistical methods, such as the mean and standard deviation of the R-interval, the length of each interval - the interval between the R-intervals, L3, and other methods can identify autonomy. The total change in neural activity, but can not distinguish between sympathetic and f| Therefore, in recent years, people have developed a method of using the f] rate bifurcation method, including the fast Fourier transform (FFT) method, the periodogram, and the autoregressive mode 1# estimation method. Since the frequency resolution obtained by the autoregressive model method has a better estimation effect than the FFT and the periodogram method for the short-term data, the autoregressive model method is now used to increase the rate of heart rate variability. analysis. However, this kind of research is also at the level of academic discussion, and there is no one based on heart rate variability #analysis

\k Value equipment for neurological function is made for medical use

11 Ming 3⁄4:

 The purpose of the present invention is to provide a system and method capable of non-invasively measuring the persuasive state of the human nervous system, and completely changing the complexity of the prior art by complexizing each of the 3⁄4 3⁄4 A method of performing exclusion and extrapolation for diagnosis is detected.

It is a further object of the present invention to provide a complete autonomic nervous system that allows it to distinguish between the recipient, the interaction between the 3⁄4祌 and the sympathetic nerve, by testing the heart rate of the subject. And can give the intersection, % 祌 and ί 十ν; which one of the gods is excitatory, and the qualitative and quantitative i-break number It is still a further object of the present invention to provide a set of mutual sympathetic and sympathetic nerves between a subject capable of operating in a plant nervous system function test system and analyzing and processing test data of heart rate variability. Software that acts to persuade the quantitative conclusions of the state.

 According to the present invention, there is provided a system and method for detecting an anesthetic power system by collecting an electrocardiographic signal and analyzing heart rate variability. The invention includes an ECG signal storage ^, 4! The electrocardiogram of the subject collected in a certain period of time is stored in the storage I ^, the storage S can be portable or single-chip type, it It may be provided with an infrared emission ^ for outputting the stored ECG signal. Accordingly, the system may further comprise an infrared for receiving and passing through the interface circuit, the output ECG signal being sent to the computer for processing Receiver; a microcomputer for performing a perceptual analysis of the ECG signal to obtain a qualitative report of the subject's confession and sympathetic nerves: an output device for printing Test the results of the tiller. At the same time, the system of the present invention also provides a speech unit and a speaker that can guide the movement of the subject, adjust the different body position, and collect the ECG signal in the state of the sympathetic and/or the sympathetic nervous system. .

 The basic principle of the present invention is to utilize the difference in body position and respiratory state on the one hand, and the different effects of drugs (not to be administered, administered, and give different drugs) on the other hand, respectively, and to pay sympathetic and I or pay Sympathetic nervous system for stimulation, detection of Φ'] heart rate degeneration before and after stimulating - use this window to conduct a table! To analyze the signal control theory, so that you can get sympathetic sympathetic nervous system and pay sympathy The interaction of the system and the conclusion of the functional state.

 According to the present invention, heart rate variability is analyzed using spectral analysis. The first is to extract each R-R interval from the original ECG signal to form an R-R interval sequence. Usually, an interval sequence is used, the amplitude value is equal to the R-R interval value, and <L can be sampled by interpolation method into time interval such as f'r. It has been shown that these two methods are equivalent. For accidental surges, premature beats and other disturbances, % processing can be performed by filtering, interpolation, and the like.

吋Quasi-random heart rate variability signal, usually approximated as a stationary random over, can be seen as a single density - No white noise W ( n ) excitation of a certain - impulse L is h ( n ) The linear beam system produces: _η = w _n * Where h" is the total excitation response, * is convolution, W, and is white noise.

Then Z=e^

Among them, Sw, S _x are the persuasion rate of input and output respectively, H is the frequency characteristic or transfer function of the system. Therefore, it is known that the ruler H(Z) can find the random signal 镨Sx(e). H

(Z) is the parametric model of the random signal. The present invention employs an autoregressive (eight model (referred to as an all-pole model) whose expression is as follows:

 M

 X(n) = -∑ a(k)X(n-k)+W(n)

 K-1

X(Z) ¹

 Two H(Z) = ―

 W(Z) 1 ∑ a(k)Z

 k=l

Where M is the order of the AR model and can be determined according to certain criteria, such as the MC or FPE criteria and the nature of the it. Through the model parameter estimation algorithm, the parameter a (k) of the AR mode S1 and the white noise spectral density No can be obtained, and then the density of the persuasion rate can be estimated by the following formula:

Sx(f) = NoAt/|l+ ∑ a _{M k} eXp(-j2 nf At) Using the system and method of the present invention, collecting and recording different poses (flat and standing) of the recognized person within a certain period of time And continuously changing ECG signals (ECG) under different respiratory states (free breathing and controlled breathing). First, the K-waves of these ECG signals are used. The adaptive criterion is used to identify the R-waves with the first derivative and find the R 1»] period, thus giving the R-wave sequence signal.吋ECG signal is preprocessed using low-pass differential.. Γ)·· The first derivative of ECG, the formula is:

 d(t -=ECG(t)-ECG(t-4)

.3⁄4 Perform a first-order low-pass wave that is low-passed? The first derivative of the t-wave is:

Replacement page (Article 26)

 < After a period of time (for example, 5 seconds), the ECG self-learning

After learning and adapting for £ CG, the above-mentioned f(t) is used to find the maximum absolute value PK and the two R-R intervals. For example, ^ Η ₀ (ϋ) = 0.7 · χ · PK is used as the threshold for identifying the R wave and the average of the two R - R as the initial value of 1V3⁄4 (RRAV) between the average R-R. Count f (t) sequentially. Find the point of PK(n) > (n) and determine whether it is the R wave and the R wave position. Here, the adaptive recursion of the threshold is:

 0.3 * (0.7 * Ρ (η)) 0.7 * ΡΚ(η)<1.5 * Ηο(π) 0.1 * (0.7 * Ρ (η)) Q.7 * ΡΚ (π)>1· 5 * Ηο(π) (") + 0.2 * KJiAV(n) 1.5*KR.4V(»)>HK>0.5 KKAV(n)

 other

itAV ("ten"

 After the above pretreatment and according to the above body position and respiratory status, the signals are divided. The autoregressive (AR) model estimates are performed for each segment of the signal to obtain the AR shuttle parameters. The estimation of the parameters of the AR model includes the estimation of the model coefficients ο, -α^, the estimation of the white noise 'S^No, and the estimation of the order M of the model, from the time domain to

"Get the persuasion rate of the measured ECG signals in each segment 1#, find out the characteristic values, 3⁄4 final ^: over 3⁄4 [f" theoretical analysis, and get the sympathetic god & ¹ and pay confession in different states Comparison and conclusion of the excitatory situation.

The invention changes the existing means for persuasing the idiopathic physiology of the plant, so that the methods of using the heart rate variability (HRV) for academic use have been studied and clinically completed, and the heart rate variability is once ( HRV) advised the Xin method of language to persuade use - | on Pro / ί inch plant Chong by k, the system being able to persuade measured as i can to persuade the autonomic nervous system ^measured;? mention ^ 7 plus direct and accurate means,

The invention can not only understand the super-potential of heart rate change, but also distinguish the interaction between a certain intersection, the 3⁄4 nerve and the sympathetic nerve, and change the prior art using the non-inference method of j, and rely on the detection to judge that it is sympathetic Still paying. 3⁄4 祌 兴奋 兴奋 祌 3 3 3 3 3 3 3 3 3 3 3 3 衣 衣 衣 衣 衣 衣 衣 衣 衣 衣 衣 衣 衣 衣 衣 衣 衣 衣 衣 衣 衣 衣 衣 衣 衣 衣 衣 衣 衣 衣 衣 衣Quantitative -: t breaks the result, ί'1 such as ί-ϊ- ί'Ι is ^ sensory nerve system 3⁄4 is 忖 忖 ^ ^tti, Ϊ" 3⁄4 兴性性 3⁄4, -; M less ^ The number; and whether there is improvement and improvement after treatment, it is also easy to draw conclusions from the measured i-curve and numbers.

Appendix a Overview

 The above and other objects, features and advantages of the present invention will become more <RTIgt; In the drawing:

 1 is a schematic representation of a plant neurological functional test system according to the invention. 2 is a schematic illustration of one embodiment of a testing process for a system and method in accordance with the present invention.

 Fig. 3 is a flow chart of a soft gram running in the autonomic nervous system function test system according to the present invention.

 It is an electrical schematic of the test system central electrical signal memory in accordance with the present invention. FIG. 5 is a schematic schematic diagram of the mid-infrared receiver of the system shown in FIG. 1.

 S 6 - Figure 12 are respectively found in the four states of the control experiment without administration.

HKV curve, where:

 Figure 6 is a curve in the supine position of the recumbent (i.e., in the sacral section of Figure 2): Figure 7 is a curve in the controlled position of the supine position (i.e., in the B segment of the figure); It is a curve in the standing free breathing state (ie, in the C segment of Figure 2); Figure 9 is the curve in the standing position controlled breathing (ie, in the D segment of Figure 2); 10 is the entire supine a curve of free breathing and controlled breathing in position (ie, Λ + Β = £ in Figure 2);

m i〗 Yes: ^ Under the condition of free breathing and controlled breathing in standing position (ie a c

+ D = curve in section F):

 12 is the curve for all 15 minutes of testing including the supine position and standing under free and controlled breathing (ie A + B + C + D = E + F = R in Figure 2).

 13- 1, 8 is a comparison of the time-domain X-signal and ^-de-dc component of each segment corresponding to Fig. 6 -...12 and the signal of the frequency-sense rate signal S after the AR model transformation ^:

1 3 is a comparison chart of the X < ί and the persuasion s signal s in the squat section of the squat free breathing in the experiment compared with the E 6: Figure 14 is a comparison diagram of the X signal and the S signal corresponding to the B segment shown in Figure 7: Figure 15 is a comparison diagram of the X signal and the S signal corresponding to the C segment shown in Figure 8: Figure 16 is the same as Figure 9 A comparison diagram of the X signal and the S signal corresponding to the D segment shown in the drawing: Fig. 17 is a view showing an X signal and an S signal corresponding to the E segment shown in Fig. 10: Fig. 18 is a F segment shown in Fig. 11 Corresponding graph of the corresponding X signal and S signal: Fig. 19 is a three-dimensional graph of four curves A, B, C, and D made by the segment state as the Z axis.

 Figure 20 is a three-dimensional graph of six curves A, B, C, D, E, and F made with the segmentation state as the Z axis.

 Fig. 21 is a diagram showing an example of a quantitative conclusion report on the effect of body position on plant persuasion after the calculation of the pedigree analysis in accordance with the present invention.

 Fig. 22 is a diagram showing an example of a quantitative conclusion report on the effects of the function of the respiratory sputum plant after the analysis of the sputum analysis in accordance with the present invention.

BEST MODE FOR CARRYING OUT THE INVENTION

 Referring to Figure 1, there is shown the principle of perennial, experimental, and f-systems according to the present invention. In the system of the present invention, a single-chip ECG signal acquisition and storage device i 0 is included, and the ECG under different posture states and the same breathing state (Fig. 2) is collected from the subject by a certain ECG lead. The signal is stored in the ECG storage; 11 within. @4 shows the electrical principle H of the ECG signal memory, where: U is the central processing; ^ CPU, U2 is, read and store iROM, U3 is the address buffer, U4 is the random memory W± AM.Uo For analog-to-digital conversion ■!!: A/1, U6 is the line driver ^, L; 7 is the binary input into the NOR gate. U8 is the ECG signal amplified by four operational amplifiers. ULEAD is the ECG signal input terminal. . The ECG signal collected through this end passes through the ECG amplifier, and is sent to the A/D converter ϋ5, sent to the CPU (U1) for processing, and stored in the random storage S (RAM M and 'port, read and save Storage: (ROM) U2.

In this embodiment, a 15-minute ECG signal is acquired and stored. Refer to LS 2, because the supine ί position and the controlled breathing faster than the free-style breathing rate are separately stimulating: Concealed nerves. Standing and free breathing stimulate the sympathetic nerves, respectively. Therefore, the present invention-Hf 15 minutes is divided into four stages of different sympathetic and/or paying, and the 3⁄4 nervous system is stimulated differently: in the first period of time A, the person who is a long time takes a flat sleep {ί (stimulation) Pay Sensory nerve) free breathing (stimulation of sympathetic nerves); in the second period of time B, the subject's position is still supine (stimulation pays the nerve), but changed to controlled breathing, that is, given a fast by the speech unit Exercising under the guidance of free-breathing beats (stimulation and sympathetic nerves): In the third period of time C, the subject's position changed from supine to standing (stimulating sympathetic nerves), free-style breathing (stimulating sympathetic nerves) The fourth period of time D is the controlled breathing of the subject (stimulation of sympathetic nerves) (stimulation of sympathetic nerves). In this embodiment, the segment is 3 minutes: the β segment is 4 minutes: the C segment is 4 minutes and the signal in the first minute is the signal during the change from the supine position to the standing position. Special treatment; D segment is also 4 minutes. ECG Signal Storage _ The ECG signals in these four different body position breathing modes were continuously tested in 15 minutes. The stored electrocardiographic signal is transmitted to the microcomputer 23 via an infrared emission ^ 12 (see Fig. 1). The microcomputer 23 is connected to the infrared receiver 21 via the interface 22, and further receives the electrocardiographic signals (tX, G) received by the infrared receiver 21.

Figure 5 shows the principle S of an infrared receiver 21 that can be used in the system of the present invention. The signal received by the infrared receiving tube D1 is operated and amplified by the operational amplifier IC1, and is compared by the comparison of !!!: IC2 is inverted by the phase If IC3, and then output by the line cloud force ^ 1C, which is output. Output signal D. _u , RS232 sent to the microcomputer 23 via the socket S2. In the figure, S], S2, and S3 are sockets. Of course, it is also possible to realize the storage of the electrocardiogram signal by means of wired transmission without using infrared transmission. The signal transmission, at this time can be used to connect the driver chip to the string of the microcomputer (亍 communication interface.

 In the test system of the present invention, there is also provided a voice unit and a speaker controlled by the microcomputer 23 for issuing the life of the subject (from lying position to standing, from free breathing to ± controlling breathing, etc.) . Of course, the language unit and the speaker can also be combined with the finger-type ECG signal set and storage device 10.

The microcomputer 23 may include a voice card, a multifunction card, a display card hard disk, a floppy disk drive, a main board, a power source, and the like. 24 also has an output device for analysis and hooking the S signal when in the different sections of the city and urban signal X (see FIG. (! - FIG. ₂₂₎ υ

 Figure 3 shows the software processing of the ECG i number, the persuasion analysis, and the quantitative results of the neural and sympathetic neural system.

参! / L Figure 3, by the ECG signal acquisition and storage device 10 sent out the ECG ^ 1 1 red red X - The external transfer is sent to the microcomputer 32. In the microcomputer 32, the electrocardiogram self-learning is performed for a certain period of time, for example, 5 seconds, and the R wave identification step S12 is entered. The adaptive criterion is used to identify the R wave by the first derivative and the R wave sequence signal is sent (ie, sequentially R-R interval values collected sequentially. It includes using low-pass differential to find the first derivative of the ECG

 d(i) = ECG(i)-ECG(t-4)

 一 The first derivative of the ECG signal is low-pass filtered to obtain:

 l) +d(i)-d (Bu 6)

Then find the maximum absolute value PK and the two R-R intervals for f(t). Hey. (Ό) = 0.7 X ΡΚ As the threshold for identifying the R wave, the average of the two R-R intervals is taken as the initial value of the average R-R interval R AV . The points of ΡΚ( η ) > Η ₀ ( η ) are found by sequentially calculating f (t ) to determine whether the positions of the R wave and the R wave are. Here, the recursive formula for threshold adaptation is:

0. 7 * ΡΚ (π)<1.5 * Ηο(η)

0.7 * ΡΚ (")>].5 *- Η ₀ (") 1.5* RKAV(„) _W - _{ft>0 5 HJiAV(n)} Otherwise, 3, the obtained R-R interval time domain signal is divided into Jt and preprocessed in step S13. That is, according to the segment shown in Fig. 2 (A, B, C, D and the E-station representing the entire supine position and the F-segment representing the entire standing position), the obtained R-R interval time signal (R wave) The sequence signal is intercepted and processed. Here, the R-wave signal that is divided in each segment is called the X signal, which is the segmented R-R interval time domain signal. Figures 16-18 are R-R interval waveforms recorded in a control experiment performed when no drug was administered to the subject. Figure 18 is the entire R-R interval waveform, while Figure 16-7] is the R-R interval waveform of A, B, C, D, h; In the figure, the symbols "ΧΑ1200Α"·"ΧΒ1200Λ" • ·... ·, the second letter indicates the segmentation number from which the curve is taken, and the most) β letter indicates the nature of the experiment. Here, Α represents an unadministered experiment. For example: Η 17 f indicates "XB1200Α" indicates that the curve is a squat section, ie, a flat squat, a control h'n, and a table gives 3⁄4 ύ ^ι ). The "ΧΕ1200Λ" marked in the middle represents the song as li rt -'pf 1. Uncontrolled squatting experiment of supine free breathing plus controlled breathing, etc.

3⁄4 It should be pointed out that for the accuracy of the test, P is only 3⁄4, and there is also a feedback check in the system. f 3⁄4, when R-R> 1.8RRAV, in中in] possible 3⁄4 Check the checkback, the threshold for the checkback is:

 H,(n) ::0.5 * Ho(n)

 After the above analysis processing is performed on the R--R interval, the processing proceeds from step S21.

 1

 The regression (AR) model is used for 镨 estimation.

 The following is the basic principle of autoregressive ( Α ) modulus estimation used in the present invention. According to the Woid decomposition theorem, a flat random signal X (n ) can be regarded as a white noise W(n) by spectral density - o. A linear system with a shock response of h( n ), ie:

Xn— Wn 3⁄4 hn ( * convolution)

贝'1 Sx(^)= Sw ( ) |fi ( ) I ²

Where Sw, S are the persuasion rates of the input and output, respectively, and H is the frequency-semi-characteristic or transfer function of the system. In this case, H(Z) is known, and the random signal is good for SxW', and H (Z) is a parametric model of random apostrophes.

 The autoregressive (AR) model nicknames the all-pole model, and its expression is as follows:

Xn = - n

 K

Z change : _ _lz)_ 1

 H (Z

W (Ζ)

 0

Replacement page (Article 26) 4 get the shuttle 3⁄4! Participate in white Number

 ∑ M ^

 Where f is the o frequency (Hz) and At is the sampling interval.

 The estimation of the AR model parameters includes: model parameters. ., ά ^ ^ Density No . and '钹3⁄4! Order M

 ∑ i

 The method of counting is not one or one, here? For example, the program used in the program

(Small square) predicted MARPLE method. This kind of twisg algorithm with a typical method is higher than the typical tkisg algorithm, and there is no spectral component splitting observation, and the half-drift is small, and the degree is equal to the level of the AR. Basic - as follows:

 There is a full-pole flat-section random process, from (1) where '1 is extended before' and then Γ3⁄4 pre-» are as follows:

 Forward prediction error: f Μ, Κ 2 K M, i person K+M-i l^K^N - h (3)

Μ, - ^ - M a _M .o - 1 : _: AR镆 order; N: X data length

 According to the minimum mean criterion, the sum of the front and back prediction errors e, , is the minimum

\R mode parameters, OMI, ,...,Ο .Μ,

 Ν-Μ Ν-Μ

 ∑ ∑

 Μ, Κ (5)

Κ=1

; 2 :

3⁄4 (i,j ) = ' . ( ) i d::

Replacement page (Article 26) IU equations (6), (8) can form a matrix of one order (iM+1) X (M+ 1 )

 RM eight M ~ (9)

among them o,

However, R _{M is required} , and the method of linear algebra can be used to find the AR mode parameter from (9).

A two R EM ( 1 )

However, if the direct push calculation method is used, the calculation amount is large, and the Hermit and the anti-Ermite characteristics of the R _M matrix are used here to derive.

 First, add two error values em ', em

 , N-M-1

 2

If _M ,K+r 3 (12) f , NM-1

According minimum mean ^{Κ ΐ} criterion, the same treatment i.e., the e _M:

 (14)

 (15)

Replacement page (Article 26) N- 1-1

⁼ Λ ( Lj W ⁺ 1⁄2 ₊ j 1⁄2+i) ( ¹⁷⁾

Λ ⁼丄

M II (18)

Finally, the time domain update formula of eight M, is derived:

 (19)

And the AR model parameter iteration formula:

 l i m (20) In equation (19):

 (twenty one)

 M

+1 2 (D3⁄4) ⁺ ^ 1⁄2 ₊ 1 (22) Equation (20) has the same form as the Levinson iteration, from which the AR馍 parameter and the white noise spectral density No can be obtained.

The 吋M-order A model finally obtains its parameters: No=e _m ,

Persuade rate density

 M

∑ a _K exp (-j 2KfKAt ) 2 Another key issue in model parameter estimation is the choice of model order M, which is an important but unresolved problem. If the order is too low, what is the estimated estimate? The peaks of existence are blurred; the order is too high, and false details are produced. In the procedure of the present invention, the inventors have repeatedly tested, calculated, compared, and concluded that the order of the information theory criterion can be satisfied between the 11th and 19th steps, and the result of 7 A is obtained. The criterion of the information theory referred to herein is : d (M+l)

 A I = Ln©M

 N

1 "5 replacement page (rule 26) According to the foregoing, the predicted mean square error e _m always decreases with the increase of the order M, and α(Μ + 1)/Ν increases with the increase of the ,, so that it is possible to obtain a minimum under a certain value. . The devaluation of this tiny point is deducted as the most orderly order.

 In the AR model parameters and white. After the density of No., it is easy to estimate the signal spectral density (PSD) of the signal using the following formula in step S22:

^S x ^(f) = Ν. / |l ₊ 3⁄4 _K exp (-j27ifk _A t) I ²

 K-1' '

Where f is the frequency (Hz) and At is the sampling interval.

 13 to 18 show the ratios of the X signal waveforms of an unadministered control experiment and the corresponding S signal curves of the respective segments obtained after the above AR model transformation. 13 is the work half density corresponding to the stage A of the subject's free breathing, and FIG. 14 is the density of the persuasion rate corresponding to the stage B of the subject's controlled position breathing, FIG. 15 corresponds to The persuasion rate of the stage C in which the subject takes a free breathing, 3⁄4 16 is the persuasion rate corresponding to the stage D of the subject taking controlled breathing, and Figure 17 is the subject. The J-force rate of the stage E of free-breathing and controlled breathing in the lying position, and Figure 18 is the stage corresponding to the free breathing and controlled call of the standing type.

I's persuaded half density.

 In step S23, each feature value is calculated based on the above-described rate 镨 density result. A conclusion report as shown in Fig. 21 and 22 is given for the doctor to use as a basis for diagnosis. The meanings of the symbols used in the tables and in the various curves are as follows:

 T——R—R interval average (ms)

VAR1 (sometimes labeled V) - R-R interval mean square error (ms ² )

P-one general persuasion rate (normalized to equal mean square error) (ms ² )

PL - low frequency power (0 · 02 - 0. 15Hz) (ms ² )

PH——High frequency power (0.15—0.40Hz) (ms ² )

 LP - low frequency center frequency (Hz)

 HF- high frequency center frequency (Hz)

 rPLH - -PL/PH low frequency advisory rate / high frequency power

 rS LH S L /SH PSD low frequency peak /PSD, 3⁄4 frequency peak

Replacement page (Article 26) PSD - Persuasion rate density

 Referring to Figures 19 and 20, the present invention also makes the segmentation rate as a Z-axis by segmentation? If density comparison chart. The excitability of the sympathetic and sympathetic nerves under various stimuli can be clearly and intuitively observed. In both figures, the first set of peaks of the curve shows the total activity of the sympathetic and sympathetic nerves, and the second set of peaks is the intensity of the sympathetic nerves when excited alone. This provides the doctor with a direct report and report on the patient's autonomic sensational state and the sympathetic and sympathetic interactions. The above various processing devices and processes can be performed by a computer software program, and in the present invention, an application software that is particularly suitable for use in the plant neuron stimulation test system of the present invention is provided.

 In order to solve the persuasiveness of the human plant neuron system, the present invention compares the following four experimental results:

 Experiment〗: Control experiment. It is a situation in which no external medicine is applied to the human body, that is, it does not disturb the plant.

 Experiment 2: Injecting atropine to stimulate the excitability of the sympathetic system: Experiment 3: Taking propranolol to stimulate the sympathetic nervous system

^Sex:

Experiment 4: Simultaneous administration of atropine and propranoline, and sympathetic and sympathetic sensation through the sputum.

Table 1 shows the statistical results of the effects of postural motion and drug on R-R interval variability in 20 trials. The meanings of the alphabetic symbols used here and / in the continuation table are the same as those mentioned above.

Table 1 State T(ms) V t ² ) LF(Hz )

 1. Not administered:

Ε. Flat (m) 891 3992 30. 0 0. 099

 (sd) 123 2431 10. 6 0. 019

F. Standing (m) 727 3400 "" 46. 4 ¹ 0. 083

 (srf) 96 1506 11. 9 0. 015

 2. Atropine:

Ε. Flat (") 937 ' 5662 ' 28. 3 0. 094

 Csd) 145 3496 14. 0 0. 020

 F. Standing Cm ) 644 · ' · '2926 ' 37. 7 0. 084

 (sd) 114 1841 16. 7 0. 021

 3. Propranolol:

 E. Flat (m) 955 * 5417 35. r 0. 103

 (sd) 133 3582 10. 7 0. 017

 F. Standing (") 828 ' ' · • 3794 ' 43. 5 ' 0. 084

 (s«/) 105 2445 9. 7 0. 016

 4. Above 2 and 3

R 2296 of the medicine is given at the same time -

E. "f bedroom (m) 1026 · · 5817 · 32.2 0.094

 124 2791 13.1 0.022

F. Standing On) 795' • 3515 37.2· 0.090

 126 2326 11.4 0.022 Table 1 (continued) Status HF(Hz) PL/PH SL/SH

Not administered,

 E. Flat (ffi) 27.0 0.280 1.388 1.535

 11.9 0.062 0.987 1.463

P. Standing On) 11.1 0.259 • ' 4.999 ' '9.862

 5.3 0.065 2.220 6.407

2. Atropine:

 E. Flat lay On) 20.6" 0.275 1.986· 3.664·

 (« ) 13.4 0.065 1.587 5.272

F. 3⁄4立 On) 7.3· ' 0.248 • * 5.702 ' 12.336

(sd) 3.1 0.066 2.825 10.036

3. Propranolol:

 E. Flat lay On) 22.7 0.274 1.970 1.738

(srf) 9.9 0.066 1.352 1.668

F. Standing (#n) 11.7 0.253 • ' 4.190 ' 8.149

 (id) 4.6 0.070 1.593 9.262

4. Above 2 and 3

7 -- The medicine is given at the same time:

 E. Flat lay On) 21.6 0.277 1.897

 ( ) 11.2 0.074 1.249

 F. Standing (m) ' " 10.3 0.241 * " ' 4.365 • ' "7.512·

 (sd) 5.1 0.063 2.175 5.395 where the asterisk indicated before the data indicates the comparison of E (free breathing plus controlled breathing zone in supine position) and F (free breathing plus controlled breathing segment in standing position) in each state (ie, the statistics of body position changes); the asterisks after the data indicate the status and control status of the second (administration of atropine), the third (administration of heart) and the fourth (both atropine and heart) Comparison of (no drug administration status) (ie, statistics of drug effects). One of the asterisks "shows a statistical value of P < 0.05; two asterisks "* * " indicate P < 0.01, and three asterisks · ' * X ' " indicate P < 0.001, which indicate a significant difference in intensity.

 First look at the impact of rest on the heart rate spectrum:

It can be seen from the statistical results in Table 1. With this method, when the human body is from the lying position to the standing position, the changes in the heart rate can be clearly detected. In Control Experiment 1 (not used), there are two main frequency components in the supine position: low frequency (~0.099H/) 30.0 ± in. 6 %, high frequency (~0.280 Hz) 27.0 ± 11.9% _; PL/ PH is 丄.4±1. In the standing position, low frequency (~0.083Hz) 46.4±11.9%, high frequency (0.259Ηζ) 11.] ±5.3%: PL/PH is 5.0±2.2. It can be seen that when the normal person from the lying position to the standing position, it represents the sympathetic and paying, and the low-frequency component of the nerve interaction increases, which means that the high-frequency component of the sympathetic nerve alone is reduced. From the lying position to the standing position, the sympathetic nerve activity increases, and the sympathetic nerve activity is relatively weakened. From the R-K interval, the sympathetic nerve is also reduced, that is, the heart rate is increased _; statistically, the P values of several changes are less than 0.001, indicating that there is a significant difference. In addition, in the other three groups of medication In the case, from the lying position to the standing position, the R-R mean (T), PL, PH, PL/PH, SL/SH have significant differences, and the change is different from the control experiment, which gives One suggestion is that the position of the normal person in the heart rate is more than that of the heart rate, and it can still be expressed under the general small drug disturbance. And in ', In the case of menstrual infarction and diabetes, which affect the nervous system, the low frequency component increases significantly from the lying position to the standing position.

The statistical results of the effects of respiration and drugs on R-R interval variability were obtained for 20 experiments.

 Table 2

A/C-. Free breathing rate Controlled breathing rate (0.33ffz)

State r(wis) Vims ¹ ) PL .%) LF( z)

 1. Not administered:

 A. Flat (iw) 901 3424 41- 8 0.100

 Sd) 119 1982 12.4 0.021

 887 2956 •■ 31.3 0.095

 (sd) 128 2064 12.8 0.019

 C. Standing (m) 728 3699 55.0 0.088

 (srf) 95 2108 12.9 0.026

D. Standing <m) 721 " · 2642 50.8 0.086

 (sd) 102 1371 13.3 0.016

 2. Atropine:

Λ. Lying (w) 956 ^ΔΔ 5076 42.0 0.096

 (sd) 154 3256 12.3 0.026

 B. Lying (m) 922 • · 3469 • · 31.7 0.091

(sd} 147 2596 9.7 0.021 C. Standing (m) 667 ^ΔΔ 3144 51.0 0.079 (sd) 112 2117 14.3 0.017

D. Standing On) 1690 ^Δ 47.8 0.083

 (sd) 115 1265 17.1 0.023

3.

A. Lying (ΛΙ) 961 ^Δ 5145 ^Δ 43.3 0.098 isd) 135 3823 9.2 0.017

B. Lying (m) 956 ^Δ 4061 39.2' 0.102

 136 3097 12.4 0.021

C. Standing (m) 830 ^ΛΛΔ 3943 54.0 0.090

 (sd) 110 2610 11.6 0.024

D. Standing (m) 828ΔΔΔ 2957 45.4 0.081 109 1957 11.3 0.017

4. Give both 2 and 3

 Two drugs:

 ΔΔΔ

 A. Flat lay ("!!) 1036 6031' 42.1 0.094

(sd) 115 3590 10.7 0.022

B. Lying n) 1017 _Δ 3664 34.4 0.084 isd) 133 2683 13.5 0.025

C. 3⁄4立(") 801 ^ΔΔ 3520 46.4 0.090

(irf) 125 2117 12.6 0.017

D. Standing ( _m ) 787 ^Δ 2902 37.3^ 0.086

(sd) 128 2567 13.6 0.021

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B. Lying (m) 28.2 ' · · 0· 337 2.022 1.764

 14.2 0.012 1.952 1.516

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 (sd) 4.9 0.069 2.371 6.679

D. Standing n) ' · 16.1 ' · · 0· 330 ' ' 3.614 7.605

 (sd) 7.8 0.019 2.114 7.734

4. Above 2 and 3

 The medicine is given at the same time:

 A. Flat (") 22.2 0.258 2.628 2.746

 (.sd) 11.6 0.063 2.812

B. Flat (m) ' 29.3 " " * 0.338 1.864

(srf) 11.8 0.017 0.875 2.295

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 (sd) 4.8 0.072 3.245 6.320

D. Standing (m) 13.1" ' ' 0.335 4.068 8.408

8.8 0.027 3.074 8.786 Where, the asterisk before the asterisk indicates the ratio of A/B, C/D (free breathing zone / controlled breathing zone) 4i: the triangle after the data indicates the second (administration of atropine), the first 3 (administration of Xinde Ning) Ί 4 (simultaneous administration of atropine and Xin De Ning) and the ratio of the control experiment K not administered) 4. One of the asterisks "'" indicates P < 0.05: two stars No. '·:' - "Expression I) 0.01: Three, asterisk "* -X" means P < 0.001, which indicates significant specificity. ±: -, three /?] "Δ" indicates P < 0.05: Two three if] numbers "△△" show P 0.01: Three three / number ¾ "Si ^ 'indicates P <0.001, which showed a significant specificity _n can be seen in Table 2 Effect on Heart Rate Respiratory Spectrum:

 The effect of breathing on the heart rate spectrum:

 In general, the center frequency of the high frequency component is consistent with the respiratory rate. In the experiment, the effect of 'ί free breathing and controlled breathing (20 times / min, ie 0.33 Hz) on heart rate ^ was compared. In the control experiment, the controlled breathing causes the center frequency of the high frequency component to be 0.22~

0.24 Hz moved to 0.33 Hz, which is the same as the respiratory rate. In the lying position and the standing position, both lower the low component and increase the high frequency component, thereby changing the PL/PH from 2.0±1.1

1.2±1.0 (in the lying position), 6.4±3.0 became 4.7±2.5 (in the standing position), and the P value was less than 0.05, which was statistically significant.

 This means that controlled breathing is performed to make the breathing force. Strong, stimulating and sympathetic excitement, so that the 神经.3⁄4 increased neurological activity, sympathetic nerve activity is relatively weak. This result is the same as the theoretical study.

 Let's take a look at the effects of drugs on heart rate 1

 The study of heart rate under drug conditions is to confirm the specificity of the heart rate spectrum, the reflection of the systemic activity of the 3⁄4 nerves, the sympathetic sympathetic system, and the sensitivity of this analysis method to changes in sympathetic and sympathetic activities.

 From the table] and we can see the effect of drugs on heart rate i.

 In the muscle '; main atropine 1.0-L. 5mg, in the lying position and standing position, the high frequency scores decreased significantly, from 27.0 ± 11.9 to 20.6 ± 13.4 (in the lying position), by I I 1 ± 5.3 down to 7.3 ± 3.1 (in the standing position). This result is consistent with the animal studies in foreign countries, using large doses of atropine to block the sympathetic (vagus) nerves, causing the high component to disappear or decrease the agreement. This indicates that the high frequency components in the heart rate spectrum do reflect the sympathy (vagus). The activity can be used as an indicator in the clinic.

 After oral administration of 40mg of heart, it can be seen from the table that, due to the blocking effect of Xindeing on the sympathetic god, the increase of the low frequency component is compared with that of the contrast.

The power of 1 . Less, .. in the experiment 1: 30.0 ± 10.6 becomes .16.4 ± 11.9, Xin Deing ' (3): 35. 1 ± 10.7 becomes Ί 3.5 ± 9.7. Therefore, we get such a hint: the change of heart r吋 in the position of the sympathetic nerve has a certain block, but because '' ill ', ίι· τ, most of it is metabolized, and the individual difference is large, "Statistically different Significant.

 From the following special cases, it can also be seen that the law of this heart rate is autonomous.

(plant) An effective research method for neurosurgery. For example, in the previous /L cases of electrocardiogram, No. 4, sinus tachycardia was diagnosed from its electrocardiogram, but it still belongs to the normal ECG range. In the control experiment (1), his eigenvalues and 20 statistical values are shown in Table 3 below:

 Table 3 State T(ms) V(mi' ) PL % ) LF(Hz)

 1016 4376 23. 3 0. 059

 20 cases mean 891 3992 30. 0 0. 099

 Sd 123 2431 10. 6 0. 019

 F. Standing 889 5239 40. 5 0. 065

 20 cases mean 727 3400 46. 4 0. 083

 Sd 96 1506 11. 9 0. 015 Table 3 (continued) Status HFCHz ) PL/PH SL/SH

 E. Lying 43. 5 0. 339 0. 535 0. 718

 20 cases mean 27. 0 0. 280 1. 388 1. 535

 Sd 11. 9 0. 062 0. 987 1. 463

 F. Standing 15. 9 0. 319 2. 555 4. 892

 20 cases mean 11. 1 0. 259 4. 999 9. 862

Sd 5. 3 0. 065 2. 220 6. 407 'human iilL I') 20 cases of statistical value comparison, his sympathetic activity is obviously 53⁄4 other People, but the sympathetic nerves are weak. 3⁄4 is consistent with the electrocardiogram of the county-speaking tachycardia. Thus "i is clear that this method is effective and sensitized.

Industrial applicability

 From the current research on heart rate spectrum, some clinical symptoms, such as myocardial infarction, spastic congestive heart failure, diabetes, high blood pressure, etc.; the heart rate spectrum changes far more than ^ tachycardia Therefore, through the study of heart rate ^, it provides an effective means for the classification of clinical diagnosis, the prediction of disease and the observation of curative effect, especially the evaluation of the autonomic nervous system of patients with heart disease.

 While the present invention has been described in connection with the embodiments of the present invention and the embodiments of the invention, it will be understood by those skilled in the art that various modifications and changes can be made within the spirit and scope of the invention.

Claims

Rights request

I. An autonomic nervous system persuasive test system characterized by:

 Portable ECG signal acquisition and storage device for continuously collecting and recording a subject in a number of different states for a predetermined period of time to stimulate the sympathetic nervous system and/or the sympathetic system ECG signal;

 a speech unit including a speaker for guiding the t: in the plurality of different states in coordination with the predetermined time to obtain the desired stimulation of the sympathetic nervous system and the /A sympathetic nervous system;

 a microcomputer for performing a perceptual spectrum analysis on the electrocardiographic signals in each different state stored by the ECG signal acquisition and storage device to obtain a sympathetic and sympathetic neural system Stimulating responses, and then their theory of adequacy: it further includes:

 R wave identification device for identifying the R wave in the EC signal and finding the R-R period. Outputting the R wave sequence signal:

 The segmentation and pre-processing device segments and pre-processes the R-wave sequence signal corresponding to the different states, and sends the segmented R-wave/f-letter letter in each different stimulation state.

' as an X signal;

 An autoregressive (AR) model error estimating device for finding model parameters including model parameters, white noise level, and model order M, and performing Z-change on the X signal in the time domain for frequency domain Persuasion rate estimate;

 Persuading a half-density computing device for calculating the perceptual spectrum of the segmental heart signal based on the model parameters:

 The output device is used for printing and outputting the related signals of the electrocardiogram signal and its advisory rate, analysis and comparison report.

 The system according to claim 2, wherein said ECG signal acquisition and storage further comprises an infrared emission device, said microcomputer further

'Bu infrared receiving II device is connected to complete the ECG signal farming and storage device

' ! \ f- Machine's request for ECG signal transmission.

: \ . The system according to the claims, wherein the different stimulation states are respectively Γ下em different essays · ' ^ a period of time, during this period, ± tester in a supine position and free call " and state, to stimulate the payment of sympathetic nervous system and cross-talking neural system: two 1 '> During this time, the X tester was in a supine resting position and a controlled breathing state to stimulate the tenth sensory nervous system: the third segment C, during this time, the subject Become a standing position and] by breathing state, to stimulate the sympathetic 祌 ;; four paragraphs 在, during this time, are in standing position and controlled breathing state, used to stimulate sympathetic and sympathetic. 3⁄4: The frequency of controlled breathing is higher than the frequency of free breathing.

 The system according to claim 3, characterized in that said predetermined time is 15 minutes ί ' , wherein said Λ section is 3 minutes, Β section is 4 minutes, C section is 4 minutes, and D section is !

.. , the system according to claim 1, characterized in that said R-wave identification device and said m-processing device further comprise low-pass differential devices '' and w' for extracting the first derivative of the electrocardiographic signal First-order low-pass wave-wave mounting of ECG-order derivatives of low-pass waves

,ϊι it分 'Ί为

 d(,t) = ECG(t)—ECG(t—4) and (i) = (i-l)+ci(i)-ci(i-6) ;

 · ';) "The κ wave m'l device also includes an electrocardiogram self-learning device, which is used to find out the maximum absolute value of f ( t ) after the self-learning of the 吋 电 Γ Γ 役 役 役! The value PK and two 1, R - Rπ] ίνί, , : i, the average of the R - R interval as the initial value of ίν3⁄4 between the average R-R: its 'I' κ wave Η >; The R wave recognition threshold is:

 Νίά :1 function is:

"0. 7 * // ₀ (r.) +0. 3 » (0. 7 * PK(n) ) 0. 7 * ΡΚ 5 * Ho(n) ίί„('[ + 1 ) - :

 0. 9 * ii„(n ) + 0. 1 * (0. 7 * PK'(n) 0. 7 * PK (n)>l. B *

(0. 8 ( ) + 0. 2 * (n) l (n -li>Q.5 (

KR W Other profit-making equipment, which is characterized by the above. Including W Yugen According to the result of the R-wave identification device, a device for generating an R-wave sequence signal curve of the R-R interval is outputted, and the signal graph is printed and output by the output device.

 7. Apparatus according to claim 5 wherein said system further comprises results for root R wave identification means and segmentation preprocessing means, each segment (eight, B, C, D, IE) A device for the R-wave sequence signal graph of A + B, F = C + D segment and R - E + F segment, and printed out by the C output device.

 8. The beam system according to claim 5, wherein said R wave identifying means further comprises a back detecting means, and when R - R > 1.8RRAV, R wave identification is performed again on the section, and back The threshold is:

H, (n ) = 0. 5 * H ₀ (n )

 9. System according to claims 1 and 5, characterized in that said self-return (AR) model estimation means and persuasion spectrum calculation means perform the analysis using the following transformation:

X _n = W _n * h„

 Where h, is the impulse response; * is the convolution, W, and is white. Sang Sheng: h(n)

H(Z) S _X (Z) then S _x ( ) The expression of the Ai? model is:

 M

 X(n) = - ∑ a (K) X (n-K) + W (n)

 K=l

 Change to the frequency domain:

 X(Z) M

 = H (Z) = 1 / ( 1+ ∑ a (K)

W (Z) K=l where the model parameter a(fe) found is:

The white singer found 1 good density is:

Replacement page (Article 26) N ₀ = e _M :

Ί3⁄4 type order is based on information theory

The M value at the very small point is taken as the optimal order value, and the order M is selected between 11 and 19;

 The persuasion rate calculation of the persuasion semi-linguistic density computing device is performed according to the following formula:

 M

S _χ (f ) = o^t / 1+ ∑ a _MK exp Η2 ί3⁄4Α _& ) | ²

 K=l

Where f is the frequency (3⁄4) and Z t is the sampling interval.

 10. The system according to claims 7 and 9, characterized in that said system further comprises a transform estimation result according to an autoregressive (AR) model, respectively corresponding to each segment and all R wave sequence signals and their perceptual rate spectrum analysis signals Contrast graph.

 11. A system according to claim 10, wherein each of said devices included in said microcomputer is implemented by software.

 12. A type of inspection! The method of persuasion of autonomic nervous system includes steps:

 Collecting and storing the ECG signal E G at the same time as the sympathetic nervous system and/or the 3⁄4 neural crests of the subject during a predetermined time period.

 b. R wave identification of the ECG, find the R-R interval, and give the R wave sequence signal: c According to the difference in the stimulation of the sympathetic nervous system and/or the sympathetic sympathy in step a, Segmenting the R wave sequence signal as an X signal:

 d. Using the autoregressive (AR) model, the Z-transformation of the X signal is performed.

 f. Calculate the persuasion rate of ECG signals:

 g. Find the eigenvalues to give a quantitative analysis of the sympathetic nervous system and the sympathetic nerves.

 13. The method according to claim 12, characterized in that said step 3⁄4a is stunned; the person confuses the nervous system "paying sympathetic 祌 进行 进行 \ 激 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7 -7

I. The subject is in a supine position and free breathing state eight:

Replacement page (Article 26) Lii. The person becomes standing ί and is in a state of free breathing C: and

 .v. ±Tester i A crit who is standing and crying and performing controlled breathing D:

 Where ± the frequency of breathing is higher than the frequency of free breathing „

 The method of claim 3, wherein the predetermined time is I 5 minutes, wherein state A (i) lasts for 3 minutes: state B (ii) continues for minutes; Segment) continuous minute hand: and state D (iv segment) lasts 4 minutes

A method according to claim 14 wherein said stimulating of said anterior and gynecological stimuli comprises administering a specific drug to a subject.

K). The method according to claim 15, characterized in that said specific substance is Ajj'a (Atropine) and/or Propranolol _c

 17. The method of claim 4, wherein the 3⁄4 is performed using a έ! criterion, further comprising the steps of:

h i - publication formula

 d(t) -ECG(t)-HCG(t-4)

 ^The first derivative of the heart signal,

 B2. Using formula

 i'(t) -.: i'(t - 1 )+d(t) - d(t- )

 The heartbeat number ό3⁄4 first derivative is low pass filtered;

 !)>. Perform ECG self-learning for a period of time, then 吋 f ( t ) find the maximum absolute i

PK 7 two i, R-R between 13⁄4, the average of the latter as the average R-R between i3⁄4 RA V ^

 The j position, where R wave 3⁄4') threshold is:

 11„(( ) - 0.7:': P

 b l. ;fi /f- Λ f- f ( t ) , find P (n)―, , Ho ( n ), and indeed R wave and R

 Αά') ii i . The wide-value adaptive recursive function is:

* Ηο(η)

Ι.Γ RK.W ( ,) -HR >0. S KKAV(n)

18. The method according to claim 17, wherein said step comprises: a step of re-recognizing the R wave, wherein the condition is that when RR > I. HRRAV, the check is performed, Θ The threshold is:

H,(n) = 0.5 * H ₀ (n) _o

 1 . The method according to claims 12 and 1.7 million, characterized in that said d and 1' are transformed by ι analysis ∑ K is one by one:

 1

Χ _Μ =-ν _π h„

Where: h„ is the impulse response, * is the volume ^\, W„ is the white noise:

X two n 1

If Z

 2

 No\H ( " ') where the function of the AW model is n

lK nK ^+W 】

i - Z for ) s:

 i (z)

 H(Z) =

 w (z)

 — K

 aK

Among them, the number of buildings is 3⁄4ί;

· "- α _Μ . I, 0.2 = Ow .;> ,... - OM · M:

Α degree No - C _M ;

 Wherein the ^ d 3⁄4 ί 吋 3 3⁄4 order M performs 3⁄4 u ^ , ie Mn

 3·

Λ IC Yi ^{= +}

 Iv

- 31 - Replacement page (Article 26) Taking the extremely small M value as the last order; the M value is between 11 and j9 - 20. The method according to claim 18, characterized in that the step f is used to estimate the electrical signal rate The formula for the density of words is

S _x (f) : o^t / a _MK exp (-j27XfK^t)

Where f is the frequency (Hz) and At is the sampling interval.

 21. A method according to any of claims 12-16, further comprising the step of comparing the test results of said different stimuli by a plurality of said stimuli and giving a comparative result.

 22. A method according to claims 12 and 20 wherein said & step is performed by software.

 23. The method of claim 21 wherein said each of said '3' is completed by software.

― 32

Replacement page (Article 26)