METHOD FOR OBTAINING USEFUL DATA ASSOCIATED WITH HEART RATE VARIABILITY PATTERN

Abstract

Method for providing a description, graphical representation, and a graphical identification of specific operating patterns of quasi-periodic cyclic systems, such as, but not limited to, reciprocating combustion engines, rotary machines, or biological organs such as the heart is disclosed. The disclosure also relates to a method for calculating an indicator evaluating the heart health or condition of an individual, as well as for diagnosing and issuing prognoses relating to the functionality, pathology, or standard of health of a machine or organism equipped with a motor or organ that operates cyclically, and to provide a description, a compact graphical representation, and a graphical identification of specific operating patterns of dynamic systems, for example economic systems such as the stock market.

Claims

1. A method for obtaining data associated with a heart rate variability (HRV) pattern, comprising: a) measuring and recording a number M of consecutive time intervals {X.sub.i}.sub.i=1, . . . ,M corresponding to cycles of one or more components of a heart pQRSt complex of an electrocardiogram, with a precision equal to or greater than 10% of the mean value of the cycle time, and M being greater than 2; b) calculating the variability on said M intervals of a sequence of consecutive vectors {?}.sub.j=1, . . . ,M?N of N components, according to the algorithm or transformation defined by the expression: $?_{j} = {{.Math.}_{n = 0}^{m} .Math. .Math. (\begin{matrix} m \\ n \end{matrix}) .Math. {(- 1)}^{n} .Math. (\begin{matrix} {? X ?}_{N_{0}, j .Math. ?_{0} + K_{0}}^{- 1} .Math. X_{j + n + k .Math. {.Math.}_{0} + J_{0}} - \\ {.Math.}_{1} .Math. {? X ?}_{N_{1}, j .Math. ?_{1} + K_{1}}^{- 1} .Math. {? X ?}_{N_{2}, j + n + k .Math. {.Math.}_{2} + K_{2}} \end{matrix})}_{k = J_{1}, .Math. .Math., J_{1} + N - 1},$ and the following notation: ${? X ?}_{L, l} = L^{- 1} .Math. {.Math.}_{h = 0}^{L - 1} .Math. .Math. X_{l + h}, with .Math. .Math. (\begin{matrix} m \\ n \end{matrix}) = \frac{m!}{n! .Math. (m - n)!},$ where the following parameters are integers and their selection determines the final form of the mentioned transformation:
{m,N,N.sub.0,N.sub.1,N.sub.2,?.sub.0,?.sub.1,?.sub.2,?.sub.0,?.sub.1,J.sub.0,J.sub.1,K.sub.0,K.sub.1,K.sub.2}, where additionally: m is a natural indicator representing the order of the discrete variation that is calculated; N is the dimension or number of components of each vector ?.sub.j, where N?2; N.sub.0, N.sub.1, and N.sub.2 indicate the number of values that are used for calculating the corresponding indicated local average in the general formula of the algorithm; ?.sub.0, ?.sub.1, ?.sub.2 have binary values 0 or 1, and indicate if the corresponding elements are, respectively, fixed or mobile in the calculation of each of the components of the vector ?.sub.j; ?.sub.0, ?.sub.1 have binary values 0 or 1, and indicate if the local mean is, respectively, fixed or mobile; J.sub.0 and J.sub.1 indicate the delay or advance of the first element that is taken in the calculation on the basis of the indicator j; K.sub.0, K.sub.1, K.sub.2 indicate the delay or advance of the first element that is taken in the corresponding local series for calculating the indicated local average; and wherein the position of the point indicated by the values of the components of each of the vectors ?.sub.j is graphically represented in two or more dimensions.

2. The method according to claim 1, comprising an additional step of comparing the data obtained with behavioral patterns associated with a vector function A={a.sub.j}.sub.j=1, . . . ,N, corresponding to the parameterization of a heart sequence, where the elements a.sub.j are fixed values or functions of one or more variables, and where an additional step of comparing the useful data obtained with said function A is performed according to the following steps: a) calculating the general angle ?.sub.i, the cosine of which is determined by: $\cos ? (?_{i}) = \frac{A .Math. ?_{i}}{.Math. A .Math. .Math. .Math. ?_{i} .Math.},$ where the symbol ? ? is the general norm of a vector in N dimensions, such that: $.Math. A .Math. = {({.Math.}_{j = 1}^{N} .Math. .Math. a_{j}^{2})}^{1 / 2};$ b) calculating the number of events M such that the angle ?.sub.i is less than a predetermined tolerance ?, where 0<?<1, such that the function A is explored in its space of existence in order to find said events in which ?.sub.i<?; c) calculating the coefficient M/M.

3. The method according to claim 2, wherein the calculation of the series {?.sub.j}.sub.j=1, . . . ,M?N of consecutive vectors of N dimensions or components is performed according to the following definitions of parameters: ${\begin{matrix} m = 0 \\ N = 5 \\ N_{0} = 5 \\ N_{1} = 5 \\ N_{2} = 5 \\ {.Math.}_{0} = 1 \\ {.Math.}_{1} = 1 \\ {.Math.}_{2} = 0 \\ ?_{0} = 1 \\ ?_{1} = 1 \\ J_{0} = 0 \\ J_{1} = 0 \\ K_{0} = 0 \\ K_{1} = 0 \\ K_{2} = 0 \end{matrix}},$ such that the definition of the variability on said M intervals is:
?.sub.j={X.sub.j+k custom-character X.sub.N,j.sup.?1?1}.sub.k=0, . . . ,N?1.

4. The method according to claim 3, wherein the following steps are additionally performed: a) calculating the number of events on the basis of the series {X.sub.i}.sub.i=1, . . . ,M with the specific definition of the vector A=t{(N+1)/2?j}.sub.j=1, . . . ,N where N>1; b) using the indicator m.sub.S1/M, with m.sub.S1=M calculated in the preceding point, for determining the existence of behavioral patterns associated with the vector function A.

5. The method according to claim 3, wherein the following steps are additionally performed: a) calculating the number of events M on the basis of the series {X.sub.i}.sub.i=1, . . . ,M with the specific definition of the vector A.sub.N=t{sin(2?.Math.j)/N}.sub.j=1, . . . ,N , where N can range from N=3 to N=12, corresponding to a sinusoidal modulation of the heart rhythm combined with the respiratory rhythm, where t can have any value; b) using the indicator m.sub.S2/M, with m.sub.S2=M calculated in the preceding point, for determining the existence of behavioral patterns associated with the vector function A.

6. The method according to claim 3, wherein the following steps are additionally performed: a) calculating the coefficient M/M on the basis of the series {X.sub.i}.sub.i=1, . . . ,M with the definition of the vector $A_{N} = t .Math. {- 1, 1, \underset{\underset{N}{?}}{0, .Math. .Math., 0}},$ where N can range from N=1 to N=20, corresponding to a compensated ectopic beat, and where t can have any value; b) using the indicator m.sub.E/M, with m.sub.E=M calculated in the preceding step, for determining the existence of behavioral patterns associated with the vector function A.sub.N.

7. The method according to claim 3, wherein the following steps are additionally performed: a) calculating the coefficient M/M on the basis of the series {X.sub.i}.sub.i=1, . . . ,M with the specific definition of the vector $A_{N} = t .Math. {N, \underset{\underset{N}{?}}{- 1, .Math. .Math., - 1}},$ where N can range from N=2 to N=20, corresponding to a regular paroxysmal tachycardia, and where t can have any value; b) using the indicator m.sub.TP/M, with m.sub.TP=M calculated in the preceding point, for determining the existence of behavioral patterns associated with the vector function A.sub.N.

8. The method according to claim 1, wherein the component of the heart pQRSt complex is the RR interval of an electrocardiogram.

9. The method according to claim 1, wherein the recording of a number M of consecutive time intervals {X.sub.i}.sub.i=1, . . . ,M, corresponding to cycles of a component of a heart pQRSt complex is performed with a precision greater than 0.01% of the mean value of the cycle time.

10. The method according to claim 3, wherein 0<?<0.1.

11. The method according to claim 6, wherein t is 1 or ?1.

Description

DESCRIPTION OF THE DRAWINGS

[0061] FIG. 1. Representation of the distance of the vectors for a particular case of heart failure. Characteristics lines of this specific case and their mathematical expressions are shown. Furthermore, central regions of the graph have been enlarged with the areas of interest highlighted in order to see the details.

[0062] FIG. 2. (a) Four-dimensional graph of the location of M?3 vectors {?.sub.i}.sub.i=1, . . . ,M?N+1 normalized with the overall mean of a normal HR (a healthy adult). (b, c) The same for two subjects with chronic heart failure (three viewing angles of each). The graphs located on the right in (b, c) show universal patterns when projected with the suitable angle. (d) Identification of the main lines of the heart failure in two different individuals with HF.

[0063] FIG. 3. (a) The different regions of normal activity and of HF found in this study: NSR (normal activity, green; Fantasia, cyan); HF (magenta). (b1-b3) The different regions occupied by the four situations found in this study in the multivariable space {log.sub.10(A1),log.sub.10(B1),log.sub.10(?.sub.N)}: NSR (n.a., green; Fantasia, cyan); MI (blue); SD (red); HF (magenta). For greater clarity, the left panel provides three situations (NSR n.a., myocardial infarction and HF), the central panel provides four situations (NSR n.a., MI, SD and HF), and the right panel provides all the situations studied. N=5 in all the results of this Figure.

DETAILED DESCRIPTION OF THE INVENTION

[0064] A map of Poincare is a graph consisting of the representation of a recurrence map or sequential path of the values having a certain variable in consecutive cycles. Specifically, a two-dimensional (2D) recurrence map is a planar projection where complex paths, with multidimensional characteristics (i.e., specific arrhythmic sequences) are overlapping and indistinguishable. This invention allows for a generalization of said recurrence maps. Among other possibilities, a normalized variability using a moving average of order N=5 can be formulated. This formulation corresponds with the following selection of the fifteen values defining the base algorithm of the methods described in this invention:

[00008] ${\begin{matrix} m = 0 \\ N = 5 \\ N_{0} = 5 \\ N_{1} = 5 \\ N_{2} = 5 \\ {.Math.}_{0} = 1 \\ {.Math.}_{1} = 1 \\ {.Math.}_{2} = 0 \\ ?_{0} = 1 \\ ?_{1} = 1 \\ J_{0} = 0 \\ J_{1} = 0 \\ K_{0} = 0 \\ K_{1} = 0 \\ K_{2} = 0 \end{matrix}} .$

[0065] By means of this selection, referred to as NL (Local Normalization), an expression of the sequential variability {?.sub.j}.sub.j=1, . . . ,M?4, with

[00009] $?_{j} = {X_{j + k} .Math. {? X ?}_{N, j}^{- 1} - 1}_{k = 0, .Math. .Math., 4}, {? X ?}_{N, j} = {.Math.}_{i = 0}^{N - 1} .Math. .Math. X_{j + i},$

is obtained and the representation thereof meets the aforementioned requirements for an ideal representation of the heart function in terms of compactness and integrity, and furthermore it reduces to a minimum exogenous and accidental influences as a result of the local normalization resulting from the values {X.sub.i}.sub.i=1, . . . ,M. The proposed method can be applied to any of the components of the heart pQRSt complex, although this example focuses on the analysis of the main component, the series of RR intervals. Generally, by exploring the interval of order from N=2 to N=100, it can be seen that a quantification measurement of the overall variability, such as the norm or argument of the center of mass vector of the distribution of points defined by the vectors ?.sub.j={X.sub.j+k custom-character X.sub.N,j.sup.?1?1}.sub.k=0, . . . ,N?1?{.sub.j,k}, recurrently has a minimum for N=5 in the case of healthy individuals. This could be due to the fact that the average heart rate is about five times greater than the respiratory rate in the human species, which gives rise to a subharmonic of order five of the HRV, thereby maximizing overall compensation when N=5. If this hypothesis is correct, deviations from normality should optimally be distinguished using the order N=5.

[0066] Fortunately, N=5 gives rise to the most complete graph, with complete graphical representation possibilities, among all the possible orders. In fact, the expression ?.sub.j={X.sub.j+k custom-character X.sub.N,j.sup.?1?1}.sub.k=0, . . . ,N?1 allows the representation thereof in N?1 dimensions since the information contained in N?1 of the total of N elements provides all the information of the vector of N dimensions. That is because the value of any of the N elements can be obtained on the basis of the other N?1 since the sum of the N elements is always nil, by definition. The resulting four-dimensional sequential information vector, which can be graphically represented in 3D plus color, gives rise to a valuable descriptive and comparative tool from the clinical point of view. This graph allows the identification of sequences that originate universal patterns, the distribution and density of which provide an immediate and complete information about the state of cardiac function. Up to five universal sequences, which can particularly be seen in the graphical representation (see an example in FIG. 1, with N=4, and other examples in FIG. 2, with N=5 and another selection of parameters), have been identified using the method described, but they are not only present when N=5: they can all be found in higher orders, and their general expressions for any order N have in fact been obtained.

[0067] The universality of these sequences has been verified in a series of HR databases, with 133 records clustered into in four basic situations with distinctive characteristics: (i) individuals in normal sinus rhythm (NSR) during normal activity, or at rest in the supine position while watching the film Fantasia; (ii) ischemic cardiopathy, specifically myocardial infarction (MI); (iii) heart failure (HF); and (iv) recovery from sudden death (SD). By applying the mentioned databases to it, the percentage of occurrence of each of the five sequences and the primary variability (the definition of which will be provided below) provide a univocal and specific set of measurements capable of assessing the state of the heart in the records included in the publically available databases that were used (see databases used below).

[0068] A Holter recorder (HR) is a set M of consecutive values corresponding to the RR intervals, which can be expressed as {X.sub.j}.sub.j=1, . . . ,M. It is easy to demonstrate that the general distance vector in N dimensions from any point {X.sub.j+k}.sub.k=0, . . . ,N?1 formed by N RR intervals to the line of identity (line of zero variability, Piskorski & Guzik 2012), defined by the identity vector {1, 1, . . . , 1}.sub.N, is defined as

[00010] $D_{j} = {X_{j + k} - {? X ?}_{N, j}}_{k = 0, .Math. .Math., N - 1} .$

The sequence of RR intervals of a healthy heart would dance around the line of identity but would never be supported on it (even in the extreme situations of complete relaxation or extreme exercise, there is a variability mathematically different from zero, even though the RRs are apparently constant). The locally normalized expression of said general distance vector is precisely the expression of the selection of NL parameters, i.e., ?.sub.j? custom-character X.sub.N,j.sup.?1D.sub.j={X.sub.N,j.sup.?1 X.sub.j+k?1}.sub.k=0, . . . ,N?1, where the basis for normalization is the local mean

[00011] ${? X ?}_{N, j} = {.Math.}_{i = 0}^{N - 1} .Math. .Math. X_{j + i} .$

In FIG. 1, a simple color code ranging between 0 and 1 (the Hue code of the Mathematica? 9.0 program) has been used as a fourth dimension.

[0069] Another selection of parameters that has been used for this study is the following:

[00012] ${\begin{matrix} m = 0 \\ N = 5 \\ N_{0} = 5 \\ N_{1} = M \\ N_{2} = M \\ {.Math.}_{0} = 1 \\ {.Math.}_{1} = 1 \\ {.Math.}_{2} = 0 \\ ?_{0} = 0 \\ ?_{1} = 0 \\ J_{0} = 0 \\ J_{1} = 0 \\ K_{0} = 0 \\ K_{1} = 0 \\ K_{2} = 0 \end{matrix}},$

which gives rise to the following expression ?.sub.j={ custom-character X.sub.M.sup.?1X.sub.j+k?X.sub.M.sup.?1X.sub.N,j}.sub.k=0, . . . ,N?1. This selection of parameters shall be referred to as NG (Overall Normalization), and the particular expression of ?.sub.j for this selection will be referred to as ?.sub.j. This expression has the following trivial relations with D.sub.j={X.sub.j+k? custom-character X.sub.N,j}.sub.k=0, . . . ,N?1 and with

?.sub.j={X.sub.j+k custom-character X.sub.N,j.sup.?1?1}.sub.k=0, . . . ,N?1.

?.sub.j= custom-character X.sub.M.sup.?1D.sub.j, and ?.sub.j=X.sub.M.sup.?1X.sub.N,j?.sub.j.

[0070] FIG. 2 shows the graphical representation of three HRs, analyzed with the proposed method, for N=5: FIG. 2 (a) represents a healthy subject, and FIG. 2 (b, c) represents two patients with chronic heart failure. While in the healthy individual it gives rise to a dense and compact shape around the origin, individuals with HF show distinctive spatial lines that follow previously established sequences. In view of this figure, some immediate conclusions can be extracted. First, the lines shown are fundamentally straight. In some cases (for example, FIG. 2 (b)), the space between two lines is joined by a characteristic plane, but there is a special viewing angle (angle of projection over the viewing plane) which systematically reduces the main straight lines and planes to just three in all cases [see FIG. 2 (c)].

[0071] As would be expected, the normalized Poincare sections of the variability of the NSR database (apparently non-pathological) are relatively centered and homogenously distributed around zero, showing a mathematical compensation (apparently) with certain randomization, i.e., a more or less spherical nucleus with a random, approximately Gaussian distribution. A more detailed observation shows that all the cases show a clear structure in the fourth dimension (color), along a specific direction. It is important to point out that this direction is exactly the same for all subjects, regardless the shape of the distribution of points representing the RR intervals. This fact points towards the existence of a subharmonic, probably related to the respiratory cycle, that reflects an automatism of the sympathetic-parasympathetic system. This effect is relatively rare or virtually absent in individuals with IM and is not found in individuals with HF and SD. The existence of an individual in the NSR database (around 12%) having the characteristics present in HF is quantified and discussed below, and it supports the fact that the presence of the sinus rhythm does not rule out the presence of a heart pathology.

[0072] The Fantasia database shows the same characteristics (lyengar et al. 1996; Schimitt et al. 2007) as the individuals in the NSR database, and almost the same percentage of subjects with HF characteristics. Furthermore, elderly healthy subjects clearly show less variability than young healthy subjects.

[0073] Almost 70% of the subjects in the HF database (Baim et al. 1986), and many of the subjects studied in the SD database (Taddei et al. 1992) show the same distinctive lines mentioned above. The percentage of subjects with homogenously distributed point clouds is proportionally inverse when compared with the individuals in the NSR database: less than 20%. On the other hand, the repetition of the patterns and their extent comply with the universal characteristics of HRV. Finally, individuals in the SD database used in this study show variable patterns that are completely different and visibly more irregular than any other group. Some of them have the same lines as the individuals with HF, but most of them show a very complex and apparently chaotic structure.

[0074] A first characteristic of the graphical representation proposed in this invention with both the NL and the NG selection (?.sub.j or ?.sub.j) is that it is centered around the origin by definition, such that the density of points in different areas can be considered a specific identifying signature of variability. Therefore, the norm of the normalized vector defining the center of mass of the graphical representation would be a primary measurement of the variability for an n-th subharmonic order, and of its overall degree of compensation (for example, in an HR of 24 hours for a circadian cycle). However, experience has shown that the entire record of vectors ?.sub.j gives rise to an inadequate compensation due to the inherent nature of the Poincare representation: it can be seen that the same arrhythmic RR interval X.sub.j appears as a common component in N?1 vectors ?.sub.j?k around the line of identity, which often produces an apparent overall compensation. To avoid this, a subset of all the series can be extracted by taking the indicator j in hops of N elements (i.e., j={1, 1+N, 1+2N, . . . }), where each X.sub.j appears only once. The distance from the center of mass of this subgroup to the origin, the graphical representation of which is virtually indistinguishable from that of the entire series except for the different densities thereof, is determined by:

[00013] $?_{N} = {[{.Math.}_{k = 1}^{N - 1} .Math. .Math. {({.Math.}_{i; N}^{M - N + 1} .Math. .Math. ?_{i, k})}^{2}]}^{1 / 2},$

where ?.sub.i,k is the component k of the vector ?.sub.i, and the notation of the sum

[00014] ${.Math.}_{i; N}^{M - N + 1} .Math. .Math. ?_{i, k}$

indicates N by N hops in the indicator i. This coefficient, referred to as primary variability (PV), can be represented for each individual as a function of N. Experience shows that ?.sub.N is about 10 to 100 times greater than the distance to the origin of the center of mass of the complete original set, which greatly amplifies the significance of ?.sub.N as it has been defined. It also depends on the number of beats in the series. In general, the minimum variability corresponds to NRS (Fantasia) individuals, closely followed by the variabilities of individuals with MI. The maximum variability corresponds to SD and HF, the distribution thereof being fairly similar. The variability of subjects in the NSR database with normal activity is at intermediate values. ?.sub.N is shown as a new quantitative measurement with extraordinary capacity to differentiate among patients with different heart function disturbances, in combination with the proposed graphical representation and arrhythmic structures derived from it.

[0075] A first classification of the different patterns that can be clustered into universal lines or sequences is provided below. On the other hand, it will be demonstrated that the density of points along those lines provides a valuable measurement of the state of the heart function. In fact, when the method of the invention is applied to the databases used, which are freely available, it gives rise to results with a high descriptive specificity for each group. As a first approach, the present invention is limited to the identification of the simplest primary arrhythmic sequences, which can be expressed in the form of a line, defined by the vector

A.sub.N=t{a.sub.pa.sub.2, . . . ,a.sub.N},

parameterized by an arbitrary variable t. Logically, the position vector of a real point (beat) on a specific arrhythmic line may correspond to any positive real value t. This reflects the intrinsic capacity of the described method: an arrhythmic line (or anomaly), which can be mathematically and universally expressed, brings together all the arrhythmias of the same nature, regardless of the heart rate and amplitude of variability. Several primary sequences that will be described below have been deciphered, and the density of points along the corresponding lines (sequences) has been calculated. These results do not exclude the existence of other more complex sequences, with specific associated characteristics that will be determined in future studies.

[0076] An immediate way to evaluate the density of points corresponding to a specific sequence is to quantify their presence in % through the entire total record of the series of RR. Given that the presence of a specific sequence is obviously not an exact or uniform amount in all the situations or in all individuals, it is necessary to use statistical means to determine the higher or lower presence of said sequence in a given heart condition. A convenient way to represent the distribution of the presence of a sequence in a certain condition is to determine the value of F.sub.i=i/M.sub.B versus y.sub.i for the corresponding database, y.sub.i being the percentage of presence of the particular sequence A.sub.N=t{a.sub.1, a.sub.2, . . . , a.sub.N} of an individual, i being the range of said individual in particular, based on his or her score y.sub.i, and M.sub.B being the total number of individuals in the database. This would be determined for each sequence and analyzed in combination for each situation.

[0077] Arrhythmia A1:

[0078] a healthy individual with NSR must show an intrinsic capacity for responding to any demand of the organism, by means of regular accelerations and decelerations of the heart rate directed by the sympathetic/parasympathetic balance. This capacity must be reflected in the appearance of the simplest form of HRV, which can be expressed as a linear ramp:

A1=t{(N+1)/2?j}.sub.j=1, . . . ,N

where the positive or negative sign of t is applied to the acceleration or deceleration of the heart rate, respectively. For example, for N=4, with an accelerated heart rate, it would be formulated as A1.sub.4.sup.+=t{1.5,0.5,?0.5,1.5}, with t>0; for N=5 and a decelerated heart rate, it would be A1.sub.5.sup.?=t{?2,?1,0,1,2}, with t>0, etc., where the higher values of t indicate a more abrupt rise or fall, or a more pronounced ramp, of the heart rate, without changes in the functional structure of the variability.

[0079] This is the dominant form of heart rate variability (HRV) in normal individuals in the MIT-BIH NSR database, as shown in Table 1. In fact, this is reflected in a slightly ellipsoid shape around the origin in the direction of the line of A1.sub.5 of any four-dimensional graph of a Poincar? map of the fifth order, which represents the HRV of a normal individual with NSR, as has been proposed herein [see FIG. 2 (a)].

[0080] Even more importantly, as the presence of this form of HRV decreases, other forms of pathological arrhythmias, which will be described below, increase by about the same proportion. This finding fundamentally points towards a basic organic fact, i.e., this arrhythmia is actually the basic degree of freedom of the HRV and reflects the adaptive capacity of a healthy organism. If a pathological situation depresses or limits this degree of freedom, other forms of HRV will manifest to compensate for this deficiency. It is important to point out that these alternative forms are not arbitrary, particularly in the individuals included in the HF database. In fact, they can be considered universal. Accordingly, the specific form of these alternative HRV patterns must be linked with the specific condition of an organism, which opens up the door to new forms of rapid, non-invasive diagnosis.

[0081] Arrhythmia B1 (Compensated Ectopic Beat):

[0082] The four-dimensional graph of HR belonging to individuals with HF generally have three lines (FIG. 2 (d)) which can be easily identified as (see FIG. 2 (d)):

B1.sub.5,1=t{?1,1,0,0},B1.sub.5,2=t{0,?1,1,0},B1.sub.5,3=t{0,0,?1,1}

First of all, these three sequences actually pertain to a single class of the following type

t{ . . . ,0,0,?1,1,0,0, . . . }

[0083] Second of all, the mean value of the four components is zero for any N>2, and these sequences can therefore be considered compensated. This means that the final point of the Poincare section of the n-th order, rests approximately on the line of identity. In other words, the arrhythmic sequence can also be considered closed or concluded since the last interval lasts for the same time as the local mean. The fact that the number of intervals with zero variability surrounding the succession {?1, 1} in most cases is greater than two (i.e., the line {1, 0, 0, ?1} does not clearly appear) should be pointed out. It can therefore be concluded that there is a specific sequence described by the following line:

B1=t{ . . . ,0,0,?1,1,0,0, . . . },

having clear ubiquity: this type of arrhythmia is definitively characteristic and clearly dominant in individuals with HF, with a mean of presence around an order of magnitude greater in HF than in SD or MI, despite the fact that it is also dominant in these subjects. While the primary variabilities (PV) of individuals in the HF and SD databases are large and very similar, what really distinguishes individuals with HF from those recovering from SD is the much higher presence in the former of arrhythmia B1. These arrhythmias correspond to compensated isolated ectopic beats; the present invention does not seek to identify their specific cardiac origin, whether supraventricular or ventricular, since the nature thereof may be associated with another characteristic that has not yet been studied.

[0084] As can be seen, this analysis can provide a new basis for elaborating a general classification based on the inherent and strongly compensated nature of these arrhythmias and their capacity to be reduced to a single universally expressible structure. On the other hand, these are arrhythmias that are relatively rare or absent in individuals in the NSR database. In fact, ectopic beats may occur in the recordings of normal individuals, but they are relatively rare. However, arrhythmias of this type are more present in the recordings of awake individuals at rest and in the supine position (Fantasia record mentioned below) than in normal individuals during normal activity. Nevertheless, the prevalence of arrhythmia B1 in HF may require a future review of the diagnostic value (Frolkis et al. 2003; and related references) of the presence of ectopic beats in combination with the capacity to effortlessly adapt to the demands of normal activity (adaptability associated with the presence of arrhythmias A1). Table 1 shows the strong inverse correlation between the presence of compensated ectopic beats (arrhythmias B1) and arrhythmias A1 (?). In addition to Table 1, the opposing presence of arrhythmias A1 and B1 is clearly illustrated in FIG. 3.

[0085] Arrhythmia B2 (Regular Paroxysmal Tachycardia):

[0086] The graphical representation shows the sequence described by [see FIG. 1 (a, b)]:

B2.sub.5,1=t{4,?1,?1,?1}

[0087] In many cases an additional line appears [see FIG. 1 (c, d)]:

B2.sub.5,2=t{?1,?1,?1,?1}

[0088] First of all, the occurrence of these sequences introduces an additional source of randomization upon being combined with A1. This provides an additional adaptive capacity which, on the other hand, may be very limited (it would look like a more intense failure) as the presence of arrhythmias A1 decreases (normal adaptability) in individuals with heart pathologies. Second of all, none of these sequences is compensated. This means that values of N of a higher order must be considered in order to find compensated or finished sequences. In this case, both B2.sub.5,1 and B2.sub.5,2 are actually part of the compensated sequence:

B2=t{4,?1,?1,?1,?1}

which actually pertains to a Poincare section of the sixth order (i.e., N=6). It can be seen that compensated sequences are described by lines of the following type:

B2.sub.N=t{(N?2),?1, . . . ,?1},

for example:

B2.sub.6=t{4,?1,?1,?1,?1}

B2.sub.7=t{5,?1,?1,?1,?1,?1}

B2.sub.8=t{6,?1,?1,?1,?1,?1,?1}

B2.sub.9=t{7,?1,?1,?1,?1,?1,?1,?1}

etc. All these sequences are almost as present as arrhythmia B1 in HF, although the presence of this type of arrhythmia drops as N increases. This sequence is hardly identifiable if N increases above 10. Actually, the arrhythmia with the greatest presence is B2.sub.6, and that is why this type of arrhythmia is generically referred to as B2 instead of B2.sub.6. The analysis of the presence of this arrhythmia in HF demonstrates that it is as omnipresent as B1, with a different dominance of one over the other, depending on the individual. This sequence is compatible with a regular paroxysmal tachycardia after a pause, which can be attributed to an atrioventricular block, which in some cases gives rise to Stokes-Adams syndrome. Its frequency of presentation in each situation is shown in Table 1. Like arrhythmia B1, arrhythmia B2 is characteristic of HF. It is important to take into account that this arrhythmia represents a noticeable pause, followed by a proportional rapid-rate series to compensate for the pause.

[0089] Arrhythmia B3 (Regular Paroxysmal Tachycardia II):

[0090] several sequences that are alternatives to B2 can be identified, such as:

B3.sub.6,1=t{?1,4,?1,?1,?1}

B3.sub.6,2=t{?1,?1,4,?1,?1}

B3.sub.6,3=t{?1,?1,?1,4,?1}

B3.sub.6,4=t{?1,?1,?1,?1,4},

or:

B3.sub.7,1=t{?1,5,?1,?1,?1,?1}

B3.sub.7,2=t{?1,?1,5,?1,?1,?1}

B3.sub.7,3=t{?1,?1,?1,5,?1,?1}

B3.sub.7,4=t{?1,?1,?1,?1,5,?1}

B3.sub.7,4=t{1,?1,?1,?1,?1,5}

etc., which can be expressed for a general indicator N as:

B3.sub.N,i=t{?1, . . . ,?1,N?2(at position i),?1, . . . ,?1}.

[0091] For a given N, all the sequences of B3 with different indicators i have the same presence, but said presence is significantly lower than B2. This sequence could be similar to intermediate situations by joining two consecutive sequences corresponding to regular paroxysmal tachycardia with relative pauses like in arrhythmia B2. It is surprising that this arrhythmia is less characteristic of HF; actually, it is as present in HF as it is in SD, with an almost identical probability distribution. On the other hand, it is significantly more present in NSR than in the individuals with IM and in the individuals in the NSR (Fantasia) database.

[0092] In some cases, a peculiar sequence appears as shadows of arrhythmia B2 [see FIG. 2 (d), for example], which can be identified as lines:

B3.sub.6,1=t{?4,1,1,1,1}

B3.sub.6,2=t{1,?4,1,1,1}

B3.sub.6,3=t{1,1,?4,1,1}

which can generally be written as

B3.sub.N,i=t{1, . . . ,1,?(N?2)(at position i),1, . . . ,1},

a somewhat complementary sequence of B3.sub.N?2, i. A quantitative analysis of this type of arrhythmia is not provided given its complexity.

[0093] Arrhythmia A2 (Relating to Breathing):

[0094] This is a relatively present sequence, though much less so than arrhythmia B1 or B2 in HF or SD, which appears as the dominant form of sub-arrhythmia in normal or asymptomatic individuals. This compensated sequence can be described by the following line:

A2.sub.N=t{sin(2?.Math.j)/N}.sub.j=1, . . . ,N,

representing a sinusoidal modulation of the heart rate along a range of N beats. This type of arrhythmia with mathematical compensation must also reflect a physiological compensation. Given that it is statistically more frequent with N=5 than with any other order, it may be concluded that it is related to the mean respiratory rate in the human species. Whether or not it is more frequent during sleep than during normal activity will be the object of future studies. Its presence in the records is analyzed in Table 1. It is relatively dominant in NRS with respect to the pathological records, therefore it is significantly less frequent in individuals with HF and SD. It can be deduced that this arrhythmia is, like A1, characteristically non-pathological. In other words, its presence is compatible with a good state of heart health.

[0095] The combination of the densities of each identified arrhythmia may constitute a valuable characteristic signature of a specific situation, thereby opening up the pathway to new diagnostic examinations, the meaning of which is out of reach of this example of application of the proposed invention. A fundamental finding is the inverse relation between the presence of certain types of arrhythmia, those which may be considered to be indicative of health, and those which may be referred to as pathological. Specifically, in the analysis of the public databases used, arrhythmia A1 and arrhythmia B1 are antagonistic: in fact, the relative presence of one with respect to the other is reversed when going from a situation of normality to a pathological state. FIG. 3 shows a universal map in which the presence of both arrhythmias in individuals in the NSR and HF databases is analyzed. A very clear difference can be seen between NSR and HF, based on the presence of A1 and B1.

[0096] Furthermore, the value of the primary variability ?.sub.N completes the set of characteristic variables for providing the distinctive, characteristic seal of each situation: it must be noted in Table 1 that the combinations of the presences of each arrhythmia and the PV makes up a unique and highly differentiated signature of each situation. The difference between MI and SD is ?.sub.N, small for MI and large for SD. The multivariable probability density function for each situation in the space of variables {log.sub.10(A1),log.sub.10(B1),log.sub.10(?.sub.N)} would be determined by the density of the points where, in the space, it would correspond to each situation, as can be seen in FIG. 3.

[0097] The clinical value of this representation could be extremely significant. In fact, the therapeutic effect of drugs with a specific cardiovascular action, targeting objectives such as sympathetic/parasympathetic axis (adrenergic beta-blockers), perhaps the neurohormonal rennin-angiotensin-aldosterone axis (IECAs, ARA II, etc.), etc., could give rise to the modification of the relative presence of each arrhythmia and displace the location of the corresponding graph in the direction of the NSR region. The potential prognostic value of the results obtained when applying the method of the present invention to HR is obvious. The observation of the four-dimensional graphical representation of HR allows the easy and immediate identification of disturbances in normality in healthy people.

[0098] Future clinical studies will expand the depth of knowledge acquired with the analysis proposed in this invention, such as the identification of new characteristics and new general arrhythmic patterns, relating to other pathologies and situations, not necessarily of a cardiac origin, for example, diabetes, hypertension, hypothyroidism, or even psychic disturbances.

TABLE-US-00001 TABLE 1 Ar- NSR(normal rhythmia activity) Fantasia MI SD HF A1+ 1.21849 0.841977 0.682145 0.355535 0.23905 A1? 0.725413 0.657101 0.502548 0.264764 0.188382 A2+ 0.417186 0.576313 0.320071 0.33573 0.158211 A2? 0.630867 0.58272 0.379129 0.284021 0.175146 B1 0.354003 0.568972 1.7985 1.48564 5.59225 B2 0.431622 0.32933 0.339216 0.493797 0.772125 ?.sub.5 10.1739 2.4295 1.7567 41.2053 48.9619

[0099] In conclusion, the present invention proposes a quantitative method not only to provide a universal representation of heart rate variability, but also a clinical tool that is potentially useful in evaluating heart function, risks, and probably other related health issues. In the present invention, among the many different universal sequence types that probably exist, some patterns that stand out have been described mathematically with a relatively simple structure, identifying five general types of arrhythmias. The databases used allowed identifying the characteristic signatures of individuals included in the NSR, MI, HF, and SD databases, by relating these basic types of arrhythmias with the different situations of heart function. As a fundamental result, it has been quantitatively demonstrated that two of these arrhythmias are characteristic of the state of health, whereas the other three are pathological. Their relative presence in an individual may eventually be related to specific situations, with the growing clinical evidence provided by this methodology which will be built on in the future. Furthermore, the temporal evolution of arrhythmic structures in a patient, which are visible with the application of the methodology of the invention, can provide very valuable information about the state and/or clinical evolution of said patient. It is considered a methodology that is easy to apply, and the potential of its results in non-invasive clinical diagnosis and prognosis could be vital.

METHOD FOR OBTAINING USEFUL DATA ASSOCIATED WITH HEART RATE VARIABILITY PATTERN

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