HK1256673A1 - Stimulative electrotherapy using autonomic nervous system control - Google Patents

Description

Stimulation electrotherapy using autonomic nervous system control

The present case is the divisional application of the following patent applications:

application No.: 201380077463.1, respectively;

application date: 6 months and 13 days 2013;

the invention name is as follows: stimulation electrotherapy using autonomic nervous system control

Cross Reference to Related Applications

This application relates to U.S. patent No.7,092,849 entitled "EXTRACTING CAUSAL information from CHAOTIC time series" entitled "EXTRACTING CAUSAL information from a chaos time series," 8/15/2006, which is incorporated herein by reference in its entirety. This patent application relates to concurrently filed applications: U.S. patent application attorney docket No.89562-000400US-874044 entitled "METHOD AND APPARATUS FOR AUTONOMIC NERVOUS system sensitivity TESTING", U.S. patent application attorney docket No.89562-000500US-874022 entitled "COMPUTER IMPLEMENTED TRAINING OF a PROCEDURE" AND attorney docket No.89562-001000US-876815 entitled "METHOD AND APPARATUS FOR stimulating ELECTROTHERAPY", the entire contents OF all OF which are incorporated herein by reference.

Technical Field

In general, the present invention relates to a method and apparatus for extracting information from chaotic time-series data generated based on a patient's autonomic nervous system and using the information to enhance therapy delivered to the patient. More precisely, the invention relates to a method and a device for analyzing the state of a patient before and after a treatment.

Background

The Autonomic Nervous System (ANS), which has sympathetic and parasympathetic subsystems, governs involuntary movements of the myocardium and of each of the organs of the body. The ANS is not directly accessible to autonomous control. Instead, it operates in an autonomous manner on the basis of autonomous reflexes and central control. One of its main functions is to maintain homeostasis in the body. The ANS also plays an adaptive role in the interaction of the organism with its surroundings.

Heart rate fluctuation measurements have been shown to be a powerful means of estimating the effect of the ANS on the cardiac system. Thus, heart rate fluctuations are a powerful indicator of ANS status and can be used to assess the status of physiological conditions associated with the ANS, such as chronic pain.

In many diseases, the sympathetic and/or parasympathetic subsystems of the ANS are affected, leading to autonomic dysfunction. Therefore, it is important to have a reliable and representative measurement of the activity and status of the ANS.

Three main categories of methods are used to obtain ANS related information from heart rate fluctuations: spectral analysis (also known as temporal analysis), statistics and calculations of correlation dimensions (or any correlation dimensions). These methods do not give easily interpretable results. Furthermore, they lack reliability and are often mathematically unsuitable for the applications they consider.

Without reliable and representative measurements of the ANS, only the therapeutic effect of a particular condition can be measured subjectively. For example, to measure a patient's pain, the patient may be asked to estimate their pain level on a 1-10 point basis.

In general, the present invention relates to a method and apparatus for extracting causal information from a chaotic time series. More precisely, the invention relates to a method and a device for analysing the state of a first system from a time-varying signal representing a chaotic series of time intervals between quasi-periodic events generated by a second system dominated by the first system. In a typical, but not exclusive, application of the invention, the first system is the Autonomic Nervous System (ANS) and the second system is the cardiac system.

Heart rate fluctuation measurement (HRV) has been shown to be a powerful means of estimating the effect of the ANS on the cardiac system. In fact, the ANS has sympathetic and parasympathetic subsystems, governing the involuntary movements of the myocardium and of every organ of the body.

The ANS is not directly accessible to autonomous control. Instead, it operates in an autonomous manner on the basis of autonomous reflexes and central control. One of its main functions is to maintain homeostasis in the body. The ANS also plays an adaptive role in the interaction of the organism with its surroundings.

In many diseases, the sympathetic and/or parasympathetic subsystems of the ANS are affected, resulting in autonomic imbalance. Therefore, it is important to have a reliable and representative measurement of the activity and status of the ANS.

Three main categories of methods are used to obtain ANS related information from heart rate fluctuations: spectral analysis (also known as temporal analysis), statistics and calculations of correlation dimensions (or any correlation dimensions). These methods do not give easily interpretable results. Furthermore, they lack reliability and are mathematically unsuitable for the applications they consider.

Disclosure of Invention

One inventive aspect is a method of analyzing an autonomic dysfunction state of an autonomic nervous system. The method includes measuring an autonomic nervous system condition, and calculating a root of the sum. The one or more values are equal to the sum of the differences raised to the exponent, and the differences are each equal to the difference of the first exponent value and the second exponent value. The first and second exponent values are each calculated based on the autonomic nervous system condition. The method also includes displaying, by a display unit, a representation of the computed root.

Another inventive aspect is a system for analyzing an autonomic dysfunction status of an autonomic nervous system. The system includes means for measuring an autonomic nervous system condition, and means for calculating a root of a sum of values, wherein one or more values are equal to the sum of differences raised to an index. The difference values are each equal to a difference of a first exponent value and a second exponent value, and the first exponent value and the second exponent value are each calculated based on the autonomic nervous system condition. The system further comprises means for displaying, by a display unit, a representation of the computed root.

The object of the present invention is to remedy the above-mentioned drawbacks of the prior art and to achieve this effect propose a method for analyzing the state of a first system from a time-varying signal representing a chaotic series of time intervals between quasi-periodic events generated by a second system dominated by the first system, comprising the steps of: extracting envelope information from the time-varying signal, constructing a phase space for the time-varying signal, extracting relative position information about points corresponding to the time-varying signal in the phase space, combining the envelope information and the position information, providing information about the first system state based on this combination.

Thus, the present invention exploits the fractal geometry of the time-varying signal and combines an envelope arithmetic scheme and an estimate of the dispersion of points in reconstructed phase space. The present inventors have found that such a combination is able to emphasize significant changes in the chaotic sequence of the time intervals when resolving insignificant changes, thereby providing accurate information about the state of the first modeled system.

A more robust and reactive response to changes in the first system state may in the present invention be obtained by calculating two envelopes of a sequence of time intervals, a first upper envelope calculated in a time sequential direction and a second upper envelope calculated in a time sequential opposite direction.

The invention also makes it possible to distinguish the sympathetic and parasympathetic subsystems of the ANS by two calculation means defined in the appended claims, to describe the time-of-day state of each of these subsystems.

Further advantageous features of the method according to the invention are defined in the appended dependent claims.

The invention also relates to a computer program and a device for performing the above mentioned method.

The present invention provides a method for analysing the state of a first system from a time-varying signal representing a chaotic series of time intervals between quasi-periodic events generated by a second system dominated by the first system, the method comprising the steps of:

a) extracting envelope information from the time-varying signal,

b) constructing a phase space for the time-varying signal,

c) extracting relative position information about a point corresponding to the time-varying signal in the phase space,

d) combining the envelope information and the location information,

e) based on this combination, information about the first system state is provided.

Preferably, said steps q) to e) are repeated each time a new time interval occurs in said time varying signal.

Preferably, step a) comprises calculating a first upper envelope of said time-varying signal in a direction of said time sequence and calculating a second upper envelope of said time-varying signal in a direction opposite to said time sequence.

Preferably, step b) comprises constructing a vector on the basis of values taken by the time-varying signal using the determined dimension and the determined time interval for the phase space.

Preferably, wherein said step c) comprises projecting said points corresponding to said time-varying signals in said phase space onto a lower dimensional space on which an order relationship can be established, and calculating distances between said projected points.

Preferably, wherein said step c) comprises projecting said points corresponding to said time-varying signals in said phase space onto a straight line, said straight line minimizing the average distance between said points and said straight line, and calculating the distance between said projected points.

Preferably, wherein said step c) further comprises identifying a positive distance and a negative distance among the calculated distances.

Preferably, wherein an index is calculated based on the positive distance or the negative distance, the index representing a probability that a state change of the first system occurs at the next event.

Preferably, wherein said step e) comprises providing said information on said first system state to a display unit.

Preferably, wherein the time-varying signal is an original signal.

Preferably, wherein the first system is the autonomic nervous system.

Preferably, wherein the second system is a cardiac system, the quasi-periodic events are R-waves of an electrocardiogram, and the chaotic series of time intervals are RR-intervals derived from the electrocardiogram. 13. The method of claim 11 or 12, wherein the step d) comprises performing a first combining operation that provides first data representative of the parasympathetic subsystem of the autonomic nervous system and performing a second combining operation that provides second data representative of the sympathetic subsystem of the autonomic nervous system.

Preferably, wherein said step d) includes performing a first combining operation that provides first data and performing a second combining operation that provides second data, a point-by-point subtraction from any of these first and second data otherwise representing a balance between said parasympathetic and sympathetic subsystems of said ANS.

Preferably, the method further comprises calculating a first index representative of a complexity index of a first curve defined by the first data and/or calculating a second index representative of a complexity index of a second curve defined by the second data.

Preferably, wherein said first system is an autonomic nervous system, said step a) comprises calculating a first upper envelope ForwHull of said time-varying signal in the direction of said temporal sequence and calculating a second upper envelope BackwHull of said time-varying signal in the direction opposite to said temporal sequence, said step c) comprises projecting said points corresponding to said time-varying signal in said phase space onto a lower dimension space on which an order relation can be established, calculating distances between said projected points and said identified positive and negative distances among said calculated distances, and said step d) comprises performing the following two combined operations:

Coeffinc ₁＝B+(4–4A–5B+4AB)·Cinc–B·Cdec

Coeffdec ₁＝B–B·Cinc+(4A–4AB–B)·Cdec

and

wherein A and B are predetermined constants, norm coeff is a normalized coefficient, and Cinc and Cdec are vectors representing positive and negative distances, respectively, and wherein the information provided in step e) comprises a vector ANS igram₁And ANSegaram₂。

Preferably, said step d) further comprises calculating the following two indices:

wherein Floor refers to the integer part ifThis parameter is negative, Floor returns to zero, ANSlength₁And ANSchlegth₂Respectively mean by the vector ANSegaram₁The length of the first curve defined and the sum of the vectors ANSegaram₂Length of the second curve, range₁Means the difference between the last value and the first value of said first curve, range₂Refers to the difference between the last value and the first value of the second curve, and N refers to being equal to the vector ANS igram₁And ANSegaram₂A predetermined number of dimensions.

The present invention also provides a computer program for analysing the state of a first system from a time-varying signal representing a chaotic series of time intervals between quasi-periodic events generated by a second system dominated by the first system when a processor is implanted, the computer program comprising instruction code for performing the method of any one of the preceding aspects.

The present invention also provides an apparatus for analysing the state of a first system from a time-varying signal representing a chaotic series of time intervals between quasi-periodic events generated by a second system dominated by the first system, the apparatus comprising processing means programmed to perform a method according to any one of the preceding aspects.

The present invention also provides an apparatus for analysing the state of a first system from a time-varying signal representing a chaotic series of time intervals between quasi-periodic events generated by a second system dominated by the first system, the apparatus comprising:

means for extracting envelope information from the time-varying signal,

means for constructing a phase space for the time-varying signal,

means for extracting relative position information about points corresponding to the time-varying signal in the phase space,

means for combining said envelope information and said position information, and

means for providing information about the first system state based on this combination.

Preferably, the apparatus further comprises means for repeating the envelope information extraction, phase space construction, region information extraction, information merging and information providing steps each time a new time interval appears in the time-varying signal.

Preferably, the means for extracting envelope information comprises means for calculating a first upper envelope of the time-varying signal in the direction of the time sequence, and means for calculating a second upper envelope of the time-varying signal in the direction opposite to the time sequence.

Preferably, the means for constructing the phase space comprises means for constructing a vector based on values taken from the time-varying signal using the determined dimension and the determined time interval for the phase space.

Preferably, the means for extracting location information comprises means for projecting the points corresponding to the time-varying signals in the phase space onto a lower dimensional space on which an order relationship can be established, and means for calculating distances between the projected points.

Preferably, the means for extracting location information comprises means for projecting the points corresponding to the time-varying signals in the phase space onto a straight line that minimizes the average distance between the points and the straight line, and means for calculating the distance between the projected points.

Preferably, the means for extracting the location information further comprises means for identifying a positive distance and a negative distance among the calculated distances.

Preferably, the method further comprises calculating an index based on the positive or negative distance, the index representing a probability that a state change of the first system occurs at the next event.

Preferably, the means for providing information comprises display means for displaying the information on the first system state.

Preferably, wherein the time-varying signal is an original signal.

Preferably, wherein the first system is the autonomic nervous system.

Preferably, wherein the second system is a cardiac system, the quasi-periodic events are R-waves of an electrocardiogram, and the chaotic series of time intervals are RR-intervals derived from the electrocardiogram. 32. The apparatus of claim 30 or 31, wherein the blending means comprises means for performing a first combining operation that provides first data representative of the parasympathetic subsystem of the autonomic nervous system and means for performing a second combining operation that provides second data representative of the sympathetic subsystem of the autonomic nervous system.

Preferably, wherein said blending means comprises means for performing a first combining operation that provides first data and means for performing a second combining operation that provides second data, a point-by-point subtraction of any of these first and second data from the other of these first and second data representing the balance between said parasympathetic and sympathetic subsystems of said ANS.

Preferably, means are further included for calculating a first index representative of the complexity index of a first curve defined by said first data, and/or means for calculating a second index representative of the complexity index of a second curve defined by said second data.

Preferably, wherein the first system is the autonomic nervous system, the envelope information extracting means includes means for calculating a first upper envelope ForwHull of the time-varying signal in the direction of the time sequence, and means for calculating a second upper envelope BackwHull of the time-varying signal in the direction opposite to the time sequence, the location information extracting means includes means for projecting the points corresponding to the time-varying signal in the phase space onto a lower-dimension space on which an order relationship can be established, means for calculating the projected points, and means for calculating a distance between the identified positive and negative distances among the calculated distances, and the combining means includes means for performing two combining operations:

₁Coeffinc＝B+(4–4A–5B+4AB)·Cinc–B·Cdec

Coeffdec ₁＝B–B·Cinc+(4A–4AB–B)·Cdec

and

wherein A and B are predetermined constants, norm coeff is a normalized coefficient, and Cinc and Cdec are vectors representing positive and negative distances, respectively, and wherein the information provided in step e) comprises a vector ANS igram₁And ANSegaram₂。

Preferably, the blending device further comprises means for calculating the following two indices:

where Floor refers to the integer part, if the parameter is negative, Floor returns to zero, ANSlength₁And ANSchlegth₂Respectively mean by the vector ANSegaram₁The length of the first curve defined and the sum of the vectors ANSegaram₂Length of the second curve, range₁Means the difference between the last value and the first value of said first curve, range₂Refers to the difference between the last value and the first value of the second curve, and N refers to being equal to the vector ANS igram₁And ANSegaram₂A predetermined number of dimensions.

Preferably, means for acquiring said quasi-periodic events in respect of the patient are also included.

Drawings

Fig. 1 shows a flow chart of a method of caring for a patient.

Fig. 2 shows a flow diagram of a method of calculating autonomic nerve dysfunction, which may be used in the method of fig. 1.

Fig. 3 shows a flow chart of a method of treating a patient, which may be used in the method of fig. 1.

Fig. 4 is a graph for determining parameter values for use in the method of fig. 3 based on measured characteristics of the patient ANS.

Fig. 5 shows an example of an ordered set of differences.

FIG. 6 is a flow chart of a method according to the present invention;

figures 7 and 8 show, respectively, as a general example, how two different envelopes are obtained from a time-varying signal, one envelope being determined in a time-sequential direction and the other envelope being determined in a direction opposite to the time-sequential direction;

fig. 9 schematically shows an example of a phase space obtained in a method according to the invention;

FIG. 10 shows a time-varying signal representing RR intervals derived from an electrocardiogram;

fig. 11 shows an overlay of the curves obtained by the method according to the invention, and each representing a temporal state of the parasympathetic subsystem considered as ANS;

FIG. 12 shows the temporal variation of two indexes obtained by the method according to the invention;

FIG. 13 shows the temporal variation of another index obtained by the method according to the invention;

fig. 14 is a flow chart of a system implemented in a method according to the invention.

FIG. 15 is a flow chart of a method of generating a balanced trajectory according to another embodiment of the present invention.

FIG. 16 is a diagram of a state table according to one embodiment of the invention.

FIG. 17 is a graph of a rotation and normalization of the graph of FIG. 16 according to another embodiment of the invention.

Fig. 18a and 18b show two equilibrium trajectories for two consecutive time intervals according to an embodiment of the invention.

Fig. 19 shows two equilibrium trajectories connecting fig. 18a and 18 b.

FIG. 20 illustrates a graph of rotating and normalizing the mean coordinate of the state table and the norm of the point over a plurality of time intervals according to an embodiment of the invention.

Fig. 21 shows a graph of the static equilibrium point in the rotated and normalized coordinate system.

Fig. 22 shows a graph of the time variation of two indices obtained by a method according to another embodiment of the invention.

FIG. 23 is a flow diagram of a method of determining coupling between indexes according to an embodiment of the present invention.

Fig. 24 shows a flow diagram of a filter for filtering an input signal according to an embodiment of the invention.

DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION

Specific embodiments of the present invention are described below with reference to the accompanying drawings.

Various details are shown herein as they relate to certain embodiments. However, the invention may also be practiced in ways other than those described herein. Modifications to the discussed embodiments may be made by persons skilled in the art without departing from the invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein.

The specific biological event produced by the patient is governed by the ANS of the patient. Thus, the ANS status of a patient can be determined by appropriate analysis of data representative of a particular event. Further, since the ANS condition of a patient may be related to one or more conditions for which the patient may seek treatment, analysis of data representative of a biological event may be used as a quantitative measure of the one or more conditions.

For example, the biological event may be related to the patient's cardiac system. Thus, data representative of the patient's heart rate or heart rate fluctuations may be used to determine a measure of pain experienced by the patient. Alternatively or optionally, the biological event may be related to respiratory or brain activity of the patient.

In some embodiments, the condition associated with the biological event comprises one or more of chronic pain, anxiety, depression, and sleep problems.

Fig. 1 shows a flow chart of a method 100 of caring for a patient. The patient may seek treatment for one or more conditions measurable by analysis of data relating to biological events governed by the patient ANS. For example, the patient may experience chronic pain.

According to method 100, autonomic dysfunction and sympathetic vagal balance are determined prior to treatment. In addition, autonomic dysfunction and sympathetic vagal balance were determined again after treatment. The difference between the autonomic dysfunction and the sympathetically vagal balance of the patient before and after treatment can be used as an indication of the efficacy of the treatment.

In step 110, autonomic dysfunction is determined.

In some embodiments, one or more of the methods and/or systems described in appendix 1 are used to determine autonomic nerve dysfunction. For example, using the apparatus described in appendix 1, data representative of a patient-generated biological event may be recorded, as dictated by the ANS of the patient. Furthermore, one or more of the data analysis methods and systems described in appendix 1 may be used to calculate autonomic dysfunction in a patient based on the recorded biological event data.

In some embodiments, methods and/or systems not described in appendix 1 may also be used to calculate autonomic nerve dysfunction in a patient. For example, the method of determining autonomic nerve dysfunction in a patient described below with reference to FIG. 2 may be used.

In step 120, sympathetic vagal balance is determined.

In some embodiments, one or more of the methods and/or systems described in appendix 1 may be used to determine sympathetic vagal balance. For example, data representative of a patient-generated biological event, as governed by the ANS of the patient, may be recorded using the apparatus and/or methods described in appendix 1. In addition, one or more of the data analysis methods and systems described in appendix 1 may be used to calculate the sympathetic vagal balance of the patient based on the recorded biological event data. In some embodiments, the recorded biological event data used to calculate autonomic dysfunction in a patient may also be used to calculate the sympathetic vagal balance of the patient.

In some embodiments, a balance curve is calculated using one or more of the methods and systems described in appendix 1, and the sympathetic vagal balance is determined based on one or more parameters extracted from the balance curve. For example, one or more of the minimum, maximum, midpoint, mean, and median of the abscissa or ordinate values may be used as sympathetic vagal balance. Alternatively or additionally, the occurrence of circulation or maintenance of long-flat switches may be used as sympathetic vagal balance.

In some embodiments, methods and/or systems not described in appendix 1 may also be used to calculate the sympathetic vagal balance of the patient.

In step 130, the patient is treated. In some embodiments, the treatment includes providing electrical stimulation to a selected site on the patient's body. Optionally, one or more treatments may be administered to the patient. For example, physical therapy, other forms of stimulation, manipulation, and pain medications, such as opiates.

In some embodiments, the method of treating a patient described below with reference to fig. 3 may be used.

After treatment, the sympathetic vagal balance of the patient is again determined in step 140. The sympathetic vagal balance determined after treatment may be compared to the sympathetic vagal balance determined before treatment. This comparison can be used to assess the efficacy of the treatment.

In some embodiments, in step 140, the sympathetic vagal balance of the patient is determined using a system and method substantially the same as the system and method used in step 120 to determine the sympathetic vagal balance of the patient prior to treatment. In some embodiments, the method and system for determining the sympathetic vagal balance of the patient after treatment in step 140 is different from the method and system for determining the sympathetic vagal balance of the patient before treatment in step 120.

After treatment, the patient is again determined for autonomic dysfunction in step 150. The autonomic dysfunction determined after treatment can be compared to the autonomic dysfunction determined prior to treatment. This comparison can be used to assess the efficacy of the treatment.

In some embodiments, in step 150, autonomic nervous dysfunction of the patient is determined using a system and method substantially the same as the system and method used in step 110 to determine autonomic nervous dysfunction of the patient prior to treatment. In some embodiments, the method and system for determining autonomic nerve dysfunction in the post-treatment patient in step 150 is different from the method and system for determining autonomic nerve dysfunction in the pre-treatment patient in step 110.

In some embodiments, the method of fig. 1 is repeated. For example, the method of fig. 1 may be used in a first treatment phase. As part of the first treatment phase, the efficacy of the first treatment can be assessed based on a comparison of the autonomic dysfunction and the sympathetic vagus nerve balance values before and after the first treatment. Also, the method of fig. 1 may be used in a second treatment phase. Similar to the first treatment phase, as part of the second treatment phase, the efficacy of the second treatment can be assessed based on a comparison of autonomic dysfunction and sympathetic vagus nerve balance values prior to and after the second treatment. In some embodiments, the second treatment phase comprises about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 minutes after the first treatment phase, about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 hours after the first treatment phase, about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 days after the first treatment phase, about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 weeks after the first treatment phase, about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 months after the first treatment phase or about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 years after the first treatment phase.

In addition, the autonomic dysfunction and sympathetic vagal balance values determined as part of the second treatment phase are compared to the autonomic dysfunction and sympathetic vagal balance values determined as part of the second treatment phase. The results of such comparisons may indicate the efficacy of the treatment through multiple treatment stages.

Fig. 2 shows a flow diagram of a method 200 of calculating autonomic nerve dysfunction in a patient. For example, the method 200 may be used in the method 100 shown in FIG. 1. In some embodiments, the method 200 shown in FIG. 2 is performed separately and differently than the method 100 shown in FIG. 1. Further, the method 100 shown in fig. 1 may use a method of calculating autonomic dysfunction that is different from the method 200 shown in fig. 2.

According to the method 200, autonomic dysfunction is calculated based on data recorded representative of biological events innervated by the patient ANS.

In step 210, the first index ANSindexl and the second index ANSindex2 are both calculated according to the method and system described in appendix 1. In alternative embodiments, different methods and systems may be used to calculate ANSindexl and ANSindex 2. In some embodiments, ANSindexl and ANSindex2 may be calculated in response to each of a plurality of consecutive biological events. For example, ANSindex and ANSindex2 are calculated in response to each of a number of heartbeats, e.g., measured with an electrocardiogram. In some embodiments, ANSindexl and ANSindex2 may be calculated in response to each of a series of 400 heartbeats. In some embodiments, ANSindexl and ANSindex2 may be calculated in response to each of a series of 512 heartbeats. In some embodiments, data from a certain number of heartbeats, for example 60, may be used for calibration, or for other purposes. In some embodiments, the heartbeat is continuous.

In step 220, a series of Difference Values (DV) are calculated. Each difference of the series is calculated based on ANSindexl and ANSindex2 values calculated in response to consecutive biological events as described with reference to step 210. For example, in step 210, ANSindexl values and ANSindex2 values are calculated for each of the consecutive biological events, and in step 220, the difference between the ANSindexl values and ANSindex2 values for each consecutive biological event is calculated. The difference values calculated for all biological events constitute a difference set.

For example, in some embodiments,

DV_i＝ANSindex2_i–ANSindexl_i，

where i is an index (index) indicating a data point.

In step 230, the set of Difference Values (DV) is sorted. For example, the difference sets may be ordered from a minimum difference to a maximum difference. In other embodiments, the second difference values may be ordered from the largest difference value to the smallest difference value.

Fig. 5 shows an example of an ordered set of differences. The difference values are depicted in rank order with the smaller difference values depicted to the left of the larger difference values, and with the distance to the horizontal axis corresponding to each sorted difference value. Fig. 5 also shows a linear fit baseline.

In step 240, the sorted differences are assigned to different intervals. For example, four intervals are defined. Indications A, B and C identify boundaries between adjacent bins of the example set of difference values shown in FIG. 5. In this example, indications A, B and C are aligned with differences 67, 167, and 421, respectively. In some embodiments, the interval is determined based on a linear or second derivative of the sorted difference. For example, each interval may include a difference value corresponding to a point at which the second derivative is less than a threshold. In some embodiments, the passable interval may be determined by an intermediate portion linear or cubic fit and/or an alternative exchange of distances to linear or cubic fits within different thresholds.

Each interval may correspond to a particular characteristic of the patient ANS. For example, the first and last intervals, the upper and lower limits may correspond to a state of depth change and a surface transient change, respectively, of autonomic neural function, while the quasi-linear middle interval may indicate a fused persistent state of autonomic homeostasis.

In step 250, the information represented in the sorted difference set is used to calculate the autonomic nerve dysfunction of the patient. Various mathematical methods may be used.

For example, the value Vr may be determined for each of the four intervals. In some embodiments, the value of each interval is determined by summing the differences of the intervals. Alternatively, the value of each interval is determined by summing the differences of the intervals raised to the exponent. For example, the index may be 2, 3, 4, 5, or other values. In some embodiments, the exponent may not be an integer, may be an irrational number, and/or may be a negative number. As one non-limiting example, the value of each of the intervals raised to the fourth power may be determined by summing the differences of the intervals.

For example, in some embodiments,

where i is a summation index representing the number of data points in the interval, n is the number of data points in the interval, and r represents the interval.

In some embodiments, the values for the interval are multiplied by a coefficient (c) specific to the region to which they relate, respectively. For example, the value associated with the first interval may be multiplied by a coefficient equal to-8.2045, the value associated with the second interval may be multiplied by a coefficient equal to 1.769, the value associated with the third interval may be multiplied by a coefficient equal to 0.90025, and the value associated with the fourth interval may be multiplied by a coefficient equal to 1.903. Alternatively, the coefficient for the first interval may be equal to-9.215, the coefficient for the second interval may be equal to-530, the coefficient for the third interval may be equal to 0.7, and the coefficient for the fourth interval may be equal to 1.23. Other coefficient values may be used.

In some embodiments, the values multiplied by their respective coefficients are summed. In addition, the value multiplied by the sum of their respective coefficients may be added to a constant C. For example, the value summed by multiplying their respective coefficients may be added to-2600. Alternatively, the constant C may be equal to-1650.

In some embodiments, the coefficient values { a- > -8-2045, b- >1.769, c- >0.90025, d- >1.903, offset- > -2600} are used with a lower sampling rate of the input electrocardiogram signal (e.g., 300Hz), and the coefficient values { a- > -9.215, b- > -530, c- >0.7, d- >1.23, offset- > -1650 } are used with a higher sampling rate of the input electrocardiogram signal (e.g., 600Hz or 1.2 kHz).

To calculate the autonomic nerve dysfunction AD, the result of the summation may be raised to an index equal to the reciprocal of the index used to determine the value associated with each interval.

For example, in some embodiments,

where i is a summation index representing the interval and n is the number of intervals.

In some embodiments, the value representative of the calculated autonomic dysfunction is graphically displayed on a display associated with the means for calculating autonomic dysfunction.

Fig. 3 shows a flow chart of a method 300 of treating a patient. The method 300 may be used in the method 100 shown in fig. 1. In some embodiments, the method 300 shown in FIG. 3 may be performed separately and differently than the method 100 shown in FIG. 1. In addition, the method 100 shown in FIG. 1 may use a method of treating a patient that is different from the method 300 shown in FIG. 3. For example, physical therapy, other forms of stimulation, manipulation, and pain medications, such as opiates.

In method 300, a patient is treated by electrically stimulating a point on the patient's skin to sensitize the autonomic nervous system.

In step 310, a site on the skin of the patient where the autonomic nervous system is sensitive is identified. For example, a graphical representation of the last part of the patient's body with the identified sensitive points may be referenced. In some embodiments, the location corresponds to a location identified as a needle application point.

In step 320, the electrical stimulation source generator is adjusted to provide the appropriate stimulation signal. For example, one or more parameters, such as at least one of frequency, amplitude, DC bias, power, and treatment duration, may be programmed into the electrical stimulation source generator. In some embodiments, the electrical stimulation source generator is programmed based on a value determined with a value calculated based on the biological event data. For example, one or more values associated with autonomic dysfunction or sympathetic vagal balance may be used to determine one or more values for one or more parameters to be programmed into the electrical stimulation source generator.

For example, fig. 4 shows a graph for determining parameter values for use in the method of fig. 3 based on measured characteristics of the patient ANS. In particular, fig. 4 shows a graph that may be used to determine the set power for an electrical stimulation source generator. In this example, the set power may be determined based on a value related to the sympathetic vagal balance. In this example, a higher set power is used for a higher calculated sympathetic vagal balance value. Alternatively or additionally, similar graphs may be used to determine other parameters for encoding an electrical stimulation source emitter based on measured patient ANS characteristics.

In step 330, electrical stimulation is provided to the site identified in step 310. For example, a needle may be inserted at each identified site, and this needle connected to an electrical stimulation source transmitter. In addition, a circuit completion path, such as a ground path, is provided by connecting the circuit completion path from the electrical stimulation source transmitter to the patient. Electrical stimulation is provided to the patient by the electrical stimulation source emitter by inserting a needle at the site identified in step 310, the electrical stimulation source generator having been encoded with the parameter values of step 320.

Although the present invention is disclosed by way of specific embodiments as described above, these embodiments are not intended to limit the present invention. Based on the above-disclosed methods and technical aspects, those skilled in the art may make modifications and variations to the presented embodiments without departing from the spirit and scope of the invention.

Referring to fig. 6, a method for analyzing ANS status includes steps S1 to S13.

In step S1, a first time varying signal or data representative of a quasi-periodic event generated by a biological system governed by a patient ANS is acquired. For example, the biological system is the heart system, respiratory system or brain system of the patient. The first time-varying signal is an original signal, i.e. a non-smoothed signal and a non-filtered signal. Thus, all variations of this signal are preserved, including the miniature variations.

In step S2, quasi-periodic events in the first time-varying signal are detected and the time intervals between these quasi-periodic events are calculated to form a second time-varying signal or data, referred to as a "time interval signal", taking discrete values consisting of a series of counted time intervals. These series of time varying signals are chaotic. In a preferred embodiment of the present invention, the time-varying signal acquired in step S1 is an Electrocardiogram (ECG) of the patient, and the time interval calculated in step S2 is an RR interval, i.e., an interval between R-waves of the electrocardiogram. Fig. 10 shows an example of the time interval signal obtained in step S2 in the case of such RR intervals in an exemplary manner. Each point in the signal of fig. 10 corresponds to a calculated time interval. Such signals are known in the art as fractal.

In practice, S2 is performed in time, i.e., each time an event occurs in the first time-varying signal, the event is detected and the time interval between this event and the previous event is calculated. In the same manner, the algorithm formed by the following steps S3 to S13 is executed each time the time interval is calculated by step S2.

In step S3, a time window W is defined. Upper limit L of time window W₁Corresponding in time to the last time interval calculated in step S2. Setting a lower time limit L₀So that the width L of the time window W₁-L₀A predetermined number N corresponding to the calculated time interval. In other words, the window W covers the lastThe (current) calculated time interval and the previously calculated time interval N-1. The predetermined number N corresponds to a time scale in which the state of the ANS is determined and visualized. This number may be selected by the user. Its default value is, for example, 40.

In step S, two convex or upper envelopes of the time interval signal obtained in step S2 are calculated in a window W. One of the various envelopes is calculated in the direction of the time sequence, rising from the lower limit of the time window W to the lower limit L₁. Calculating another envelope in a direction opposite to the time sequence, from the upper limit L₁Down to the lower limit L₀And then reset in time sequence. By way of general illustration, fig. 7 and 8 show, for a given arbitrary signal SIG in the window W, the corresponding upper envelope as calculated in the chronological direction and the corresponding upper envelope as calculated in the direction opposite to the chronological direction, respectively. As is evident from these figures, the two envelopes are different and therefore contain different supplementary information on the variants of the signal SIG. It should be noted that the upper envelope of a given signal f (t) is given by:

the upper envelopes, as obtained in step S4 of the present invention, are each in the form of a table or vector having N values, each of which corresponds to one of the discrete values extracted from the time interval signal. The table corresponding to the upper envelope calculated in the direction of the chronological order will be referred to below as ForwHull, and the table corresponding to the upper envelope calculated in the direction opposite to the chronological order will be referred to below as BackwHull.

The sequence of steps S5 to S10 is performed in parallel to step S4. Step S5 mainly consists in constructing a multidimensional phase space for the time interval signal portion in the window W. The concept of phase space is known per se in mathematical physics. For example, the protocol used for the construction of the phase space and the reasons for this construction are described by Packard et al, 1980, 1/9 on Physical ReviewLetters, Vol.9, 45Described in a paper entitled "Geometry from a Time Series" and in a paper entitled "predictingchanotic Time Series" on Physical Review Letters, vol 8, 59, by Farmer et al, 24, 1987. The present invention follows the above scheme, and as such, the phase space is constructed in the following manner: from the series of values taken by the time interval signal in the window W, from the lower limit L₀To the upper limit L₁From X₁、X₂、X₃...X_NThe nomenclature, vectors, e.g., three-dimensional, are constructed using time intervals or delays, e.g., of four dimensions. Thus, in general, the first vector will have as its first component the first value X of the time interval signal in the window W₁Fifth value X of the time interval signal in the window W as its second component₅And a ninth value X of the time interval signal in the window W as its third component₉. The second vector will have as its first component a second value X of the time interval signal in the window W₂And as second and third components thereof sixth and tenth values X of the time interval signal in the window W₆Equal X₁₀And so on.

Preferably, to obtain the value N of such a vector, the series of vectors is completed by repeating the last completed vector as many times as possible at the end of the sequence. The vectors obtained are listed below:

although in the preferred embodiment of the invention the dimensions of the vector, i.e. the dimensions of the phase space, and the time interval are equal to 3 and 4, respectively, these dimensions and time intervals may be different. However, when such dimensions and time intervals are different, it is preferable to keep their product equal to 12.

The vectors obtained as described above each represent a point in the phase space. It has been observed by the present inventors that the points of the phase space are not randomly distributed, but form a set of points, each of which represents a common equilibrium state of the ANS. As an illustration, fig. 9 shows the phase space obtained during a tilt test applied to a patient, i.e. a test (80 ° angle) in which the patient is levered from a horizontal position to a quasi-vertical position, as seen, the phase space includes two separate sets of points CL1, CL 2. Each of these sets of points CL1, CL2 corresponds to one of the horizontal and quasi-vertical positions described above.

Step S6 consists in reducing the dimensions of the phase space in order to obtain positional information about the points relative to each other. Step S6 more specifically consists in orthogonally projecting the points of the phase space, i.e. the points related to the above-mentioned vectors, onto the lower dimensional space where the order relationships are established. In general, step S6 projects points of the phase space onto straight lines that minimize the average distance between the points and the straight lines. This straight line passes through the set of points, as depicted in fig. 9 at reference SL. This can be obtained by conventional linear fitting methods. The straight line gives an orientation which can be chosen arbitrarily, preferably according to the phase space axis which is most parallel to the straight line.

Once all the points of the phase space are projected on the above straight line, step S7 calculates the relative distances between the projected points while observing the time order of the points. Precisely, step S7 calculates the first point in the time sequence (i.e., with the first vector or point (X)₁、X₅、X₉) Associated proxels) and a second point in the temporal sequence (i.e., associated with a first vector or point (X)₂、X₆、X₁₀) The associated proxels), then the distance between the first and third points in the chronological order, then the distance between the first and fourth points in the chronological order, and so on. Then step S7 calculates the distance between the second point in the time series and the third point in the time series, then calculates the distance between the second point and the fourth point in the time series, and then calculates the distance between the second point and the fifth point in the time series, in such a manner. Then step S7 calculates the distance between the third point and the fourth point in the time series, and then calculates the distance in the time seriesAnd the distance between the third point and the fifth point in (1) is based on such support. Therefore, step S7 calculates the N (N + l)/2 distance. Given the curve of the projected straight line on which the points lie, these distances are positive or negative (the value zero is taken into account, for example, as a positive value). All these distances are set in a table and arranged therein in the order in which they are calculated. This table represents the average distance between the set of points in the multi-dimensional phase space.

In step S8, the positive distance and the negative distance calculated in step S7 are discriminated. More specifically, a first table Tinc and a second table Tdec are created, comprising the absolute values of the positive and negative distances, respectively, the values in each of these tables Tinc, Tdec maintaining the same order, i.e. chronological order, of their original tables. The tables Tinc, Tdec created in step S8 may have different lengths. Starting at the last (most recent) time position in each of the tables Tinc, Tdec, the first intersecting set of N successive values with the highest average is selected and kept in the table, the other values being discrete, thus reducing the dimensionality of each of these tables to N in step S9. Furthermore, if one of these values of N, which is kept in the table Tinc or Tdec, is lower than the predetermined value R, these values are replaced in the respective table Tinc or Tdec by the values preceding in the group of values of N. The predetermined value R may be selected by the user. This value R represents the minimum change in event interval between events in the first time-varying signal deemed important to the user. The two tables obtained at the end of step S9 will be referred to below as Cinc (the table containing the positive distances) and Cdec (the table containing the absolute values of the negative distances).

In step S10, the tables Cinc and Cdec are combined with the upper envelopes ForwHull and BackwHull to provide information about the time-of-day status of the ANS. To achieve this effect, two different operations are performed in real time, called CTl and CT2, which are disclosed below:

CTl:

Coeffinc ₁＝B+(4–4A–5B+4AB)·Cinc–B·Cdec

Coeffdec ₁＝B–B·Cinc+(4A–4AB–B)·Cdec

where a and B are predetermined constants, in a preferred embodiment of the invention a and B are equal to 0.5 and norm coeff is a normalized coefficient.

Coeffinc ₁·ForwHullIs the table Coeffic₁And ForwHull, and

Coeffdec ₁·BackwHullis the table Coeffdec₁And BackwHull.

CT2:

Where A and B are the same predetermined constants as in CT1, norm coeff is the same normalized coefficient as in CT1, ₂Coeffinc·ForwHullis the table Coeffic₂And ForwHull, and ₂Coeffdec·BackwHullis the table Coeffdec₂And BackwHull.

According to the invention, the table ANSegram obtained as above by the operation CT1₁Representing the status of the parasympathetic subsystem of the ANS, and obtained as above by the operation CT2₂Representing the state of the sympathetic nervous system of the ANS. Thus, the present invention not only provides information about ANS statusBut also the sympathetic and parasympathetic subsystems of the ANS. In practice, as will be apparent below, the table ANSigram₁And ANSIgrani₂Will be presented to the user in the form of a curve connecting the points of the table. The shape of this curve will be directly judged by the user. For example, smooth ANSegaram₁And ANSegaram₂The curves would indicate a low reactivity of the ANS, however, it was observed that, for example, a continuously increasing slope in these curves would indicate a change in velocity over a time interval, i.e. in case the first time-varying signal is an ECG, a change in heart activity. The user will also have the possibility to compare the morphology of these curves with previously observed curve morphologies to accurately identify problems affecting the patient. In addition, the curve ANSIgram₁And ANSegaram₂Point-by-point subtraction of one from the other will give the user an observation of the balance between the sympathetic and parasympathetic subsystems, which the inventors of the present application found is non-linear.

In step S11, a first index ANSindex is calculated₁For representing tables or curves ANSigram₁And calculating a first index ANSindex₂For representing tables or curves ANSigram₂The complexity index of (c). When the corresponding curve ANSegaram₁、ANSigram₂Index ANSINdex when each shows a large fluctuation₁、ANSindex₂Are respectively larger numbers, and when curve ANSigram₁、ANSigram₂Index ANSINdex when each shows small fluctuation₁、ANSindex₂Respectively, are small numbers, i.e. almost straight.

These indices are typically calculated as Bouliland dimensions normalized to the outside, for example in the following way:

where Floor refers to the integer part, if the parameter is negative, Floor returns to zero, ANSlength₁And ANSchlegth₂Respectively refer to the curve ANSegram₁Length and curve ANSegaram of₂Length of (1), range₁Curve of finger curve ANSIgram₁Is the difference between the last value and the first value, range₂Curve of finger curve ANSIgram₂Is compared to the first value.

In step S12, an index ANSirisk is calculated, which represents a curve ANSigram₁And ANSegaram₂Is detected at the next event in the first time-varying signal (i.e. in the case of ECG, at the next R-wave detected), which means the probability of a change in the state of the ANS. This index ANSirisk represents the activity level of the ANS in another way. The calculation of the index ANSirisk is based on one of the tables Tine and Tdec obtained in step S8, in particular in the case described above in connection with step S6, where the direction of the projected straight line is chosen according to the axis, this straight line being most parallel to the axis. This index ANSirisk is generally determined in the following manner: first, it is determined that the number of values a1, a1 is greater than a predetermined number rstart, the number of values a2, a2 is greater than rstart +1, the number of values a3, a3 is greater than rstart +2, …, in the table Tdec, the number of values a1, a1 is greater than a predetermined number rstart, in the table Tdec_rsto_p–_ratart，_rsto_p–_ratartGreater than a_rsto_p–_ratartGreater than rstop, where rstop is also a predetermined value. Then, the number a is calculated_iIs calculated as the weighted average of (a).

The preferred relationship for determining the numbers rstart and rstop is given below:

rstop＝Floor(-rstart+0.5|RstCenter-3.95-1.43rstart|+RstCenter+16)

in step S13, a curve ANSIgram is displayed₁And ANSegaram₂And index ANSINdex₁、ANSindex₂And ANSirisk. Preferably, the first time-varying signal is also displayed. Then, the algorithm returns to step S2 for the next event in the time-varying signal acquired from the patient.

Examples of results obtained using the method according to the invention are now disclosed with reference to fig. 10 to 13.

Fig. 10 shows signals representative of RR intervals of healthy patients during a 5 minute period. At the time t of this cycle₀And t₁In between, the patient is subjected to a tilt test. It can be seen that the speed change occurs at time t₀And t₁In RR intervals therebetween. In practice, however, this speed change may only occur at time t once the general decrease in signal is discernable₀Some time later is detected on the RR interval signal. In the example of FIG. 10, the time at which a change in velocity has occurred, as may be observed from a single experiment by conventional means, is referred to as t₂Instant of time t₂Relatively close to the time t₁. For some patients it is also mentioned that the tilt test does not always lead to a definite change in the velocity at the RR interval, thus making it difficult to detect changes.

FIG. 11 shows at time t₀And t₁ANSigram curve obtained during the tilt test between₁And (3) superposition. Each of these curves is considered to be an ANS side-deal after tapping the patient's heart or rather after determining RR intervals"photographing (photopraphy)" of the time state of the sensory nerve component. In fig. 11, the darker the curve, the newer it is. It can be seen that the curve ANSIgram₁At time t₀And t₁Which means that the process according to the invention is very reactive. Since the morphology of this curve is apparent, no ruler is required, however the aspect ratio is determined for displaying the curve. Figure 12 shows on the same graph a series of indices ANSindex obtained during the five minute period mentioned above₁And a series of indices ANSindex₂. Index ANSindex₁Denoted by the fork, ANSindex₂Indicated by a rectangle. Interestingly, we note that the index ANSindex₁Increased at the start of the tilt test, and at time t₁Has previously fully peaked with the patient at the 80 position and at the above-mentioned time t₂In a conventional manner, and even more fully observed, while the index ANSindex₂Slowly increasing at the beginning of the tilt test until the first peak is sufficiently located at the instant t₁And then. Thus, the index ANSINdex₁Fast response, and index ANSindex₂With a slower reaction. Index ANSINdex once the patient has reached the 80 ℃ position₁Reduced, yet index ANSindex₂Take over and exhibit different waves. All of this is in complete agreement with the presently known behavior regarding the sympathetic and parasympathetic subsystems. In particular, the presence of the above waves in the index ANSindex2 can be illustrated by the release of catecholamine hormones from the sympathetic nervous system.

Fig. 13 shows the evolution of the index ANSirisk during the 5 minute period mentioned above. It can be seen that this index essentially shows a peak at the midpoint of the ramp between times t0 and t 1. In practice, rather than being displayed as a curve as shown in fig. 13, the index ANSirisk may be presented to the user in the form of an upward and downward measured movement as a function of time.

The method as described above is typically performed by a suitably programmed processor. As shown in fig. 14, the section named by reference 1The processor is connected to the output of the acquisition unit 2 via a suitable interface (not shown). The acquisition unit 2 is associated with an electrode 2a connected to the patient and performs an analog-to-digital conversion to generate a first time-varying signal representative of a quasi-periodic event. The acquisition unit 2 is, for example, an ECG unit. A display unit 3 is connected to the processor 1 to display the results provided by the method according to the invention, such as the curve ANSegram₁And ANSegaram₂These curves ANSegram₁And ANSegaram₂Difference between them, index ANSindex₁And ANSindex₂These indices ANSindex₁And ANSindex₂(see fig. 12) and/or an index ANSirisk and a first time-varying signal.

In practice, several embodiments are possible for arranging the units 1, 2, 3 relative to each other. According to a first embodiment, the processor 1 and the display unit 3 are part of a laptop computer, which is connected to the acquisition unit 2, for example via a USB port. According to a second embodiment, the processor 1 is part of a plug-in electronic board. According to a third embodiment, the processor 1, the acquisition unit 2 and the display unit 3 are part of a stand-alone device, further comprising a main circuit board, a printer, a media recorder (CD-ROM.), a battery, etc. According to the fourth embodiment, the processor 1 and the display unit 3 are part of a handheld device, for example, a cellular phone, a device in Palm OS (registered trademark), a PocketPC (registered trademark) device, any personal digital assistant, or the like.

Further, in some embodiments, the connection between the electrode 2a and the acquisition unit 2, between the acquisition unit 2 and the processor 1, and/or between the processor 1 and the display unit 3 may be a wireless connection, such as a Bluetooth (registered trademark) connection.

The invention as described above may be used in various applications, especially where the assessment of the ANS is expected with respect to diagnostic and prognostic processes, for example:

(1) department of cardiology:

risk stratification (arrhythmia, coronary heart disease, hypertension.)

dosing of beta-blockers

Indication of the cardioverter of syncope patients

Prognostic factor for myocardial infarction

2) The endocrinology department:

-diabetes and risk assessment

Estimation of familial autonomic abnormalities

3) The anesthesia department:

better administration of analgesics and hypnotics

Detection of cardioprotective agents

Assessment of risk of syncope during spinal and epidural anesthesia

4) Obstetrics and gynecology department:

fetal monitoring, detection of fetal instability

5) Pain control and treatment:

adjusting the dose of analgesic

Coupling with PCA (patient controlled analgesia)

Evaluation of pain in infants and children

6) Sleep disorders:

detection of SAS (sleep apnea)

7) Heart transplantation:

-detecting rejection

Evaluation of ANS regeneration of the heart

Although the present invention has been described in the context of an ANS, it will be readily apparent to those skilled in the art that the principles of the present invention will be applied to systems other than ANS, and in particular different biological systems, provided that the events in the time-varying signal are quasi-periodic and the corresponding series of time intervals are chaotic, i.e. weakly dependent on the initial conditions.

Claims (14)

1. A method of analyzing the extent of autonomic dysfunction of the autonomic nervous system before and after treatment, the method comprising:

measuring an autonomic nervous system condition at successive points in time separated by predefined interval types, wherein the autonomic nervous system condition is characterized by: a first index value and a second index value at each of the successive time points, wherein a stimulating electrotherapy treatment is applied within a range of the time points;

obtaining a difference of the first and second exponent values at each of the successive time points;

calculating a root of the sum of values, wherein one or more values are equal to a sum of differences raised to an index to form a metric, wherein the metric estimates a degree of autonomic dysfunction;

displaying, by a display unit, a representation of the computed root; and is

The first and second exponent values are:

and

where Floor refers to the integer part, if the parameter is negative, Floor returns to zero, ANSlength₁And ANSchlegth₂Respectively, the length of the first curve defined by the vector ANSigram1 and the length of the second curve defined by the vector ANSigram₂Length of the second curve, range₁Means the difference between the last value and the first value of said first curve, range₂Refers to the difference between the last value and the first value of the second curve, and N refers to being equal to the vector ANS igram₁And ANSegaram₂A predetermined number of dimensions.

Wherein:

wherein:

ForwHull is the first upper envelope of the time-varying signal in the time-sequential direction,

BackwHull is the second upper envelope of the time-varying signal in the reverse chronological direction,

Coeffinc ₁＝B+(4–4A–5B+4AB)·Cinc–B·Cdec，

Coeffdec ₁＝B–B·Cinc+(4A–4AB–B)·Cdec，

and

and

where a and B are predetermined constants, norm coeff is a normalized coefficient, and Cinc and Cdec are vectors representing positive and negative distances, respectively.

2. The method of claim 1, wherein the difference values belong to a subset of a set of difference values, and wherein the subset comprises a plurality of difference values of the consecutive set of difference values when the set of difference values is ordered by values within a range of time points.

3. The method of claim 2, wherein the set of difference values comprises four subsets.

4. A method according to claim 2 or 3, wherein boundaries between subsets are defined based on the second order inverses of the set of differences.

5. The method of any one of claims 1 to 4, wherein the index is the inverse of the root.

6. The method of any one of claims 1 to 5, wherein the root is a quadruped root.

7. The method of any of claims 1-6, wherein measuring the autonomic nervous system condition comprises measuring a heart rate over time, and wherein the predefined interval type is an interval between two heartbeats.

8. A system for analyzing the extent of autonomic dysfunction of an autonomic nervous system, the system comprising:

means for measuring an autonomic nervous system condition at successive points in time separated by predefined interval types, wherein the autonomic nervous system condition is characterized by: a first index value and a second index value at each of the successive time points, wherein a stimulating electrotherapy treatment is applied within a range of the time points;

means for obtaining a difference of the first and second exponent values at each of the successive time points;

means for calculating a root of the sum of values, wherein one or more values are equal to a sum of differences raised to an index to form a metric, wherein the metric estimates a degree of autonomic dysfunction; and

means for displaying, by a display unit, a representation of the computed root;

a system, wherein the first and second exponent values are:

and

where Floor refers to the integer part, if the parameter is negative, Floor returns to zero, ANSlength₁And ANSchlegth₂Respectively mean by the vector ANSegaram₁The length of the first curve defined and the sum of the vectors ANSegaram₂Length of the second curve, range₁Means the difference between the last value and the first value of said first curve, range₂Refers to the difference between the last value and the first value of said second curve, and N refers toIn the vector ANSegaram₁And ANSegaram₂A predetermined number of dimensions of (a) is,

wherein:

wherein:

ForwHull is the first upper envelope of the time-varying signal in the time-sequential direction,

BackwHull is the second upper envelope of the time-varying signal in the reverse chronological direction,

Coeffinc ₁＝B+(4–4A–5B+4AB)·Cinc–B·Cdec,

Coeffdec ₁＝B–B·Cinc+(4A–4AB–B)·Cdec，

and

and

where a and B are predetermined constants, norm coeff is a normalized coefficient, and Cinc and Cdec are vectors representing positive and negative distances, respectively.

9. The system of claim 8, wherein the difference values belong to a subset of a set of difference values, and wherein when the set of difference values is ordered by value, the subset comprises a plurality of difference values of the consecutive set of difference values.

10. The system of claim 9, wherein the set of difference values comprises four subsets.

11. The system of claim 9 or 10, wherein boundaries between subsets are defined based on the second order inverse of the set of differences.

12. The system of any one of claims 8 to 11, wherein the index is the inverse of the root.

13. The system of any one of claims 8 to 12, wherein the root is a quartic root.

14. The system of any of claims 8-13, wherein measuring the autonomic nervous system condition comprises measuring a heart rate over time, and wherein the predefined interval type is an interval between two heartbeats.