ES2343054B2 - DEVICE AND METHOD FOR THE DETECTION OF THE ALTERNANCE OF VENTRICULAR REPOLARIZATION THROUGH WINDING. - Google Patents

Abstract

Device and method for the detection of alternation of ventricular repolarization through poisoned

This invention features a device and a method for detecting alternation of repolarization ventricular based on continuous time analysis and on Information extraction through poisoning. How main feature, the method has great robustness against the noise and is very robust against frequency variability cardiac due to the normalization technique used. Further, includes a procedure that separates the information from the repolarization of artifacts. By the characteristics above, it is especially suitable for implementation in transportable monitoring devices and devices implantable The decision about the existence of alternation is performed by analyzing a coefficient obtained from the spectral components in the multiples of half of the heart rate from the spectral analysis of the electrocardiogram directly in the time domain doing use of a dynamic poisoning procedure.

Description

Device and method for the detection of alternation of ventricular repolarization through poisoned

Technical sector

The object of the patent is the description of a method that can be implemented in physical devices, both external and implantable in the human body, for the detection of alternating in cardiac repolarization. Its use is therefore intended for clinical environment being included in the technical sector of the biomedical instrumentation

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State of the art

The invention consists of a device and a method for detecting alternating wave patterns of ventricular repolarization on electrical signals of the heart or electrocardiogram (ECG). The ECG is the electrical recording of the heart activity and is used to establish the diagnosis of heart diseases. Each heartbeat consists of a set of waves that are traditionally denoted by the PQRST letters. The segment of the signal between the S wave and the T wave, referred to as the ST-T complex, corresponds with ventricular repolarization and is related to various pathologies

T wave alternation (AOT) or alternation of ventricular repolarization has been proposed as an indicator of risk for the diagnosis of ventricular failures and arrhythmias ventricular Recent studies have concluded that the appearance of AOT is a stratifier of the risk of sudden death Cardiac [Geh05], It has also been concluded that AOT may appear prior to cardiac ventricular fibrillation and in after occlusion of the coronary arteries that also they can lead to episodes of ventricular fibrillation and death sudden cardiac Its detection may be valid to predict such episodes and act accordingly, for example in a implementation in implantable defibrillators that can complement and improve the detection of positive cases.

The AOT consists of small variations in the morphology, amplitude and duration of the ST-T complex that occur between consecutive beats. In most of the cases, the AOT episodes are characterized by amplitudes close to the microvolts doing their inspection and recognition virtually impossible visual.

An example of an episode is shown in figure 1 of alternating T wave. Consider that the complex ST-T in the absence of AOT episodes presents a morphology that we can associate with a pattern referenced as A. In if there is AOT, periodic variations occur heartbeat at beat on the ventricular repolarization segment. In this case a new pattern appears on the ST-T complex that is referred to as B. The consecutive alternation between patterns A and B (Fig. 1) corresponds to an AOT event (ABABABA ...). This phenomenon is reflected in the spectrum of the signal in the components frequencies equal to half the beat frequency.

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Among the various analysis techniques of the T wave alternation proposed so far, can be quote:

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 The spectral method [Ros94], [Smi98]: This technique proposes to analyze energy variations of the frequency spectrum of the ECG signal of the time series of a group of beats, so that a peak of energy is sought for a revealing frequency of the fluctuation sought.

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 The demodulation technique complex [Nea91]: this technique attempts to model the fluctuation of the amplitude of the T wave by a sinusoid of frequency 0.5 cycles per beat and variable amplitude and phase, so that ensure dynamic tracking of alternating variations of the T wave.

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 The moving average technique modified [Ver00]: this method consists of calculating, every two beats, the moving average of the level of the T wave at a given point in the repolarization segment and in quantifying the difference of amplitude between the two means.

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 Stretching technique [Ber97]: In this technique the T wave is superimposed on a template and the Temporary component is stretched so as to minimize the difference between the template and the analyzed beat.

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 The autocorrelation technique [Bur97]: This is a technique that consists in quantifying, in the temporal domain, amplitude and morphology variations of the repolarization wave based on a correlation index. Each T wave is correlated with a representative mean T wave of a series of beats, translating an alternation, positive or negative, in a correlation index oscillation around the unit value. The interlaced series of coefficients is analyzed using a zero crossing counter in the domain of weather.

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 The wavelets approach [Cou99]: The ECG signal breaks down as a sum of Gaussians that subsequently processed, so that the different ones are isolated wave components (P, QRS and T) and appear like this singularities, revealing in particular an alternation for the wave T.

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 Transformed of Karhunen-Loewe [Lag96]: truncated KLT is used to compact the energy of the ST-T complex in a reduced number of coefficients to later analyze the series spectrally using the correlation method.

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 Capón filtering method [Mar00]: is a variant of complex demodulation. A FIR filter that minimizes the power of the output signal while Preserves the alternating component. It is applied instead of a filter invariant low pass.

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 Poincaré projection method [Str02]: Poincaré maps are used to analyze systems dynamic that show periodicity. You start by getting the Poincaré series taking samples in the same phase of the complex ST-T of consecutive beats and pairs are represented of differences between the samples of that series. The alternation is identify when two separate point groupings are present on maps.

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 Method of transforming periodicity [Sri02a]: the periodicity transform is applied to the interlaced series of some characteristics of the T wave, such as for example the amplitude of peak, area or variance. With this method you calculates the energy of the orthogonal projection of each series in the subspace of periodicity sequences 2 beats.

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 Method of statistical tests [Sri02b]: different statistical tests are proposed for the AOT detection.

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 Relationship method of Laplacian probability [Mar06]: Given a signal model in which noise and alternation are included, the maximum probability estimator (MLE) and the generalized probability ratio test (GLRT) are They can use for the estimation and detection of alternation. Be demonstrates that the probability density function of physiological noise It corresponds to a Laplacian distribution. The MLE and the GLRT For this model they are based on median filters.

The methods described in the state of the art for the detection of AOT must deal with two effects main inherent to ECG signals. These two effects are, the ECG signal noise and frequency variability cardiac

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 The noise on the ECG signal is one of the main problems for the detection of AOT, especially when its power is of the same order of magnitude than that of the power of the alternation. In this case, it may happen that the noise conceals the small variations associated with the AOT. This fact is common for all detection methods. proposed, with no solution other than the inclusion of pre-processed stages for the elimination of noise.

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 The variability of the heart rate is another major problem in detecting AOT in the sense that some of the methods described are designed with the necessary condition that the analyzed ECG signal present stationary conditions in heart rate. To the be the ECG signal a variable frequency signal the methods of detection require obtaining such proximity conditions to periodicity through actions external to the AOT analysis. The adapted solutions to achieve these conditions consist of the administration of drugs, such as atropine or dobutamine, or in performing stress tests monitored by medical staff. This frequency variability factor Cardiac output is magnified when the AOT detection method needs a high number of beats for the ECG analysis as it is done complicated that these stationary conditions remain in the weather. The techniques described above use frames of high length of at least 64 to 128 beats.

These two effects prevail in the case of certain signals such as Holter records or tests of effort or ergometry records.

The present invention presents a new technique for detection, quantification and calculation of the waveform alternating in the analysis of the alternation of the T wave of a ECG signal Regarding the techniques referred to above, this The invention has the following properties:

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Processes the ECG in the domain of weather.

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Be based on spectral analysis, but instead of processing series temporary extracted from the ECG uses the ECG as the original signal in the analysis.

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Use a reduced number of beats in the analysis, 8 to 32, decreasing the effect of variability of the heart rate, improving the resolution of the analysis and reducing the computational cost making it valid for implementation on any existing device, including portable or implantable devices.

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Is robust against noise, being the valid method for the analysis of any type of electrical signal from the heart from any existing source or device, among which you can cite, long-term Holter records, signals coming of stress tests, cardiac monitoring devices or intracavitary signals from electrophysiological studies or implantable devices.

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further of estimating the level of alternation of the signal, it allows to recover the alternating waveform and define the temporary instants of the ECG on which it appears.

Explanation of the invention.

The described invention aims at T Wave Alternation (AOT) detection by analyzing the frequency components at half the beat frequency. The proposed detection methods are applied on blocks of ECG signal formed by several beats. To model each of These heart signal blocks are considered two properties: proximity to periodicity conditions and proximity to conditions stationary In order to achieve these conditions the operation of the invention is based on block analysis with a reduced amount of beats (Fig. 2). On the one hand you get decrease the heart rate variability of the block thus favoring conditions close to periodicity. For another side it is achieved that the succession of PQRST waves of each beat is stationary (Fig. 3).

The method proposed in the invention is based on the theoretical relationship between the heart rate and the frequency of the alternating waves. The ECG is considered to consist of a stationary signal. Let f c be the average heart rate at a given time of the ECG. Under these conditions the occurrence of an AOT event, which occurs periodically every two beats, will be reflected in the frequency spectrum at half of the average heart rate fc / 2.

The method proposed in the invention performs the ECG treatment directly in the time domain. The analysis is performed on segmented blocks of L beats. The description of the ECG signal, p0 (t) , is based on an additive model that is obtained from the periodic repetition of a single beat q (t) . Thus block of L beats, for all L even and without loss of generality, is expressed as:

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where T {b} is the block period of heartbeats. Because p (t) a periodic function its frequency spectrum P {0} (\ omega) is a pulse train at the multiple frequencies of the average beat frequency.

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where a k are the Fourier series development coefficients of p_ (t) .

The appearance of AOT is modeled as an additive component ε (t) of an alternating wave function that is assumed to arbitrary morphology, duration and amplitude. This function is repeated every couple of beats, so its frequency is half the heart rate. Adding this function to the proposed ECG signal model gives the following function:

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whose Fourier transform is:

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Where b _ {k} are the coefficients of the Fourier series of the periodic function expansion alternating wave. AOT is manifested in the spectrum as impulse functions that are repeated at frequencies multiple of half the beat frequency. The aim proposed by the invention method is to extract information from the coefficients {b k} _ alternating wave of the rest of the ECG signal along with noise that may exist. For this, the method of the invention must increase the significance of the AOT harmonics in relation to the main harmonics of the signal and noise.

The method proposed in the invention is adapted to Different modes of operation or implementations. These implementations can be fixed material devices, portable or implantable. Three types of modes are defined operation applicable to the three types of implementation of the invention. These are, normal mode operation, operation in noisy mode and real-time operation.

Normal mode operation is proposed to signal analysis in stable conditions such as case of ECG records taken in medical centers in conditions Low noise The clinical results of this method are the estimated alternating power and the waveform of the alternation.

Operation in noisy mode is proposed for signal analysis in unstable conditions with high level of noise, such as signals from Holter registers Long lasting or stress testing. The clinical outcome of This mode of operation is the estimated alternating power.

Real-time operation is proposed to the implementation of devices in which the detection of the AOT must be performed in a known time interval what Small enough for decision making. This mode is indicated in implementations of implantable devices, such as case of implantable defibrillators and pacemakers, in which The noise level of the intracavitary signal is low. The detection of AOT is used as an added parameter in the detection of phenomena of ventricular fibrillation and sudden cardiac death.

The possible implementations of the invention They may include one or more of the following characteristics:

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A mechanism for extracting ECG signals implemented in the own device or a mechanism that allows to obtain the signals from another device capable of doing so (195).

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One o several graphic presentation devices that allow the visualization of the physiological signal captured with one or several derivations as well as the results of the analysis, as per example a screen or any print media (215, 220, 210, 225, 235).

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One o several communication mechanisms that allow sending commands of device control or transmission of information to another device based on any known communication system wireless or via cable (235).

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One o Multiple disk-based data storage devices solid or magnetic state (185), removable or not, allowing store signals, results, or performance data needed in the Device operation

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A Operation interface based on a keyboard (200), mouse (205) or operation buttons (230).

The results shown for the estimation of The AOT can be one or more of the following:

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He value of the power estimate, which can be given in units of voltage such as volts or power such as watts (220).

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The ECG zone where the alternation appears superimposed to the ECG in the time axis. Since the amplitude of the wave alternating is negligible with respect to the power of the signal, this one Overlay will be represented with a different color. To arrange Gradients of wave power information include gradients of color that allow to differentiate the temporary instants in the that there is greater power of the alternating wave of those who have a lower power (225).

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The estimated waveform of the corresponding altering wave (215).

The presentation of the results commented on The device can be combined with other physiological data of the signal or of the patient deemed appropriate (210). Among them, to example mode are included, the estimation of the noise level of the Signal, heart rate, respiratory rate, values heart variability, blood pressure, data regarding gasometry tests, etc.

Description of the figures

Figure 1: a schematic example is shown with ABABA pattern succession ... on the repolarization segment which illustrates an episode of AOT.

Figure 2: in order to characterize the heart rate variability, histograms are shown of the standard deviation of the heart rate for the same group of ECG signals from ergometry tests for blocks of lengths of 8, 16, 32, 64 and 128 beats. On the axis of ordered the standard deviation values of the heart rate and on the abscissa axis the number of times these deviation values appear. The scales on the axis of ordered from the previous graph are different to appreciate the distributions of cases of 64 and 128 beats. How can check, for a smaller heartbeat block, the standard deviation of Heart rate is lower. In these conditions of analysis with the use of heartbeat blocks with lengths of 8, 16 and even 32 beats you get lower frequency variabilities so conditions of proximity to the periodicity.

Figure 3: 128 isolated beats are shown from a Holter log aligned from the complex QRS As you can see the morphology of the waves does not have a great variation Therefore it can be assumed that the cardiac signal it consists of a waveform that is repeated in certain Temporary moments to give rise to a heartbeat.

Figure 4: An example window is shown used for the extraction of AOT information. This window it is synthesized from the periodic repetition of a pulse square with a variable duty cycle (20, 25) of \ frac {T_ {w}} {T_ {b}} \ cdot100%.

Figure 5: the corresponding spectrum is shown to a block of 32 beats with a frequency of 71.2 beats per minute (1.8 Hz). As you can see the harmonics that reflect the AOTs are located in odd multiples of half of the heartbeat frequency of 0.6 Hz (40, 45, 50, 55). The signal used in the simulation it has been created using physiological noise additive. For the example a periodic pulse has been used rectangular to the beat frequency with a duty cycle of 25%

Figure 6: the sequential algorithm of functioning. The stages of Extraction are differentiated and / or ECG storage (80), signal adequacy and elimination of noise and artifacts (85), extraction of information from AOT by synthesizing a periodic and poisoned window of the signal (90), post-processed to improve AOT detection (95), AOT detection from the calculation of RAOT And to finalize the stages that can be implemented from jointly or separately for the presentation of results (110) and sending data to the output interface (105).

Figure 7: the algorithm that allows the extraction of information related to the AOT through the poisoned from a periodic function. For this, the average frequency of the heartbeat block (115, 120) and synthesizes a periodic window that will be multiplied by the block (125, 35, 20, 25, 140). In order to increase resolution and sensitivity in detection, the poisoning is done with a variable number of windows consisting of time shifted versions of the original (135). The number of windows used will depend on the signal conditions as well as the characteristics of processing and memory of the physical device where it is implemented The procedure.

Figure 8: a design scheme of the stage for the improvement of AOT detection. This stage increases the significance of the information that identifies the AOT. The actions taken are the following: elimination of noise, artifacts and Background ECG (145, 155) and estimation of the signal trend (150).

Figure 9: the design scheme of the stage destined to the detection of the AOT, from the estimation of the power spectral density of the poisoned signals (160) the T wave alternation ratio (RAOT) is calculated (165, 170) on which the decision of existence or not of AOT is made (175).

Figure 10: A heartbeat block is shown with an arrhythmia in the fourth heartbeat that causes a deviation from the average frequency in the block. It can be seen that the periodic window w (t) correctly adjusts with the ST-T complexes of the first beats but does not synchronize from said pathological event. In this case the extraction of the alternation information is not done correctly so that detection failures can occur.

Figure 11: the method for the frequency normalization per truncated period (180), from which is obtained a periodic ECG signal from a frequency chosen This method is used to improve AOT detection. when the heart rate variability of the block of Beats is high.

Figure 12: the possible ones are shown physical implementations of the device of the invention and the elements that compose them. There are two types of implementations, a device with a screen for data analysis (190) and another valid version for an implantable device (240) as you can Be a defibrillator.

Figure 13: an interface of graphic representation of the display-based alternation of the AOT information in time. Different are used colors on the repolarization segment in the representation of ECG without changing its morphology (255). This result is obtained from the parallel design algorithms and its Resolution depends on the number of windows used. The representation is obtained by averaging and weighing in a way temporary the signal blocks that have obtained the highest RAOT. The high powers are identified with one color and low with another (245, 250).

Figure 14: the summary scheme of device operation, the modes of adaptive operation to the signal conditions and actions taken regarding cardiac variability (250) and noise (265). The recovery phase of the signal is also shown. alternation (295) and together with the phases of presentation and / or Transmission of results (300).

Embodiment

No noise has been included in the ECG model described above. In the case of a noisy scenario, the introduction of additive noise v (t) of arbitrary probability and power density function is considered. Including the noise, the proposed ECG model is then x (t) = p (t) + v (t) , and its spectrum:

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Prior to the AOT analysis, the method performs a preprocessing stage with the objective of extracting said information. First, a poisoning is performed on the ST-T complexes of the function x (t) through a periodic window w (t) with an arbitrary duty cycle of \ frac {T_ {w}} {T_ {b}} \ 100% cdot. This window is synthesized from the periodic repetition of an arbitrary waveform pulse. The period chosen in the window w (t) is equal to the average period of the ECG signal, i.e. T {b}. The pulse width is defined by the variable T w. Choosing different width values results in different work cycles. The power spectral density function S w (ome) of the periodic window w (t) for the case of periodic repetition of a rectangular window (Fig. 4) is:

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As a result of enventandado produced by multiplying the periodic ECG with additive noise x (t) and the periodic pulse w (t) is obtained ECG windowing x _ {w} (t) can be expressed as:

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In order to eliminate redundant information or unnecessary, such as background ECG or low noise artifacts frequency, as is the case with baseline noise, applies a post-processing stage immediately after the poisoned. Among the possible actions taken are performs a consistent step in the subtraction of segments consecutive repolarization extracted, thus obtaining the poisoned differential ECG:

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Noise is the variable that can mainly mask AOT detection. Once the background ECG is eliminated, the dependence of the poisoned differential signal on the noise is analyzed in order to evaluate the performance of the proposed method. For this, the statistical properties of the poisoned differential noise function described above are analyzed. If a stationary process is assumed in a broad sense, the autocorrelation function R v {d} (τ) of the differential noise v d (t) is as shown below:

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being R {v} (t) the autocorrelation function of the noise. The power spectral density function is obtained from the spectral density of the noise S v (ome) as the Fourier transform of R v d (ta) ,

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The autocorrelation of the poisoned differential noise function R wd (t, t- \ tau) can be calculated from the differential function taking into account that v wd (t) = v_ {d} (t ) \ cdotw (t) . After the poisoning the resulting process is not stationary but cyclo-stationary [Gar75]. In this way:

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and the spectral density function of power is calculated as the Fourier transform of the average of the autocorrelation on the variable weather.

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that in the Fourier domain equivalent to convolution:

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The numerator of the previous expression imposes zeros in the origin and in all the harmonics of the beat frequency. On the other hand, the work cycle of the periodic pulse w (t) can be chosen according to certain criteria that allow fine-tuning the detection of AOT, with values between 5% and 45%. A good compromise solution, by way of example, may be the use of a 25% duty cycle ( T w = T b / 4). In this case, all odd coefficients of the poisoned differential ECG are canceled:

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The detection of AOT events is subject to the search for the frequency peaks in the harmonics of the multiple frequencies of the alternating wave (Fig. 5), that is, f_ {c} / 2 (40), where f_ {c = 1 / Tb, and all its odd multiples that are compared to the noise in its vicinity. As a result of this analysis, a T Wave Alternation Ratio (RAOT) is obtained on which criteria that allow the existence or not of AOT in the analyzed heartbeat block will be established. This detection can be done through thresholding, peak detection techniques, use of neural networks, etc.

Once established the existence or not of AOT, the results presentation phase will be carried out where, the device of the invention can calculate a waveform estimated alternation wave by a transformation reverse and filtered power spectral density calculated

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The design scheme proposed in the invention it happens in five different sequential stages (Fig. 6), which They are listed in order of application:

one

ECG extraction and / or storage (80).

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Signal adaptation and elimination of noise and artifacts (85).

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Extracting AOT information by synthesizing a periodic and poisoned window of the signal (90).

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Postprocessed for the improvement of AOT detection (95).

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AOT detection from calculation of the RAOT (100).

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Results presentation (110).

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Output interface (105).

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Each of the following are described below. described stages and the design blocks that make them up:

one

ECG extraction and / or storage (80).

Its function is the of capturing the ECG signal that can consist of one or more referrals This capture mechanism is implemented in the own device (195). Otherwise it will adapt the signals taken by any other device capable of this by means of the physical reading of the data or of its reception via Any means of transmission.

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Signal adaptation and elimination of noise and artifacts (85):

Its function is the to adapt the input signal to facilitate its analysis, eliminate noise and artifacts such as baseline noise. It can be implemented by any existing method in the state of the technique such as linear filtering, nonlinear filtering or multitasking processing.

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Extracting AOT information through poisoned w (t) (90):

Its function is the to process the heartbeat block at the input to discriminate the spectral information outside the AOT. This stage takes place in parallel in order to improve the resolution of the method. The scheme design is based on the average of the spectra through the poisoned in different areas of the signal. When the cycle of Window work is small (values below 25%), the sweeping area over the ST-T complex decreases and it may be the case to cover areas of the repolarization segment ventricular in which there is no AOT being able to exist in areas adjacent. With this objective, the present invention analyzes the signal through different offset versions of the window periodic, calculating the average spectrum resulting from the product of each of them with the ECG block. This scheme, in addition to providing greater robustness against noise, increases the resolution of the detection, eliminating the effect that the use of Windows with small duty cycle. This stage is subdivided. in the following blocks to implement:

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QRS Detection (115):

Your mission is the of detecting the QRS complexes of the heartbeat block. It can use in its implementation any existing method in the state of the art, such as the use of filters adaptive or transformed wavelet.

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Calculation of T _ {b} (120):

The frequency and standard deviation of heart rate.

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Window synthesis (125):

Its function is to synthesize the window that is used to extract the information from the AOT. For this purpose a periodic signal from a pulse with a period equal to the average beat frequency T {b} (25) and a duty cycle between 5% and 45% is synthesized. The choice of the duty cycle is determined based on the resolution required by choosing a value of T w (20). ECG poisoning with small work cycles extracts a smaller amount of information from the ST-T complex and is affected by noise to a lesser extent, while poisoning with larger work cycles is more affected by it. Regarding the morphology of the pulse (30) from which the window is synthesized, it has an arbitrary waveform, such as hamming, hanning, triangular, rectangular, Kaiser windows, etc. Or you can also use non-parameterized waveforms such as windows adapted with waveforms similar to the ST-T complex.

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Delay (130):

Its function is the of generating a shifted version of the periodic window synthesized This block is responsible for entering a delay? of arbitrary duration. For small delays, it increases the resolution of the method and its robustness against noise. The chose the number of displaced versions of the window will depend on the signal conditions and unit characteristics central processing unit (CPU) and physical device memory a implement.

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Wrapped (140):

It's a block intended to realize the product of the signal with the version displaced from the periodic window that corresponds to it.

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Post-processed for improve AOT detection (95):

This stage has as a mission to increase the significance of the alternation over the signal noise by processing so that detection AOT is favored. The blocks to be implemented will be one or several of the following:

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Background ECG Elimination (145).

The Background ECG along with low frequency noise artifacts, for means of subtraction of consecutive segments of repolarization extracted.

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Trend Estimation (150):

This sub-stage calculates the trend of the signal by partially discriminating the changes that the signal has from those produced by noise artifacts, so that the AOT information is enhanced in the next detection stage. To this end, skillful methods are used for this purpose, such as the case of empirical mode decomposition (EMD: Empirical Mode Decomposition) [Hua01] that manages to separate the useful information corresponding to ventricular repolarization of noise and artifacts. The present invention proposes a method of estimating the trend from the separation that is carried out by partial reconstruction of the signal obtained as the sum of the intrinsic mode functions (MFIs: Intrinsic Mode Functions) that have not been identified as noise . The main problem in carrying out this separation is to identify the useful components. In the present invention, useful information is determined in the EMD domain using the descriptors Hjorth [Hjo70, Hjo73] and the spectral purity index (SPI: Spectral Purity Index) [Sor05], Consider x [n] , n = 1 , ..., N-1 , an isolated ST-T complex. The model that describes this part of the heartbeat is considered additive:

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where s [n] is the valid ST-T complex that contains the information corresponding to ventricular repolarization and v [n] groups the rest of undesirable components. The signal s [n] represents the trend of the original ST-T complex and corresponds to a slow variation signal, while v [n] is characterized as a rapidly varying signal, associated with higher frequency components. The original signal is represented by the EMD decomposition technique:

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where c_ {i} [n] are the MFIs in the EMD domain and q_ {L} [n] the remainder. The partial sum of MFIs is used to separate the signal x [n] into its two additive components as follows:

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The MFIs c i [ n ] of faster oscillations, from the first order, c 1 [ n ], to the order ( P-1 ), c P-1 [ n ] , are considered as non-relevant components to describe ventricular repolarization. The procedure for determining the P index is based on the analysis of the spectral purity index. This parameter is calculated as:

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where m i represents the ith spectral moment of the power spectrum of the signal x [n] . The SPI index indicates the extent to which a signal can be described by a single frequency. In our case, the P index is identified as the first MFI that does not have oscillatory characteristics, starting with higher order MFIs. In other words, the order P of the P- th MFI is obtained when the value of the spectral purity index for that mode takes a certain value.

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Noise elimination (155):

Within this block is eliminated noise by filtering techniques.

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AOT detection: RAOT calculation and decision (100):

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Density Estimation Spectral power (160):

Your mission is the of calculating the total spectrum of the signal, particularly in the proximity to the beat frequency (0-5 Hz). Any method existing in the state of the art, both parametric as non-parametric, it is valid for implementation.

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Search and analysis of peaks in the harmonics of f_ {c} / 2 (165):

It is the block responsible for extracting the parameters that identify the AOT of the frequencies f_ {c} / 2 (40), 3 f_ {c} / 2 (45), 5 f_ {c} / 2 (50), 7 f_ { c} / 2 (55), ..., and the noise in its vicinity (60, 65, 70, 75).

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RAOT calculation (170):

From the extracted parameters the RAOT is calculated the existence of AOT

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AOT Decision (175):

The decision on the detection of the AOT is taken from the value of RAOT calculated and can be taken using thresholding criteria or by using any discrimination technique of patterns such as support vector machines, systems experts, genetic algorithms, neural networks, etc.

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Results presentation (110):

The results shown for the estimation of the AOT can be one or several of the following:

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The value of the estimate of power, which can be given in voltage units such as volts or power such as watts (220).

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The area of the ECG in which the alternation appears superimposed on the ECG on the axis of times (250, 255). Since the amplitude of the alternating wave is negligible with respect to the signal strength. This overlay It will be represented with a different color. To dispose of the Wave power information gradients are included color (245) that allow to differentiate the temporary instants in the that there is greater power of the alternating wave of those who have a lower power (225).

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The estimated waveform of the corresponding alternating wave (215).

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Output Interface:

Is the interface which transmits the information to a user, to another stage of processing or to a device, about the existence or not existence of AOT in the signal.

The proposed design schemes are based on ECG conditions close to the periodicity for the detection of AOT. These conditions are difficult to achieve in some cases. A signal with high heart rate variability can cause a lack of synchronization between the ECG signal and the window w (t) making the extraction of the AOT information unsuccessful (Fig. 10).

It is necessary to generalize the signal model presented at the beginning in order to correct this possible lack of synchronization effect between the window and the ECG. In this extension the conditions of proximity to the stationarity of each beat are maintained. Variability in heart rate is introduced to the model from the succession of times T interlatido {k}, k = 1, 2, ..., L, formed by the series of times between two consecutive R waves (T RR). The elements of the sequence consist of the duration of each beat. Assuming that the beats have a standing waveform, the ECG is defined as:

twenty

This generalized ECG model corresponds with the model described above in the case of samples with little dispersion over heart rate, that is with a Small standard deviation from the average heart rate. In this case, conditions close to the periodicity and therefore, the ECG model corresponds to the model described initially.

In order to solve the problems of detection that originate from the lack of synchronization between the window and signal block due to the variability of the heart rate, the present invention includes a modification additional method by applying a process period normalization of the ECG signal.

There are period normalization techniques with the objective of keeping the signal period constant from compressions and temporary expansions. Among those described in the State of the art are [Ram97] and [Jyh01] based on multitasking transformations. Both have the advantage of being invertible being able to recover the original signal from the normalized by applying a multitasking transformation inverse

Multitask transformations in the spectrum of frequencies correspond to widening transformations and compression over time and if it affects the complexes ST-T can lead to distortions that mask or introduce false episodes of AOT. Therefore, these techniques They may not be appropriate for AOT detection. For example, the appearance of an alternation over the width of the complex ST-T could disappear if all complexes were normalize with the same temporal width. The proposed solution in This invention is realized by frequency normalization cardiac by truncated period (180), which consists of the imposition of a constant period for the heartbeat block. This truncated changes the frequency components of the signal but not change the temporary information of the alternation since no modifies the morphology of the ST-T complex that keeps intact

Let T max be the period imposed on the heartbeat block taken as the maximum interlaced times of the sequence of times T k:

twenty-one

       \ vskip1.000000 \ baselineskip
    

From which you can build the following signal with constant period:

22

In this signal the effect of the heart rate variability, affecting only the noise component On this normalized period signal The procedures and steps described above apply. In First, the information of the AOT is extracted by poisoned:

2. 3

Subsequently in order to remove artifacts ventricular repolarization segments are subtracted consecutive:

24

The decision about the existence or non-existence of AOT is made in the same way by analyzing the spectrum of x wd_ {norm} (t) . In this case the alternation harmonics will appear in the multiples of \ frac {1} {2T_ {max}} Hz.

The choice of the truncated period of T max has been proposed in order to ensure that all beats are adjusted for duration within the normalized period and to ensure that the periodic pulse poisoning encompasses a greater part of the ST-T complex. Alternatively, depending on the operating specifications of the invention, the value of the truncated period can be chosen arbitrarily, for example taking a fixed and constant value for the entire analysis, and can be a user configurable value of the device. . For example, in the case of real-time operation, the period used in the normalization must be a default value entered as an input parameter of the device or as an input value entered by the user of the device depending on the characteristics of the patient in any range of values that allow high enough heart rates.

The normalization stage of the described period is applied in cases in which the ECG heart rate variability is high (250) and is performed after (120) the poisoned, synthesizing w (t) (125) from the period T max chosen. This extension of the method of the invention, by eliminating the effect of heart rate variability, achieves greater robustness in the detection of AOT and allows to accommodate and solve the different operating scenarios and implementations described.

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Claims (25)

1. Method for the detection of the alternation of cardiac ventricular repolarization characterized by the extraction of the information of ventricular repolarization by means of the continuous time poisoning of blocks of variable length beats of a bioelectric signal of the heart, ECG, where the poisoning it is performed with a window defined by a periodic signal of adjustable duty cycle. 2. Method according to claim 1, characterized in that the poisoning process (140) is defined as the product of the periodic window (30) and the signal block. 3. Method according to any of the preceding claims, wherein the poisoning process of the same ECG block is carried out with different versions of the periodic window (30) displaced (130) with the following delays: 0, ta, 2 \ tau, 3 \ tau, ..., m \ tau , with \ tau and m being preset values or parameters entered by the user and not exceeding the maximum delay value of the repolarization segment duration. 4. Method according to any of the preceding claims, characterized by a periodic window (30) with the following aspects:

?

The window period (25) it can be a fixed value chosen or it can be the average period of heartbeat block that is poisoned.

?

The work cycle of the Periodic window is variable and is comprised between the 15-45% (20, 25)

?

The periodic window (30) is synthesized as the periodic repetition of a pulse (35).

?

The duty cycle of the periodic window is calculated from the average period of the ECG signal, T b, (20), and the pulse time of the window, T w (25), as \ frac {T_ {w}} {T_ {b}} \ cdot100%.

5. Method according to claim 4, wherein the Pulse waveform (35) can use any waveform parameterized as a window to extract information, such as for example the use of hamming, hamming, triangular windows, rectangular, kaiser or waveforms can also be used not parameterized as is the case of windows adapted with the waveforms similar to the ST-T complex. Method according to any of the preceding claims, characterized by a technique for reducing the effect of heart rate variability by means of a procedure for normalizing heart rate by using a truncated period (180). 7. Method, according to any of the previous claims, where a series of actions are carried out post-processing of the poisoned signal (95) with in order to highlight the information corresponding to the alternation in relation to noise. 8. Method, according to any of the previous claims, wherein a method is used subtracting consecutive beats (145) in order to eliminate noise Background ECG and low frequency artifacts. 9. Method, according to any of the previous claims, in which the trend of the ST-T segment (150) to separate information from the alternation of the T wave, AOT, of the unwanted information. 10. Method, according to any of the previous claims, wherein a method is used which by filtering the signal eliminates noise artifacts (155). 11. Method, according to any of the previous claims, wherein the density is estimated power spectral (160) of the poisoned signal (90) and post-processed (95). 12. Method according to claim 11, wherein from the search and analysis of peaks in the harmonics of f_ {c} / 2 (165) the coefficient of alternation ratio of the T wave, RAOT (170) is obtained ), where f c is the average frequency of the ECG signal. 13. Method according to claims 11 and 12, wherein from the relationship between the odd harmonics of f_ {c} / 2 (40, 45, 50, 55) and the noise in its vicinity (60, 65, 70, 75) RAOT coefficient is calculated. 14. Method, according to any of the previous claims, which determines the existence or not of alternating ventricular repolarization from the coefficient RAOT (175). 15. Method, according to any of the previous claims, which calculates the results on the existence of alternating repolarization from density spectral power of the poisoned signal and post-processed (165) and RAOT coefficient (170). 16. Method according to claim 15, characterized in that the estimation of the signal corresponding to the alternating waveform, ε (t) , obtained by filtering the poisoned spectrum from the odd harmonics (40, is calculated and visualized ) 45, 50, 55) located in multiples of half the frequency chosen in the periodic window (20). 17. Method, according to any of the previous claims, wherein when the duration of the RR intervals, that is the intervals between consecutive R waves, of the beats of a block exceeds a certain percentage of the average value of the duration of all intervals, it is performed the normalization of the period of the block (180) of beats. 18. Method according to claim 17, wherein the percentage from which normalization is performed is of 10%. 19. Method according to claims 17 and 18, in which the normalization period is chosen as the maximum of RR intervals of the heartbeat block or as a fixed value that should be greater than 0.65 seconds and that can be entered by the device user (180). 20. Method, according to any of the previous claims, wherein a technique of empirical decomposition of modes that allows components to be separated that characterize the AOT of the noise components (140). 21. Method according to claim 20 in the which is operated by characterizing the signal as the sum of intrinsic mode functions. 22. Method according to claim 20 in the that the signal is separated from the noisy components by Hjorth descriptors and the spectral purity index. 23. Device for the detection of the alternation of cardiac ventricular repolarization characterized by including means for extracting the information of ventricular repolarization by means of continuous time poisoning of heartbeat blocks of variable length of a bioelectric signal of the heart, ECG, where the poisoning is done with a window defined by a periodic signal of adjustable work cycle. 24. Device for detecting the alternation of cardiac ventricular repolarization according to claim 23, characterized by including means for performing the steps of the method according to any of claims 2-22. 25. Device according to claim 23, characterized by operating in an adaptive or predetermined manner depending on the operating scenario in which it is located and its operating requirements, the operating modes being:

?

Normal mode operation, intended for the analysis of signals or signal blocks with low level of noise and stable conditions of frequency variability cardiac, in which the periodic window is generated at the frequency average heartbeat

?

Operation in noisy mode, intended for the analysis of signals or signal blocks with high level of noise, in which period normalization is performed (180) taking the normalization frequency as the maximum RR interval of the analyzed signal block.

?

Time mode operation real.