• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
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    • 专利摘要:
    The subject of the present invention is a method for the description, graphic representation and graphic identification of specific operating patterns of quasi-periodic cyclic systems, such as, but not limited to, reciprocating combustión engines, rotating machines or biological organs such as the heart. Another object of the present invention is a method for calculating an index of qualification of the heart condition or condition of an individual as well as its application for diagnosing and issuing prognostic judgments about the functionality, pathology or level of health of a machine or organism endowed with an engine or cyclic operating organ and for the description, compact graphic representation, and graphic identification of specific operating patterns of dynamic systems, for example economic systems such as the stock market. (Machine-translation by Google Translate, not legally binding)
    公开号:ES2630834A1
    申请号:ES201600164
    申请日:2016-02-24
    公开日:2017-08-24
    发明作者:Alfonso Miguel GAÑÁN CALVO
    申请人:Universidad de Sevilla；
    IPC主号:
    专利说明:

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    TITLE
    Procedure for obtaining useful data associated with the pattern of heart rate variability
    OBJECT OF THE INVENTION
    The proposed invention consists of a procedure and the resulting multidimensional graphic or code to characterize and qualify dissipative cyclical or quasi-periodic systems of any nature, whether natural or artificial, in a compact, universal, adaptable and accessible way for anyone with minimal training. It is based on the following elements:
    (i) A generalized formulation of the normalized and sequential variability of the time intervals between subsequent cycles, using a key or key of fifteen numbers (or parameters) that determine the particular algorithmic expression of said sequential variability. This expression can also be considered as a generalized transform of the original numerical series of time intervals. In addition, this transformation is umvoca and establishes a sequential partition of the original series, selecting subsequent groups of N elements in an order pre-established by the chosen key. Such groups can be considered vectors in an N-dimensional space.
    (ii) A spatial representation in N-dimensions of the vectors or points generated by the transformation defined by the chosen key. In particular, for N = 4, the 3 dimensions and the color can be used. In the case N = 5, the 3 spatial dimensions, the color, and a partition of the fifth dimension that can be dynamically represented can be used, for example by a time sequence (video) of the three-dimensional representation corresponding to each interval of said partition , using time as the representation dimension of the fifth dimension.
    (iii) The identification of common patterns or spatial correlations ("clusters") between groups of points, such as axes or crystallographic planes, generalized surfaces, etc., obtained through the representations of a sufficient number of systems of the same nature .
    (iv) The comparison of an individual system of a certain nature with said common patterns, and the determination of the presence of said common patterns in the individual system that is to be qualified.
    Contrary to any other method that uses other transformations, such as the Fourier transform or the "Wavelet Transforms" of any kind, the proposed procedure is intrinsically adaptive, and contemplates the accumulation of knowledge through experience. It also allows you to use the instantaneous time scale, or any other that can be chosen locally, as the main reference scale of the analysis; This makes it possible to identify universal variability patterns, independent of other time scales or exogenous variables, which are inaccessible to other transformations such as Fourier, since these superimpose all temporal scales regardless of their sequence of occurrence.
    STATE OF THE TECHNIQUE
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    The dissipative systems are characterized by specific periods of time, whether constant or variable. Therefore, they belong to a special class of dynamic systems whose degrees of freedom are critically restricted or limited. Contrary to what happens with generic dissipative dynamic systems, which exhibit chaotic behavior [see Grassberger and Procaccia, 1983 (a) and (b)], attractors of cyclical dissipative systems are well defined and generally simple. Such systems are, in general, mechanical or biomechanical engines, which exchange contributions or demands for energy or work with the environment, be it a car engine, a wind turbine, or a heart. However, while constant time periods characterize both artificial clocks and natural motors, designed specifically to minimize variability - or that are not subject to changes in demands -, engines subject to varying demands also have variable time periods. This strategy is the most economical way to guarantee the adaptability of an organism (for example, an animal) or a machine (for example, a car) equipped with these engines.
    The ability to adapt to changes in demands is probably the main priority of natural and artificial systems equipped with movement capabilities. These systems are inherently dissipative. On the other hand, all artificial systems designed to work under constant demand, also need to accelerate from a pause or decelerate to stop. The degree of adaptability, or how a system responds to a given demand - which in turn has its own characteristic time - qualifies its strength, its robustness, or its health, and usually determines its survival.
    In general, adaptable mobile systems experience periods in which they are at rest: for example, ralenri regimes of internal combustion engines to avoid frequent starts and stops, animal sleep, etc. In these periods of inactivity, the system extends the characteristic period of time, exclusively to balance the internal dissipation. However, the complexity of the most adaptable mobile systems requires that its internal dichtic engines have a limited number of degrees of freedom. These limited degrees of freedom, which guarantee adaptability, are almost incompatible with constant periods of time. The variability exhibited by an idle system openly reflects its internal characteristics and describes its commitments, often hidden under a general operation. For example, the 2-stroke engines exhibit a strong variability when they are on the track, and the heart rate variability can be observed during deep sleep or in a situation of deep relaxation, much better than during exhaustive exercise in healthy subjects.
    A wide range of methods have been proposed to characterize dissipative dynamic systems: description of foreign attractors, Lyapunov exponents, entropy analysis, power laws, Fourier analysis, multidimensional phase portraits, etc. However, none of these methods provide exhaustive information or portraits for the dissipative systems, both typical and adaptive, due to their inherent nature. There is no compact equivalent to a QR code or a graphic to provide complete information on the nature, adaptability, health status and internal characteristics of a public dissipative system.
    A very general case of systems with high variability and adaptability is that of living organisms with circulatory system and mobility. The circulatory system of living organisms is an autonomous mechanical system delicately coupled with the system
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    respiratory, and both developed by evolution in response to the complex oxygen demand patterns associated with movement. Circulatory health is based on adaptability, which implies inherent variability. In the exemplary embodiment of the present invention described in the following, an N-dimensional graph calculated by the described method and representing heart rate variability reveals two universal arrhythmic patterns as specific health signs: one reflects the ability to adapt cardiac, and the other the symptom of the heart-respiratory rhythm. In addition, at least three arrhythmic universal patterns are identified whose presence increases progressively to the detriment of the two health patterns, in certain pathological situations (myocardial infarction, heart failure, and recovery after sudden death). The presence of the identified universal arrhythmic structures, together with the position of the center of mass of the heart rate variability graph, provide an unprecedented quantitative assessment of the pathology-health gradient.
    The heart is the first organic autonomous volumetric pump developed by nature, and allowed the great leap that mobility meant for living organisms. The basic functions of the heart are contractility and heart rate. Heart rate variability, or HRV, represents a degree of vital freedom from the evolution of an autonomous organism, with a circulatory system (Malik, 1996) that allows the immediate adaptive response to oxygen demand. These demands can have infinite profiles of variability. However, by principles of economics, nature responds to demands by creating a limited number of structures or patterns, instead of giving different answers to the infinite possible solutions. Therefore, it is worth asking: do you have to measure predetermined structures or patterns in the HRV , would they reveal general modes of adaptability (health) or failure (pathology) some of these structures , can they appear combined , which would be their size and relative relative weight with respect to the rest of the events This is complementary or antagonistic structures , etc.
    The respiratory system, complementary to the circulatory system in mobile organisms that live in the air, is also a volumetric pump (formed by the thorax and lungs) with another characteristic time and operation with an appreciable level of coupling to the heart. The flexible relationship between heart rate and respiratory rate implies a specific HRV (Raghuraj et al. 1998; Weese-Mayer et al. 2006; Pinna et al. 2006; Guzik et al. 2007; Grossman & Taylor, 2007). Heart rate is also subject to other endogenous influences, such as digestion (Heitkemper et al. 2001), age and sex (Ryan et al. 1994;
    Umetani et al. 1998; Kuo et al. 1999; Antelmi et al. 2004; Sacha et al. 2014), biochemical mechanisms (Evans et al. 2001; Liu et al. 2003; Chow et al. 2014), or psychic activity (Berntson et al. 1997; Katon et al. 2003; Carney et al. 2005; Thayer & Lane 2007), whose characteristic times are decoupled from the autonomous control of heart rate.
    When such influences reach or exceed external demands (including the circadian cycle, Molnar et al. 1996), the organism may present with pathological arrhythmias. However, life-threatening ones are those that present frequencies higher than respiratory rate (Berntson et al. 1997; Kleiger et al. 1987; Bauer et al. 2008; Goldberger et al. 2008). The example of application of the present invention described herein reveals that in the human species there may be universal specific arrhythmic sequences (internal structure) as a co-evolutionary product of the autonomic nervous system (sympathetic).
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    parasympathetic), and that these sequences can be specific to the healthy heart or the pathological heart.
    The need for non-invasive, precise and specific tools for diagnosis and prognosis is a priority factor for the advancement of Medicine. The compact graphic representations of the physiological systems have been a great help for doctors in terms of details and accuracy in diagnosis. Obtaining images of internal systems and tissues (for example, ultrasound, computed tomography, or nuclear magnetic resonance imaging) has changed our lives. Therefore, in cardiac quantification (CQ), echocardiography (echoCG) has represented a very important development. However, electrocardiographic data series provide irreplaceable complementary information, not accessible to echoCG in many aspects, such as the appearance of temporal pathological patterns and behaviors, ectopic rhythms, etc. The reduction of the time series of a Holter (HR) heart rate register to a compact graphic representation (or graphic) may have a complementary climatic value to that of the echoCG. Preliminary considerations are necessary: in order to determine the appearance of generic patterns that reflect normal or pathological characteristics, and to improve their specificity, the graph should reduce to a minimum (i) individual differences in weight / size, sex, age, etc., (ii) long-term cardiac frequency influences, with times comparable to or greater than those of respiratory rate, and (iii) exogenous and endogenous influences, other than those of cardiac or circulatory origin. The graphic representation sought must provide the complete information provided by the sequence of all cardiac events (for example, the complete sequence beat to beat, or the RR intervals). After an exhaustive review, no evidence has been found of the existence of a representation with all those characteristics sought, to date.
    Among the different approaches to study HRV, including tools for the analysis of dynamic systems and chaos (Kurths et al 1995; Ivanov et al 1999; Pikkujamsa et al. 1999;
    Balocchi et al. 2004; Gao et al. 2007), multiscale entropy analysis (Gao et al. 2015), frequency domain analysis (Parati et al. 1995; Aubert et al. 2003; Martinmaki & Rusko 2008; Shiogai et al. 2010), and chains de Markov (Andeao et al. 2006, Sandberg et al. 2008;
    Saglam et al. 2015), the Poincare maps of the RR intervals provide useful compact graphs for HRV analysis (Kamen et al. 1996; Brennan et al. 2001; Guzik et al 2006; Piskorski & Guzik 2007; Khandoker et al. 2013; Burykin et al. 2014). The high stochastic heterogeneity that HRV presents in general is indicative of the existence of strong internal structures presumably universal. Under this premise, the most appropriate tool for HRV would be an analysis of standardized time sequences, against global stochastic tools, or Fourier transforms. In fact, the Fourier transform can be definitively excluded as a suitable tool to analyze universal variability patterns, since it essentially analyzes the content of fixed time scales (or frequencies) in the entire register, so that it mixes the patterns of the same potential nature for high and low heart rates, in the same HR.
    The Poincare representation (or return maps) provides a direct sequence analysis of the RR series to identify temporal patterns. However, the limitations of the representation of the Poincare return maps in the (2D) plane (Khandoker et al. 2013) hide much of the potential of this type of graphs.
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    DESCRIPTION OF THE INVENTION
    It is a first object of the present invention a procedure for the description, compact graphic representation, and graphic identification of specific patterns of operation of quasi-periodic clinical systems, such as but not restricted to alternative combustion engines, rotary machines, or organs. Biological as the heart, characterized by:
    to. The measurement and recording of a number M of consecutive time intervals {X.}. = M
    corresponding to complete cycles of the system, machine or organ to be qualified, with a precision better than 10% of the average value of the cycle time, preferably better than 0.01% of said average value, with M being greater than two;
    b. The calculation of the sequence of consecutive vectors d} of N components
    according to the algorithm or mathematical transformation defined by the expression:
    I m (m ^ l,
    d = E _ H) "X
    I "= 0
    V "J
    N0, j + K0 j + "+ k-e, + J o
    e / X) 1 (X)
    d / Ni, j + Ki / N2, j + "+ k- £ 1 + K2 '
    1k + N-1
    with the following notation:
    XL = tX + „•
    (m ^
    h = 0
    V "J
    m!
    "! (m -")!
    The following parameters are whole numbers and your choice determines the final form of the mentioned transformation:
    {m N N N N £ £ £ J J K K K}
    ! niiy iiy, ^ iy 1 ^ iy 2 ’° 0’ ° 1 ’° 2’ ^ 0 ’^ 15 JV0’JV1’ 2 J '
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    where:
    - m is a natural index related to the order of the discrete variation that is calculated and therefore would be related to the order of the finite difference between m subsequent values of a discrete function,
    - N is the dimension or number of components of each dj vector, being
    N> 2,
    - N0, N1, and N2 indicate the number of values used to calculate the corresponding local average indicated in the general formula of the algorithm,
    - e0, el, e2 have binary values 0 or 1, and indicate whether the corresponding elements are mobile in the calculation of each of the components of the vector dj,
    - J0 and J1 indicate the delay or advance of the first element that is taken in the calculation from the index j,
    - K0, K1, K2 indicate the delay or advance of the first element taken in the corresponding local series to calculate the indicated local average.
    The second object of this invention is the graphic representation in two or more dimensions of the position of the point indicated by the values of the components of each of the dj vectors calculated as described above, for example, but without restrictions, using
    the two spatial dimensions of a screen, the numerical value of the color, and the size of a circular marker to represent four dimensions, or five if axonometric or conical projections are used in the plane, allowing in this case that the user can visualize the extension Volumetric representation of the three-dimensional representation generated for the identification of specific graphic patterns exhibited by the system studied.
    A third object of the present invention is the automatic determination of the existence of behavior patterns defined by a vector function A = aj} = N, where
    Elements a.j are fixed fixed values or functions of one or more user-defined variables, without any restriction, according to the following procedure:
    to. Calculation of the generalized angle q whose cosine is given by: cos (q) =
    A d All -lidII
    where dj vectors are calculated as described above, and the symbol
    means the general norm of a vector in N dimensions, such that
    / 1/2
    IN
    A =
    Laj V j = 1 J
    b. Calculation of the number of events M 'such that the angle q is less than a tolerance
    predetermined, being 0 <e <1, preferably 0 <e <0.1, so that function A must be explored in its space of existence to find those events in which q <e; that is, that M'depends on the choice of values
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    concrete variables of the function A. In particular, if A is constant, M 'is unique.
    C. Use of the M '/ M coefficient as a qualification index of the system as it exhibits predetermined behaviors defined by vector A at greater or lesser intensity, where such behaviors may be desired or undesired.
    Also considered object of the present invention is a procedure for the quantitative determination of the adaptability of quasi-periodic machines or systems, characterized by:
    to. Subject the machine or system to a variation of the operating regime, either by progressive increase or reduction of energy consumption, by measuring the consecutive valuesX. of the time intervals corresponding to each cycle.
    b. The use of the M '/ M coefficient, calculated according to the third object of this mentioned invention, from the series {X.}. = M with the specific definition of the vector
    A = t {(N +1) / 2 - j} J = l_N, where N> 1, and said vector corresponds to a homogeneous acceleration or deceleration of the system if t = 1 or t = -1, respectively.
    Another object of this invention is a procedure for calculating a qualification index of the condition or cardiac health of an individual, characterized by:
    to. The measurement and recording of a number M of consecutive time intervals {Xi}. = 1 M
    between "R" peaks of the "pQSRt" cardiac complex with an accuracy better than 10 ms, M being greater than two;
    b. The calculation of the series 8} of consecutive vectors of N dimensions or
    L j Jj = 1, ... M-N
    components as described in the first object of this invention, calculated according to the following definitions of the fifteen parameters:
    (r 'I
    m = 0 N = 5 No = 5 N = 5 N2 = 5
    ^ o = 1
    e = 1
    <e2 = 0>
    ^ 0 = 1
    V = 1 Jo = 0
    JJ = 0 K0 = 0
    K1 = 0 K2 = 0
    L 2 J
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    so that the definition of the calculation algorithm according to the first object of this invention is finally 8, = X. ,, (X) r - 1}.
    J l J + k IN, j k = o, ..., N — 1
    In the same sense, the object of this invention is a procedure for calculating a qualification index of the condition or cardiac health of an individual, characterized by:
    to. The calculation of the number of events M 'according to the third object of this invention from the series Xi}. = 1, M with the concrete definition of the vector A = t {(N +1) / 2 - j} j = 1 N,
    where N> 1.
    b. The use of the mS1 / M index, with mS1 = M 'calculated in the previous point, directly or in combination with any other qualification index, to determine the level of cardiac health, for example but not restricted to the use of mS1 / M as Direct index of qualification of cardiac health.
    In the same way, a procedure for calculating a qualification index of the cardiac condition or health of an individual, characterized by:
    to. The calculation of the number of events M 'according to the third object of this invention from the series {Xt}. = 1 ^ with the concrete definition of the vector AN = t {sin (2 ^ j) / N}. ^ N,
    where N can vary between N = 3 to N = 12, which corresponds to a sinusoidal modulation of the heart rate combined with the respiratory rate, where t can have any value, for example, but without restriction, 1 or -1.
    b. The use of the mS2 / M index, with mS2 = M 'calculated in the previous point, directly
    Or in combination with any other qualification index, to determine the level of cardiac health, for example but not restricted to the use of mS2 / M as a direct index of cardiac health qualification.
    Likewise, a procedure for calculating a qualification index of the cardiac condition or health of an individual, characterized by:
    to. The calculation of the M '/ M coefficient according to the third object of this invention from the
    series {X,}, M with the concrete definition of vector AN = 11—1,1, Q, ..., or |, where N
    it can vary between N = 1 to N = 20, corresponding to a compensated ectopic beat, and where t can have any value, for example, but without restriction,
    1 or -1.
    b. The use of the mE / M index, with mE = M 'calculated in the previous point, directly or in combination with any other qualification index, to determine the level of cardiac health, for example but not restricted to the use of mE / M as Direct index of qualification of cardiac health.
    Likewise, it is also the object of this invention a procedure to calculate a qualification index of the condition or cardiac health of an individual, characterized by:
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    to. The calculation of the M '/ M coefficient according to the third object of this invention from the
    series}, = ,. M with the concrete definition of the vector AN = t | n, - 1, ..., - lj, where N
    it can vary between N = 2 to N = 20, corresponding to a regular paroxysmal tachycardia, and where t can have any value, for example, but without restriction, 1 or -1.
    b. The use of the mTP / M index, with mTP = M 'calculated in the previous point, directly or in combination with any other qualification index, to determine the level of cardiac health, for example but not restricted to the use of mTP / M as Direct index of qualification of cardiac health.
    Any use is also considered object of the present invention to diagnose the functionality, pathology or level of quality or health of a machine or organism equipped with a motor or organ of public operation, which uses any of the procedures described above.
    Another object of this invention is any use used to make prognostic judgments about the functionality, pathology or level of future quality or health of a machine or organism equipped with a motor or organ of public operation, using any of the procedures described above.
    A final object of this invention is any use for the description, compact graphic representation, and graphic identification of specific patterns of operation of dynamic systems, such as but not restricted to economic systems such as the stock market, which uses any of The procedures described above applied to:
    to. Sequential series of values {X}. = 1 M, obtained by measuring the value of a certain
    characteristic amount of the system at regular time intervals or with a certain frequency.
    b. Sequential series of values {X}. = 1 M, obtained by measuring the value of a certain
    characteristic amount of the system following certain temporary guidelines, not necessarily regular or constant, for the acquisition or measurement of the securities
    X.
    DESCRIPTION OF THE FIGURES
    Figure 1. Representation of the distance of the vectors for a particular case of heart failure. Some characteristic lines of this specific case and their mathematical expressions are shown. In addition, the central regions of the graph are expanded with the areas of interest highlighted to appreciate the details.
    Figure 2. (a) 4-dimensional graph of the location of M - 3 vectors {D,} M — N + 1
    normalized with the global average of a normal HR (a healthy adult). (b, c) The same for two subjects with chronic heart failure (three angles of vision of each). The graphs to the right in (b, c) show universal patterns when projected with the angle
    appropriate. (d) Identification of the main lines of insufficiency in two different individuals with HF.
    Figure 3. (a) The different regions of normal activity and HF found in this study: NSR (normal activity, green; Fantasia, cyan); HF (magenta). (b) The different 5 regions occupied by the four situations found in this study in the multivariable space (log10 (^ 1), log10 (M), log10 (FN)}: NSR (na, green; Fantasia, cyan) ; MI (blue);
    SD (red); HF (magenta). For clarity, the left panel provides three situations (NSR n. A., Myocardial Infarction and HF), the central panel four (NSR n. A., MI, SD and HF), and the right panel all the situations studied. N = 5 in all the results of this figure.
    10
    MODE OF EMBODIMENT OF THE INVENTION
    A Poincare map is a graph consisting of the representation of a return map or sequential trajectory of the values that a certain variable presents in consecutive cycles. Specifically, a two-dimensional (2D) return map is a flat projection where the 15 complex trajectories, with multidimensional characteristics (that is, specific arrhythmic sequences) appear superimposed and indistinguishable. This invention allows a generalization of said return maps. Among other possibilities, a normalized variability can be formulated using a moving average of order N = 5. This formulation corresponds to the following choice of the fifteen values that define the base algorithm of the procedures described in this invention:
    m = 0 N = 5 N = 5 N1 = 5 N2 = 5
    £ 0 = 1
    e = 1
    <e2 = 0> £ 0 = 1
    £ = 1 J0 = 0
    J1 = 0 K = 0 K1 = 0 K 2 = 0
    Through this choice, which we will call NL (Local Normalization) an expression of
    sequential variability S}, with
    L JJj = 1, ..., M-4
    d = fc dX) 1-4
    N-1
    X
    k = 0, ..., 4 '' lN, j
    ■ 'L X +,
    Y
    i = 0
    whose representation meets the aforementioned requirements for an ideal representation of cardiac function in terms of compactness and integrity, and also reduces to a minimum
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    exogenous and accidental influences thanks to the local normalization resulting from the values {Xt} = 1 M. The proposed procedure can be applied to any of the
    components of the complex ca ^ aco "pQRSt", although this example focuses on the analysis of the main component, the series of RR intervals. In general, exploring the range of order from N = 2 to N = 100, it is observed that a measure of quantification of the global variability, such as the norm or argument of the mass center vector of the point distribution
    defined by the vectors dj = X ax N j - 4, = 0. .N-1 -fe ..} repeatedly presents a
    minimum for N = 5 in the case of healthy individuals. This could be due to the fact that the average heart rate is approximately five times higher than the respiratory rate in our species, a fact that results in a subharmonic of order five of the HRV, thus maximizing the overall compensation when N = 5. Yes This hypothesis is correct, the deviations from normality should be optimally distinguished using the order N = 5.
    Fortunately, N = 5 gives rise to the most complete graph, with possibilities of complete graphical representation, among all possible orders. Indeed, the expression
    dj = ^ Xj + ^ X ^ jN. - l} allows its representation 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. This is so because the value of any of the N elements can be obtained from the other N-1 since the sum of the N elements is always null, by definition. The resulting four-dimensional sequential information vector, which can be represented graphically in 3D + color, gives rise to a valuable descriptive and comparative tool from the weather point of view. This graph allows the identification of sequences that originate universal patterns, whose distribution and density provide immediate and complete information on the state of cardiac function. Using the procedure described, up to five universal sequences have been identified, especially visible in the graphic representation (see an example in Figure 1, with N = 4, and other examples in Figure 2, with N = 5 and another choice of parameters ), but not only present when N = 5: all can be found in larger orders, and in fact their general expressions have been obtained for any order N.
    The universality of these sequences has been verified in a series of HR databases, with 133 records grouped into four basic situations with distinctive characteristics: (i) individuals in normal sinus rhythm (normal sinus rhythm, NSR) during their normal activity, or at rest in supine position, while watching the movie "Fantasy"; (ii) ischemic cardiopate, specifically myocardial infarction (myocardial infarction, MI); (iii) heart failure (heart failure, HF); and (iv) sudden death recovery (sudden death, SD). When applied to the aforementioned databases, the percentage of occurrence of each of the five sequences, and the primary variability (whose definition will be seen later), provide a specific and specific set of measures capable of assessing the state of the heart in the records included in the databases used and that are publicly available (see Databases used below).
    A Holter (HR) register is a set M of consecutive values that correspond to the RR intervals, which can be expressed as {xj} = m- It is easy to show that the vector
    generalized distance in N dimensions from any point {X;. +.} t formed by N
    RR intervals to the identity line (zero variability line, Piskorski & Guzik 2012),
    5
    defined by the identity vector {1,1, ..., 1} N, is defined as
    D = {x + k
    ] L] + k
    X
    N, jik = 0.
    . The
    , N-1
    RR interval sequence of a healthy "dancing" heart around the identity line, but would never rely on it (even in extreme situations of total relaxation or extreme exercise, there is a mathematically different variability from zero, although RR seemingly constant). The locally normalized expression of said generalized distance vector is precisely the expression of the choice of parameters NL, that is to say
    S, ° (X) r D, = {X) r X ,,, —1}, where the basis of normalization is the local average
    ] / N, j] L IN, j] + k Jk = 0 N — 1
    X
    N, j
    = £ X-. ,
    i = 0
    In Figure 1, a simple color code varying between 10 0 and 1 (the "Hue" code of the Mathematica® 9.0 program) has been used as the fourth dimension.
    Another choice of parameters that has been used for this study is the following:
    fifteen
    m = 0 N = 5 No = 5 N1 = M N = M
    £ o = 1 e1 = 1
    <e2 = 0>
    £ 0 = 0
    £ = 0 J0 = 0
    J1 = 0
    K0 = 0
    K1 = 0 K2 = 0
    L 2 J
    which gives rise to the following expression
    S = {X) _1 X. + k - (X) “V X)}
    ] L ! M j + k / m / n, j Jir-
    At this choice
    'M] + k / M IN, Jk = 0, ..., N — 1
    of parameters we will call it NG (Global Normalization), and the particular expression of S. for this election we will call it A .. This expression has the following relations
    trivial with
    j
    DJ = lx + k - <Xn, j Ik = 0 ... N — 1 and with Sj = X — 1}
    k = 0, ..., N — 1
    A] 2 X> M DJ - and A] = <X) M1 <X.fi.
    Figure 2 shows the graphical representation of three HRs, analyzed with the proposed procedure, for N = 5: Fig. 2 (a) represents a healthy subject, and fig. 2 (b, c) two patients with 20 chronic heart failure. While in the healthy individual it results in a dense form and
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    Compact around the origin, individuals with HF show distinctive spatial lines, which follow preset sequences. In view of this figure, some immediate conclusions can be drawn. First, the exposed lines 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 angle of vision (angle of projection on the plane of view) that systematically reduces the main lines straight and flat to only three in all cases [see Fig. 2 (c)].
    As expected, the standardized Poincare sections of the NSR database variability (seemingly non-pathological) are relatively centered and homogeneously distributed around zero, showing a mathematical compensation (apparently) with a certain randomization, that is, a more or less spherical nucleus with random distribution, approximately Gaussian. A more detailed observation reveals that all cases show a clear structure in the fourth dimension (color), along a specific direction. It is important to note that this address is exactly the same for all subjects, regardless of the form of the distribution of points that represent the RR intervals. This fact points to the existence of a subharmonic, probably related to the breathing cycle, which reflects an automatism of the sympathetic-parasympathetic system. This effect is relatively rare or almost absent in individuals with MI, and is not found in individuals with HF and SD. The existence of some individual from the NSR database (about 12%) that presents the characteristics present in the HF is quantified and discussed later, and supports the fact that the presence of sinus rhythm does not rule out the presence of any heart disease.
    The "Fantasia" database shows the same characteristics (Iyengar et al. 1996; Schimitt et al. 2007) as the individuals in the NSR database, and almost the same percentage of subjects with HF characteristics. In addition, older healthy subjects clearly show less variability than younger subjects.
    Almost 70% of the subjects in the HF database (Baim et al. 1986), and many of those in the SD base studied (Taddei et al. 1992) show the same distinctive lines mentioned above. The percentage of subjects with homogeneously distributed point clouds is inversely proportionally when compared to individuals in the NSR database: less than 20%. On the other hand, the repetition of the patterns and their extension meet the universal characteristics of the HRV. Finally, the individuals of the SD database used in this study show variable patterns, totally different and visibly more irregular than any other group. Some of them have the same lines as individuals with HF, but most of them show a very complex and apparently chaotic structure.
    As a first feature of the graphic representation proposed in this invention with both the NL and NG (Sj or Aj) choice, it is centered around the origin by
    definition, so that the density of points in different areas can be considered as a specific identifying signature of variability. Therefore, the norm of the standardized vector that defines the center of mass of the graphic representation would be a primary measure of the variability for a subarmonic order, and of its degree of global compensation (for example, in a 24-hour HR for a circadian cycle). However, our experience shows that the entire registration of Aj vectors results in inadequate compensation, due to the inherent nature of the Poincare representation:
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    it can be verified that the same arrhythmic RR interval Xj appears as a common component in N-1 vectors Dj_k around the identity line, which often produces an apparent global compensation. To avoid this, a subset of the entire series can be extracted by taking the index j in jumps of N elements (that is, j = {l, 1 + N, 1 + 2N, ...}), where each Xj appears only one time. The distance from the center of mass of this subgroup to
    origin, whose graphic representation is almost indistinguishable from that of the entire series except in its different densities, is given by:
    F N =
    N _1 f M _ N +1 A
    And EA, .k
    k = 1 ^ i; N J
    -i1 / 2
    2
    M _N + 1
    where you say the component of the vector Di, and the notation of the summation EDik indicates
    i; N
    jumps from N to N in the index i. This coefficient, which we call primary variability (PV), can be represented for each individual as a function of N. Our experience shows that FN is approximately 10 to 100 times larger than the distance to the origin center of mass of the original set. complete, a fact that greatly amplifies the significance of FN as defined. It also depends on the number of
    beats in the series. In general, the minimum variability corresponds to the individuals of NRS (Fantasia), closely followed by the variability of individuals with MI. The maximum variability corresponds to SD and HF, its distribution being quite similar. In intermediate values is the variability of the subjects of the NSR database with normal activity. FN is shown as a new quantitative measure with extraordinary capacity to differentiate patients with different cardiac function disorders, in combination with the proposed graphic representation and the arrhythmic structures that derive from it.
    Then we offer a first classification of the different patterns that can be grouped into universal sequences or lines. On the other hand, we are going to demonstrate that the density of points along those lines provides a valuable measure of the state of cardiac function. In fact, when our procedure is applied to the databases that we have used, which are freely available, it results in results with high descriptive specificity for each group. As a first approximation, in this work we limit ourselves to the identification of the simplest primary arrhythmic sequences, which can be expressed in the form of a line, defined by the vector
    An = t {a.1, Oj, ..., aN},
    parameterized by an arbitrary t variable. Of course, the position vector of a real point (beat) on a specific arrhythmic line can correspond to any positive real value t. This reflects the intrinsic capacity of the described procedure: an arrhythmic line (or anomaly), which can be mathematically and universally expressed, brings together all arrhythmias of the same nature, regardless of the heart rate and the amplitude of the variability. Several primary sequences have been deciphered and will be described below and the density of points along the corresponding lines (sequences) has been calculated.
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    These results do not exclude the existence of other more complex sequences, with associated specific characteristics, which will be determined in future studies.
    An immediate way to assess the density of points corresponding to a specific sequence is to quantify its presence in% throughout the total record of the RR series. Since the presence of a specific sequence, obviously, is not an exact or uniform amount in all situations or in all individuals, it is necessary to use statistical means to determine the greater or lesser presence of said sequence in a given cardiac 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 = i / Mb
    front yi for the corresponding database, where yi is the percentage of presence of the
    particular sequenceAN = t {a1, a2, ..., aN} of an individual, i is the range of that individual in
    particular, based on your yi score, and Mb is the total number of individuals in the base of
    data. This would be given for each sequence, and would be analyzed in combination for each situation.
    Arrhythmia A1.- A healthy individual with NSR must exhibit an intrinsic capacity to respond to any demand of the organism, through regular accelerations and decelerations of heart rate directed by sympathetic / parasympathetic balance. This capability should 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 = ... 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 accelerated heart rate, A1 + = t {l. 5.0.5, —0.5.1.5} would be formulated, with t> 0; for N = 5 and decelerated heart rate A1— = t {- 2, —1,0,1,2}, with t> 0; etc., where values greater than t indicate
    a steep rise or fall, or a more pronounced ramp, of the heart rate, without changes in the functional structure of the variability.
    This is the dominant form of cardiac variability (HRV) in normal individuals of the NSR database, MIT BIH, as shown in Table 1. In effect, this is reflected in a slightly ellipsoidal shape around the origin in the direction of the A15 line of any 4-dimensional graph of a fifth-order Poincare map, representing the HRV of a normal individual with NSR, as we have proposed [see fig. 2 (a)].
    More importantly: as the presence of this form of HRV decreases, other forms of pathological arrhythmias, which will be described below, increase approximately in the same proportion. This finding points fundamentally to a basic organic fact: this arrhythmia is, in reality, the basic degree of freedom of HRV, and reflects the adaptability of a healthy organism. If any pathological situation depresses or limits this degree of freedom, other forms of HRV will be presented to compensate for this deficiency. It is important to note that these alternative forms are not arbitrary, especially in the individuals included in the HF database. In fact, they can be considered as universal. Consequently, the specific form of these alternative HRV patterns must be linked to the specific affectation of an organism, opening the door to new forms of rapid non-invasive diagnosis.
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    Arrhythmia B1 (compensated ectopic beat). The 4-dimensional graphics of the HRs belonging to individuals with HF, usually have three lines (Figure 2 (d)) that can be easily identified as (see Figure 2 (d)):
    B1S1 = 1,1,0,0}, B152 = t {0, -1,1,0}, B1
    5.3
    t {0,0, -1,1}
    First, these three sequences actually belong to a single class of the type
    t {..., 0,0, -1,1,0,0, ...}
    Second, the average value of the four components is zero for any N> 2, and therefore these sequences can be considered "compensated." This means that the last point of the Poincare section of the first order is based on the identity line. In other words, the arrhythmic sequence can also be considered "closed" or "completed", since the last interval lasts at the same time as the local average. It is noteworthy that the number of intervals with zero variability, which surround the sequence {- 1, 1} in most cases is greater than two (that is, the line {1, 0, 0, - 1} does not appear clearly). Therefore, it can be concluded that there is a specific sequence described by the following line:
    B1 = t {.., 0,0, -1,1,0,0, ...},
    which presents a clear ubiquity: this type of arrhythmia is definitely characteristic and clearly dominant in individuals with HF, with an average presence around an order of magnitude greater in HF than in SD or MI, although it is also dominant in these subjects. While the primary (PV) variability of individuals in HF and SD databases is large and very similar, what really distinguishes individuals with HF from those recovering from SD is the presence of arrhythmia B1, much older in the first. These arrhythmias correspond to compensated isolated ectopic beats; In this work, we do not intend to identify its specific cardiac origin, either supraventricular or ventricular, since its nature may be associated with another characteristic not yet studied.
    As can be seen, this analysis can provide a new basis for developing a general classification based on the inherent and strongly compensated nature of these arrhythmias and their ability to be reduced to a single universally expressible structure. On the other hand, these are relatively rare or absent arrhythmias in the individuals in the NSR database: in fact, ectopic beats may appear in the records of normal individuals, but they are relatively rare. However, this type of arrhythmias is more present in the records of awake individuals at rest and in supine position ("Fantasia" record cited below) than normal individuals during normal activity. However, 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 ability to adapt without problems to the demands of normal activity (adaptability associated with the presence of A1 arrhythmias). 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 opposite presence of arrhythmias A1 and B1 is clearly illustrated in Figure 3.
    Arrhythmia B2 (paroxysmal regular tachycardia). In the graphic representation we can find the sequence described by [See Figure 1 (a, b)]:
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    B25l = t {4, -1, -1, -1}
    In many cases an additional line appears [see Figure 1 (c, d)]:
    B25.2 = t {-1, -1, -1, -1}
    First, the appearance of these sequences introduces an additional source of randomization, when combined with A1. This provides an additional adaptive capacity, which on the other hand can be very limited (more intense insufficiency would appear) by decreasing the presence of A1 arrhythmias (normal adaptability) in individuals with cardiac pathologies. Second, none of these sequences is compensated. This means that higher order N values must be considered to find compensated or terminated sequences. In this case, both B251 and B25 2 are actually part of the compensated sequence:
    B 2 = t {4, -1, -1, -1, -1}
    which actually belongs to a sixth order Poincare section (that is, N = 6). It is noted that the compensated sequences are described by lines of the type:
    B2 „= t {(N - 2), - 1, ..., - 1},
    for example:
    B 26 = t {4, —1, —1, —1, —1}
    B27 = t {5, -1, -1, -1, -1, -1}
    B 2S = t {6, -1, -1, -1, -1, -1, -1}
    B 29 = t {7, -1, -1, -1, -1, -1, -1, -1}
    etc. All these sequences are almost as present as the B1 arrhythmia in the HF, although the presence of this type of arrhythmia decreases as N increases. This sequence is hardly identifiable if N increases above 10. Actually, the Arrhythmia with the greatest presence is B26, so we call this type of arrhythmia generically B2, instead of B26. The analysis of the presence of this arrhythmia in HF shows that it is so ubiquitous
    as B1, with different dominance of one over the other depending on the individual. This sequence is compatible with a regular paroxysmal tachycardia after a pause, attributable to an atrio-ventricular block, which in some cases leads to a Stokes-Adams smdrome. Table 1 shows its frequency of presentation in each situation. Like arrhythmia B1, arrhythmia B2 is characteristic of HF. It is important to note that this arrhythmia represents an appreciable pause, followed by a proportional series of rapid frequency to compensate for the pause.
    Arrhythmia B3 (regular paroxysmal tachycardia II) .- Several alternative sequences to B2 can be identified as:
    £ 36! = t {-1,4, -1, -1, -1}
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    B3V = t {-1, -1,4, -1, -1}
    B3tJ = t {-1, 1, 1,4, 1}
    B3m = t {-1, -1, -1, -1,4} or
    B371 = t {-1.5, 1, 1, 1, 1}
    B37.2 = t {-1, 1,5, 1, 1, 1}
    B373 = t {-1, 1, 1.5, 1, 1}
    B374 = t {-1, —1, —1, —1,5, —1}
    B37.4 = t {- 1, -1, -1, -1, -1.5}
    etc., which can be expressed for a general index N such as:
    B3Ni = t {-1, ..., - 1, N - 2 (in positioni), - 1, ..., - 1}
    For a given N, all B3 sequences with different indices have the same presence, but significantly less than B2. This sequence could be assimilated to intermediate situations by joining two consecutive sequences corresponding to regular paroxysmal tachycardia with relative pauses as in arrhythmia B2. It is striking that this arrhythmia is less characteristic of FH; in fact, it is as present in HF as in SD, with an almost identical probability distribution. On the other hand, it is significantly more present in NSR than in individuals with IM and in those in the NSR database ("Fantasia").
    In some cases a peculiar sequence appears as "shadows" of arrhythmia B2 [see, for example, Fig. 2 (d)], which can be identified as lines:
    B 3SJ = t {- 4,1,1,1,11 B 3 * 2 = t {1, -U, U}
    B3 * 3 = t {1,1, 4,1,1}
    which can be written, in general, as B 3n i = t {1, ..., 1, - (N - 2) (at position i), 1, ..., 1},
    a complementary sequence in some form of B3N-2, i. We will not provide a quantitative analysis of this type of arrhythmia, given its complexity.
    Arrhythmia A2 (related to breathing). It is a relatively present sequence, although much less than the 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:
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    A2n = t {sin (2p ') / N} j = l_N
    which represents a sinusoidal modulation of the heart rate over a range of N beats. This type of arrhythmia with mathematical compensation should also reflect a physiological compensation. Since it is statistically more frequent with N = 5 than with any other order, it can be concluded that it is related to the average respiratory rate in the human species. Whether or not it is more frequent during sleep than in normal activity will be the subject of future studies. Its presence in the records is analyzed in Table 1. It is relatively dominant in NRS over 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 cardiac health.
    The combination of the densities of each arrhythmia identified can constitute a valuable signature characteristic of a specific situation, thus opening the way to new diagnostic explorations, the meaning of which is beyond the scope of this example of application of the proposed invention. A fundamental finding is the inverse relationship between the presence of certain types of arrhythmia, those that can be considered indicative of "health", and those that can be called "pathological". Specifically, in the analysis of the public databases that are used, the arrhythmia A1 and the arrhythmia B1 are antagonistic: in fact, the relative presence of one over the other is reversed when passing from a normal situation to a pathological state. A universal map is presented in Figure 3 in which the presence of both arrhythmias in the individuals of the NSR and HF databases is analyzed. A very clear difference between NSR and HF can be observed, based on the presence of A1 and B1.
    In addition, the value of the primary variability FN completes the set of characteristic variables to provide the characteristic, distinctive seal of each situation: note in Table 1 that the combinations of the presences of each arrhythmia and the PV form a unique and very firm signature. differentiated from each situation. The difference between MI and SD is FN, small for MI and large for SD. The multivariable probability density function for each situation in the variable space {log10 (Al), log10 (M), log10 (FN)} would be given by the density of the points at which, in space, correspond to each situation , as shown in Figure 3. The weather value of this representation could be enormously significant. In fact, the therapeutic effect of drugs with cardiovascular specific action, aimed at objectives such as the sympathetic / parasympathetic axis (beta-adrenergic blockers), perhaps the renin-angiotensin-aldosterone neuro-hormonal axis (ACE, ARA II, etc. ), etc., could lead to the modification of the relative presence of each arrhythmia and displacing the location of the corresponding graph towards the direction of the NSR region. The potential prognostic value of the results obtained when applying our procedure to HR is evident. The observation of the graphic representation in 4 dimensions of an HR allows the easy and immediate identification of normality alterations in healthy people. Future clinical studies will expand the depth of knowledge learned with the analysis proposed in this invention, such as the identification of new characteristics and new general arrhythmic patterns, related to other pathologies and situations, not necessarily of cardiac origin, for example, diabetes, hypertension , hypothyroidism, or even psychological disturbances.
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    Table 1
     Arrhythmia  NSR (normal 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
     f5  10.1739 2.4295 1.7567 41.2053 48.9619
    To conclude, we have proposed a quantitative procedure not only to provide a universal representation of heart rate variability, but also a potentially useful weather tool to rate heart function, risks, and probably other related health problems. In this work, among the many different, probably existing, universal sequence types, we have mathematically described some prominent patterns, with a relatively simple structure, identifying five general types of arrhythmias. The databases used have allowed us to identify the characteristic signatures of the individuals included in the NSR, MI, HF and SD databases, relating these basic types of arrhythmias to the different situations of cardiac function. As a fundamental result, we have demonstrated quantitatively that two of these arrhythmias are characteristic of the state of health, while three others are pathological. Their relative presence in an individual may eventually be related to specific situations, with the growing chemical evidence provided by this methodology that will accumulate in the future. In addition, the temporal evolution of the armic structures in a patient, visible with the application of our methodology, can provide very valuable information about their status and / or their clinical evolution. We believe that it is an easy application methodology, and that the potential of its results in non-invasive clinical diagnosis and prognosis could be vital.
    Experimental databases used. The Holter (HR) records analyzed in this study have been obtained from Physionet.org (Goldberger et al. 2000). The following databases have been used:
    • Non pathological. Records of normal activity (normal sinus rhythm, NSR): MIT-BIH Normal Sinus Rythtm Database. This database includes 18 long-term ECG records of the individuals referred to by the Beth Israel Deaconess Medical Center in Boston. The individuals included in this database have not had significant arrhythmias. The database consists of 5 men, from 26 to 45 years of age, and 13 women, from 20 to 50 years of age.
    • Non-pathological, awake and at rest, subjected to homogeneous visual information: Fantasia Database (Iyengar et al. 1996; Schmitt & Ivanov 2007), composed of twenty young people (21-34 years of age) and twenty elderly people ( 68-85 years) rigorously healthy and
    in sinus rhythm. Each subgroup includes an equal number of men and women. All individuals were subjected to 120 minutes of rest in supine position while watching the movie "Fantasia" (Disney, 1940) to help maintain the waking state, during the time of continuous ECG registration.
    5 • Ischemic Heart Disease (MI): European ST-T Database (Taddei et al. 1992). It's about a
    project whose objective was to create a prototype ECG database for the evaluation of the quality of ambulatory ECG monitoring systems. Thirteen research groups from eight countries provided 90 ECG records. From these records, we have selected the 29 individuals who had suffered an MI.
    10 • Heart Failure (HF): HR was obtained from Congestive Heart Failure RR Interval
    Database (Baim et al. 1986). This database includes the RR intervals of 29 patients, 34 to 79 years of age, diagnosed with congestive heart failure classes I, II, and III of the NYHA.
    • Sudden death (SD): the HR, used (Greenwald 1986), were obtained from Physionet 15 (Goldberger et al. 2000).
    权利要求:
    Claims (1)
    [1]
    5
    10
    fifteen
    twenty
    25
    30
    35
    Procedure for obtaining useful data associated with the pattern of heart rate variability (HRV), characterized by:
    to. The measurement and recording of a number M of consecutive time intervals [X.}. = M corresponding to cycles of one or more components of a complex
    cardiac "pQRSt" of an electrocardiogram, with an accuracy better than 10% of the average value of the cycle time, and M being greater than two;
    b. The calculation of the variability over said M intervals of a sequence of consecutive vectors [dj} = M_N of N components according to the algorithm or
    mathematical transformation defined by the expression:
    H (m;
    (_one)" ((
    _one
    No, j'V or + Ko
    X .................... Xj + n + ke j _e (X
    _one
    N1> J'V1 + K1
    X
    N2, j + n + k '£ 2 + K2 ‘
    1 k = Ji .... J + N _1
    one
    and the following notation:
    X
    L, l
    = IX,
    l + h '
    with
    h = 0
    m!
    v n
    n! (m _n)!
    where the following parameters are whole numbers and your choice determines the final form of the mentioned transformation:
    [m, N, N 0, N1, N 2, eo, e1, e2, V0, V1, J 0 ’J1, K 0, K1, K 2},
    where, additionally:
    - m is a natural index that represents the order of the discrete variation that is calculated,
    - N is the dimension or number of components of each vector 8j, being
    N> 2,
    - No, N13 and N2 indicate the number of values used to calculate the corresponding local average indicated in the general formula of the algorithm,
    - eo, e1, e2 have binary values 0 or 1, and indicate whether the corresponding elements are mobile in the calculation of each of the components of the vector S,
    - V0, V1 have binary values 0 or 1, and indicate whether the local average is mobile (the value 0 would indicate that the average would not be local, but fixed),
    - Jo and J1 indicate the delay or advancement of the first element that is taken in the calculation from the index j,
    - Ko, K1, K2 indicate the delay or advance of the first element that is taken in the corresponding local series to calculate the indicated local average.
    and where it is plotted in two or more dimensions of the position of the point indicated by the values of the components of each of the vectors Sj.
    2- Procedure according to claim 1, characterized in that it comprises an additional step of comparing the useful data obtained with patterns of
    10
    behavior associated with a vector function A = fij} = N corresponding to
    the parameterization of a cardiac sequence, where the elements a} - are fixed values
    or functions of one or more variables, and where an additional stage of comparing the useful data obtained with said function A is performed, according to the following steps:
    To d
    to. Calculation of the generalized angle q whose cosine is given by: cos (q) =
    All • lidII
    b.
    C.
    where the symbol means the general norm of a vector in N
    dimensions, such that A =
    f N
    to
    1/2
    Vj = 1 J
    Calculation of the number of events M 'such that the angle q is less than a predetermined tolerance e, where 0 <e <1, so that function A is explored in its space of existence to find those events in which q <e ;
    Calculation of the coefficient M '/ M.
    3- Procedure according to the previous claim, where:
    15 a. The calculation of the series d} of consecutive vectors of N dimensions or
    L j Jj = 1, ... M-N
    Components are performed according to the following parameter definitions:
    m = 0 N = 5 No = 5 N1 = 5 N2 = 5
    eo = 1
    e1 = 1
    <e2 = 0>
    £ 0 = 1
    Si = 1 Jo = 0
    J1 = 0
    K0 = 0
    K1 = 0
    K2 = 0 2,
    so that the definition of variability over said M intervals is
    I = k «<X> -1, j -1l = 0 ... n-1 '
    twenty
    4- Procedure according to the previous claim, where the following steps are additionally carried out:
    5
    10
    fifteen
    twenty
    25
    30
    35
    40
    to. Calculation of the number of events from the series Xt}. = M with the concrete definition of the vector A = t {(N +1) / 2 - j} j = 1 N, where N> 1;
    b. Use of the mS1 / M index, with mS1 = M 'calculated in the previous point, for the determination of the existence of behavior patterns associated with the vector function A.
    5- Procedure according to claim 3, wherein the following steps are additionally performed:
    to. Calculation of the number of events M 'from the series {X.}. = 1 M with the definition
    concrete of the vector AN = t {sin (2p) / N} j = 1 N, where N can vary from
    N = 3 to N = 12, corresponding to a sinusoidal modulation of the heart rate combined with the respiratory rate, where t can have any value;
    b. Use of the mS2 / M index, with mS2 = M 'calculated in the previous point, for the
    determination of the existence of behavioral patterns associated with the vector function A.
    6- Procedure according to claim 3, wherein the following steps are additionally performed:
    to. Calculation of the coefficient M '/ M from the series {X.}. = 1 M with the definition
    concrete of the vector AN = 11— 1,1, Q, ..., or |, where N can vary from N = 1
    up to N = 20, corresponding to a compensated ectopic beat, and where t can have any value;
    b. Use of the mE / M index, with mE = M 'calculated in the previous point, for the determination of the existence of behavior patterns associated with the AN vector function.
    7- Procedure according to claim 3, wherein the following steps are additionally performed:
    to. Calculation of the M '/ M coefficient from the series {Xt}. = 1 M with the definition
    concrete of the vector AN = t | n, - 1, ..., - 1 j, where N can vary from N = 2
    up to N = 20, corresponding to a regular paroxysmal tachycardia, and where t can have any value;
    b. Use of the mrF / M index, with mTP = M 'calculated in the previous point, for the determination of the existence of behavior patterns associated with the AN vector function.
    8- Method according to any of the preceding claims, wherein the component of the cardiac complex "pQRSt" is the RR interval of an electrocardiogram.
    9. Method according to any of the preceding claims, wherein the registration of a number M of consecutive time intervals {X.}. = M corresponding to cycles of
    A component of a cardiac complex "pQRSt" is performed with an accuracy greater than 0.01% of the average value of the cycle time.
    5
    10- Procedure according to claim 3, wherein 0 <e <0.1.
    11- Method according to any of claims 6-8, wherein t is 1 or -1.
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    同族专利:
    公开号 | 公开日
    EP3420891A4|2019-10-09|
    WO2017144763A1|2017-08-31|
    EP3420891A1|2019-01-02|
    ES2630834B1|2018-07-10|
    US20190046055A1|2019-02-14|
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