• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
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    • 专利摘要:
    System and method for the automatic detection of abnormal electrophysiological patterns. The present invention is a system (1) and a method for automatic real-time detection of anomalous electrophysiological patterns, such as circular and rotational activation patterns, from electrophysiological signals picked up by at least a first set of electrodes such as a Multi-electrode catheter from which an electrophysiological activation pattern is generated that compared with one with at least one pre-established electrophysiological activation pattern to detect activation nodes, and generate a warning signal when these are detected. (Machine-translation by Google Translate, not legally binding)
    公开号:ES2706537A1
    申请号:ES201731161
    申请日:2017-09-29
    公开日:2019-03-29
    发明作者:Munoz Gonzalo Ricardo Rios；Rodriguez Antonio Artes；Maiz Angel Arenal；Diaz Francisco Jesus Fernandez-Aviles
    申请人:Fundacion Para La Investig Biomedica Del Hospital Gregorio Maranon；Universidad Carlos III de Madrid；Fundacion para la Investigacion Biomedica del Hospital Gregorio Marañon；
    IPC主号:
    专利说明:

    [0001]
    [0002]
    [0003] OBJECT OF THE INVENTION
    [0004]
    [0005] The object of the invention is a system and a method for the automatic detection in real time of anomalous electrophysiological patterns.
    [0006]
    [0007] In particular, this system and method makes it possible to detect electrophysiological patterns such as circular and rotational activation patterns, commonly called rotors, automatically and in real time from electrophysiological signals picked up by at least a first set of electrodes such as a multi-directional catheter. electrode.
    [0008]
    [0009] BACKGROUND OF THE INVENTION
    [0010]
    [0011] Currently, electrophysiology is known as the study of the electrical properties of cells and biological tissues, and its principles are used to record the electrical activity of different parts of the human body that range from cells, or neurons, to organs, or muscles. For example, the recording of this electrical activity is used to detect anomalies in human body functioning such as atrial fibrillation (AF).
    [0012]
    [0013] AF is the most common sustained arrhythmia in clinical practice and is characterized by an electrical disorganization of cardiac activation patterns. Currently, AF affects more than 6 million people in Europe, and its prevalence will double in 50 years with a 25% risk of development for people over 40 years of age.
    [0014]
    [0015] AF is not only the most common cardiac arrhythmia, but it is associated with an increase in morbidity and mortality (heart failure and stroke), a high number of hospitalizations, and a 100% higher mortality rate for subjects with this condition. with respect to those who do not.
    [0016]
    [0017] Currently, antiarrhythmic drugs are used to treat this condition, which offer limited efficacy, including possible side effects.
    [0018] As an alternative to said drugs, methods of catheter ablation are used. Currently, these procedures of catheter ablation are based on the observation of the ectopic foci of the pulmonary veins that trigger AF, being the isolation of the pulmonary veins and the disconnection of the left atrial muscle from the arrhythmogenic activity the key points of this type of procedures for the healing of subjects with recurrent paroxysmal and refractory symptomatic AF.
    [0019]
    [0020] More specifically, the ablation procedure aims to modify the substrate of the atrial wall, for example by applying radiofrequency signals by means of a catheter placed inside the atrium that heats the cardiac tissue, which allows to modify conduction zones generating a scar that prevents the electrical propagation of the signals that activate the heart.
    [0021]
    [0022] Currently, one of the most commonly used ablation procedures is the pulmonary vein isolation procedure (Pulmonary Vein Isolation, PVI). For this purpose, a circumferential ablation is performed around the four pulmonary veins, checking their total disconnection with the electrogram registers by means of the catheter electrodes. This procedure of ablation presents an efficiency in 70-80% of the subjects who present recurrent paroxysmal AF. Despite this, its success is limited to subjects who present refractory symptomatic AF, for which different ablation strategies have been suggested without achieving high success rates.
    [0023]
    [0024] Although, currently, the predominant theory of maintenance of AF is based on the observation of electrophysiological patterns composed of multiple electrical wave fronts that propagate randomly, the existence of spatially stable sources of reentry (rotors) has been recently identified. proposals as maintenance mechanism of the FA. This fact has increased the attention in the rotors as possible causes of the maintenance of the AF, and in the study of the ablation of zones in which rotors are detected both for patients with paroxysmal and persistent AF.
    [0025]
    [0026] In order to detect these rotors, recently we have developed methods capable of detecting rotors based on the identification of singular points, also called phase singularities (Phase Singularity, PS), using phase maps. An example of this method is known as "Focal Input Rotor Modulation" (FIRM), which offers the offline processing of information captured exclusively by a basket-type catheter during the ablation process and which requires the visual inspection, by an expert, of the results to be able to interpret them.
    [0027]
    [0028] More specifically, in the FIRM method the signals of the electrodes are recorded by a polygraph that exports the signals to a storage device (external hard drive, pendrive or similar). The storage device is connected to the computing unit (PC) that performs the signal processing, performing a visual inspection of activation maps by an expert.
    [0029]
    [0030] Precisely, the problem with this method is that the signals are treated offline to obtain a mapping, which has to be studied visually, usually outside the operating room, by an expert cardiologist to detect rotations, and therefore the entire surgical procedure is lengthen.
    [0031]
    [0032] DESCRIPTION OF THE INVENTION
    [0033]
    [0034] A first aspect of the invention relates to a system for automatic detection in real time of activation nodes characteristic of abnormal electrophysiological patterns in at least one electrophysiological signal. The system is intended to receive a first signal comprising an electrophysiological signal which behaves according to an electrophysiological pattern. This first signal comes from a first set of electrodes that can be linked to the human body ..
    [0035]
    [0036] Said activation nodes are usually related to a sudden variation in the amplitude of the electrophysiological signal, and indicate a possible anomalous behavior of the functioning of the part of the human body measured by the first set of electrodes.
    [0037]
    [0038] Preferably, the first set of electrodes is linked to an amplifier commonly used in electrophysiology laboratories, and this amplifier is connectable with the system. In this way, the electrophysiological signal generated by the first set of electrodes is amplified and ready to be processed by the system.
    [0039]
    [0040] Preferably, the electrophysiological signal is: an electrocardiogram (ECG or EKG), an electro-angiography (EAG), an electro-ventriculography (EVG), an intracardiac electrocardiogram (EGM), an electroencephalography (EEG), an electrocorticography (ECoG or iEEG), an electromyography (EMG), an electrooculography (EOG), an electroretinography (ERG), an electronystagmography (ENG), an electroolfactography (EOG), an electrocochleography (ECOG or ECochG), an electrogastrography (EGG), an electrogastroenterography ( EGEG), an electroglotography (EGG), an electropalatography (EPG), an electroarteriography (EAG), an electroblock (EBG), an electrodermography (EDG), an electrohysterography (EHG), an electroneuronography (ENEG or ENoG), an electropneumography ( EPG), an electrospinography (ESG), an electrovomerography (EVG), or any other signal related to the electrical properties of cells or tissues.
    [0041]
    [0042] More specifically, the system comprises:
    [0043] • a data processing unit, configured to receive the first signal, and which in turn comprises:
    [0044] • a memory with instructions and a plurality of preset electrophysiological activation patterns, and
    [0045] • a microprocessor linked to the memory, where the instructions enable the microprocessor to:
    [0046]
    [0047] or generate an electrophysiological activation pattern from the first signal,
    [0048] or compare the electrophysiological activation pattern with at least one pre-established electrophysiological activation pattern to detect the activation nodes, and
    [0049]
    [0050] or generate when the activation nodes are detected, a warning signal comprising information of the activation nodes.
    [0051] Preferably, the system comprises an analog / digital converter linked to the data processing unit and connectable to at least the first set of electrodes, wherein the analog / digital converter comprises a receiving element for receiving the first signal from the first set of signals. electrodes, a conversion element for converting the first signal to digital format and a transmission element for transmitting it in digital format to the data processing unit. Note that this analog / digital converter is used when the first signal is in analog format, depending on its original digital or analog format of the facilities or elements of the electrophysiology laboratory.
    [0052]
    [0053] Additionally, the analog / digital converter comprises a second input intended to be linked with a second set of electrodes configured to generate a reference signal. Preferably, this reference signal also passes through the amplifier before it reaches the analog / digital converter to be amplified.
    [0054] Preferably, the instructions enable the microprocessor to pre-process the first signal in order to eliminate noise.
    [0055]
    [0056] More specifically, during the pre-processing of the first signal, said reference signal is used, in combination with the first signal, by the microprocessor for the detection of electrophysiological noise. The detection of this electrophysiological noise allows the microprocessor to subtract unwanted electrophysiological signals, improving the reliability of the system by avoiding false electrophysiological activations produced by electrophysiological noises.
    [0057]
    [0058] Additionally, the system comprises a warning unit linked to the data processing unit and configured to receive the warning signal and reproduce it preferably visually or aurally.
    [0059]
    [0060] Preferably, the warning unit is selected from: auditory indicators, indicator lights, visual indicators, sensory indicators such as vibration or quantitative indicators, such as numerical values associated with the different electrophysiological patterns such as the number of turns per second or the number of rotors in the signal, among others.
    [0061]
    [0062] More preferably, said warning unit is of the light indicator type such as a monitor that receives said warning signal and reproduces the position of the electrophysiology activation nodes with respect to the first set of electrodes. Said monitor receives the isochronous map and represents it to facilitate the location of electrophysiological activities
    [0063]
    [0064] Preferably, each pre-established electrophysiological activation pattern comprises activation nodes associated with at least one isochronous map which in turn comprises pre-established propagation vectors whose shape and distribution represents the shape of the anomalous electrophysiological activation node produced by an electrophysiological activation. These pre-established electrophysiological activation patterns are selected among linear, non-linear, circular and rotational isochronous maps, polynomial, fixed value based on an activation extracted from the electrophysiological signal, fixed value based on a mathematical model of electrophysiological signal or a variable value based on learning machine.
    [0065] A second aspect of the invention relates to a method for automatic real-time detection of abnormal electrophysiological patterns in at least one electrophysiological signal that makes use of the system described above.
    [0066]
    [0067] More specifically, this method comprises the following steps:
    [0068] a) generate an electrophysiological activation pattern from the first pre-processed signal,
    [0069] b) comparing the electrophysiological activation pattern with at least one pre-established electrophysiological activation pattern to detect the activation nodes, and c) generating a warning signal comprising information of the activation nodes.
    [0070]
    [0071] Additionally, the method comprises a pre-processing step of the first signal to eliminate noise.
    [0072]
    [0073] More specifically, the previous stage comprises:
    [0074] • eliminate electrical noise by filtering, and
    [0075] • Eliminate electrophysiological noise by detecting and subtracting unwanted electrophysiological signals.
    [0076]
    [0077] More specifically, step a) comprises:
    [0078] • generate a second signal comprising the approximation of the first pre-processed signal,
    [0079] • generate a third signal comprising the interpolation of the second signal, • detect local activation times included in the third signal,
    [0080] • generate, from the third signal and activation times, a fourth signal, • represent the fourth signal in the form of an isochronous map,
    [0081] • detect the presence of activation nodes,
    [0082] • generate a propagation vector for each activation node, and
    [0083] • generate an electrophysiological pattern that includes the isochronous map, the activation nodes and their propagation vectors.
    [0084]
    [0085] Preferably, the interpolation of the second signal is carried out by different techniques such as an interpolation based on neighboring neighbors, a cubic interpolation, linear interpolation, a weighted inverse distance interpolation or a spline interpolation.
    [0086] More specifically, step b) comprises:
    [0087] • compare the electrophysiological pattern with at least one of the preset electrophysiological activation patterns by the point-to-point scalar product, and
    [0088] • detect the direction of the propagation vector by applying a first and a second threshold in the electrophysiological pattern,
    [0089]
    [0090] More specifically, step c) comprises:
    [0091] • generate a warning signal when the activation node is detected and indicate the direction of the sector.
    [0092]
    [0093] Additionally, the method comprises a stage where it detects the position of the electrodes of the first set of electrodes and relates them graphically with the location of the electrophysiological activities.
    [0094]
    [0095] Preferably, said warning signal is sent to the system monitor so that it reproduces it graphically.
    [0096]
    [0097] In this way, a system and a method that detects and notifies graphically, visually or sonorously a user, such as doctors or surgeons, the existence of activation nodes, and in which areas they are automatically and in time is obtained. real, without having to export the data and process them in third-party dependencies and without a third user having to analyze the results in off-line mode.
    [0098]
    [0099] DESCRIPTION OF THE DRAWINGS
    [0100]
    [0101] To complement the description that is being made and in order to help a better understanding of the characteristics of the invention, according to a preferred example of practical realization thereof, a set of drawings is attached as an integral part of said description. where, with illustrative and non-limiting character, the following has been represented:
    [0102]
    [0103] Figure 1 shows a schematic view of the system of the preferred embodiment.
    [0104]
    [0105] Figure 2 .- Shows a schematic view of the method of the preferred embodiment.
    [0106] Figure 3a - 3e.- Shows a graphic representation of the different signals of the method of the preferred embodiment.
    [0107]
    [0108] Figure 4.- Shows a graphic representation of the reference signal and its reference points.
    [0109]
    [0110] Figure 5.- Shows an example of voltage deflections associated with depolarizations.
    [0111]
    [0112] Figure 6 .- Shows a graphic representation of the detection thresholds of the rotor.
    [0113]
    [0114] PREFERRED EMBODIMENT OF THE INVENTION
    [0115]
    [0116] In a preferred embodiment of the present invention, the system (1) is configured for automatic detection of activation nodes in abnormal electrophysiological patterns, such as circular and rotational activation patterns, commonly referred to as rotors used to detect abnormalities in human body functioning such as atrial fibrillation (AF). This detection is done in real time and is obtained from electrophysiological signals.
    [0117]
    [0118] This system (1), as shown in Figure 1, comprises: an analog / digital converter (2), a data processing unit (3) and a warning unit (4).
    [0119]
    [0120] Specifically, the analog / digital converter (2) is linked to an amplifier (5) commonly used in electrophysiological laboratories to perform AF ablation procedures.
    [0121]
    [0122] More specifically, this amplifier (5) receives an electrophysiological signal comprising unipolar or bipolar intracavitary electrograms (EGMs) that are produced by a first set of electrodes such as a multi-electrode catheter in contact with the heart of a subject; and a reference signal comprising an electrocardiogram (ECG) produced by a second set of electrodes such as 10 electrodes in contact with pre-established outer points of the body of the subject, four of them at peripheral points and 6 of them at precordial points. Preferably, the first signal is used as an electrophysiological signal of the electrical activity of the intercavities of the heart, while the ECG is used as a reference signal for the synchronization and elimination of unwanted noises in the first signal.
    [0123] Additionally, said amplifier (5) eliminates the low and high frequency noise produced by the first and second set of electrodes, applying bandpass filtering and amplifying the result of the filtering, thus passing electric signals in millivolts to electrical signals in the range of volts.
    [0124]
    [0125] Preferably, the amplifier (5) is linked to a recording unit (7) that registers and monitors both the first and the second signal, said recording unit (7) being a common element of these electrophysiological laboratories.
    [0126]
    [0127] Preferably, these electrophysiological laboratories also include a 3D mapping system (6) which uses said signals from the first and second set of electrodes to generate a 3D mapping that reconstructs at least the anatomy of the heart.
    [0128]
    [0129] On the other hand, the data processing unit (3) is linked to the analog / digital converter (2) and the warning unit (4), and comprises a memory with instructions and a circular and rotational activation pattern. ; and a microprocessor linked to memory, where the instructions allow the microprocessor:
    [0130] • pre-process the first signal to eliminate noise,
    [0131] • generate an electrophysiological activation pattern from the first pre-processed signal,
    [0132] • compare the electrophysiological activation pattern with at least one pre-established electrophysiological activation pattern to detect activation nodes, and • generate a warning signal comprising information from the activation nodes.
    [0133]
    [0134] Preferably, said warning unit (4) is a monitor that visually represents said electrophysiological activation pattern, as well as the activation nodes, in real time and automatically.
    [0135]
    [0136] On the other hand, in this preferred embodiment, the method for automatic real-time detection of circular cardiac activations using the system described above, as shown in FIG. 2, comprises the following steps:
    [0137] • previous stage of pre-processing the first signal to eliminate noise,
    [0138] • a) generate an electrophysiological activation pattern from the first pre-processed signal,
    [0139] • b) compare the electrophysiological activation pattern with at least one pre-established electrophysiological activation pattern to detect the activation nodes, and • c) generate a warning signal comprising information of the activation nodes.
    [0140]
    [0141] Specifically, the previous step comprises performing a pre-processing of the first signal and the reference signal. The first signal as received by the system (1) has a shape as shown in Figure 3a, where the ventricular contribution is shown in shading. On the other hand, the reference signal as it is received by the system (1) is shown in figure 4, where additionally different reference points are indicated (Q-peak, R-peak, S-peak, T- end).
    [0142]
    [0143] More specifically, in this particular case the first signal has a deviation, or noise, of low frequency caused by the respiratory movement of the subject, which is eliminated by applying to the signal a median filter whose output is subtracted from the first one. In addition, the unipolar configuration of the electrodes of the multi-electrode catheter registers far-field activations, capturing the ventricular activity, and concealing the atrial activations, in order to counteract this effect, a subtraction of the average heartbeat of the subject is performed. That is, the microprocessor calculates the unipolar contribution of various ventricular beats obtained by each electrode of the multi-electrode catheter and obtains a ventricular pattern using as a reference to locate the segments affected by the ventricular contribution in the unipolar signals that make up the first signal.
    [0144]
    [0145] Additionally, the microprocessor examines variations in the ECG length of the reference signal when that ventricular pattern is calculated. More specifically, as and as shown in Figure 4, the reference points (Q-peak, R-peak, S-peak, T-end) are located in the reference signal. The subintervals and the lengths of the reference signal are defined by:
    [0146]
    [0147]
    [0148] In this way, for each unipolar signal the microprocessor calculates a QRST interval and a segment is extracted according to the following equation:
    [0149]
    [0150]
    [0151]
    [0152]
    [0153] Additionally, the microprocessor performs a cubic spline interpolation to transform the segments of the subintervals into
    [0154]
    [0155]
    [0156]
    [0157]
    [0158] Likewise, to correctly cancel the ventricular contribution, the pattern p (i) is increased or reduced with said spline interpolation to meet the length of each segment.
    [0159] The corrected p® pattern and the segments
    [0160]
    [0161] In this way, false activations are eliminated, and when using the reference points only the subintervals are modified and not the whole pattern.
    [0162]
    [0163] On the other hand, step a) comprises detecting the local activation times of the second signal of the activation nodes. Said activation nodes are related to depolarizations, said depolarizations are characterized by an abrupt deviation of the action potential registered by the multi-electrode catheter. Depending on the configuration of the acquisition, as well as the reference used in the amplifier, the polarity of the first signal can be inverted, reflecting activations with positive deviations.
    [0164]
    [0165] In this particular embodiment, for the detection of the local activation time a range of 20 to 40 ms is considered as the duration of a depolarization, as shown by way of example in Figure 5. In this way, the microprocessor generates A second signal comprising the first approximate pre-processed signal by a linear function of the interval defined by 2M + 1 centered at time n0, expressed as:
    [0166]
    [0167]
    [0168]
    [0169]
    [0170] where, ^ [n0] represents the function of the slope at time n0, and this function ^ [n0] is estimated by the mean square error.
    [0171]
    [0172]
    [0173]
    [0174]
    [0175] Which can be simplified to:
    [0176]
    [0177]
    [0178]
    [0179] This can be interpreted as a filter with a pulse response that follows the following equation:
    [0180]
    [0181] Whose result, represented in figure 3d, resembles the result of a differential operator.
    [0182]
    [0183] Additionally, the microprocessor inverts this resulting signal rectifies p [nQ obtaining a new signal, fí + [n , represented in figure 3e. In case the configuration of the first signal relates activations with positive deviations the signal fí + [n ] is directly ^ [n]. More specifically, the amplitude of the peaks fí + [n varies from activation node to activation node, the deviations being a consequence of the constant cardiac activity that prevents the first set of electrodes from having a uniform atrial contact, resulting in changes of amplitude.
    [0184]
    [0185] Additionally, in step a) the microprocessor implements a threshold function (th [n]) that detects local activation time and that is updated at each instant by following the following equation:
    [0186]
    [0187]
    [0188] The variables M¿ and n¿ are the amplitude and time where the last peak is detected, r defines the decay rate of the exponential function and the constant a specifies the value of the lower threshold .
    [0189]
    [0190] Additionally, step a) specifically generates a third signal by interpolating the signal p [n], and combines it with the activation times to generate a fourth signal represented by an isochronous map. In said isochronous map each signal [n] is mapped in a space according to the physical position of the multi-electrode catheter. The nJk nodes that correspond to the position of the electrodes belong to the set of nodes N.
    [0191]
    [0192] Preferably, said interpolation is performed by the interpolation technique known as inverse discrete weighting (IDW), and defined according to the equation:
    [0193]
    [0194]
    [0195]
    [0196] where:
    [0197]
    [0198]
    [0199]
    [0200]
    [0201] Preferably, the presence of rotational activations in said isochronous maps is detected by the estimation of the optical flow, where the intensity of an I (Z, t) image is defined by:
    [0202]
    [0203]
    [0204]
    [0205]
    [0206] where t is time, x is space [x, y] T and f is the vector of propagation in 2D [u, v] T.
    [0207]
    [0208] More specifically, to the isochronous map with the activation nodes, the method described by Horn-Schunck is applied to estimate the optical flow, specifically by means of the following equation:
    [0209]
    [0210]
    [0211] Where, VI, It is the intensity of the image, f is the vector of propagation in 2D [u, v] T and A is a regulation constant.
    [0212]
    [0213] In this way, the direction vectors are generated for each activation node, or pixel, f which reflect the direction of propagation of the reconstructed address vector in the isochronous map.
    [0214]
    [0215] Additionally, this isochronous map is compared with a pattern of the plurality of pre-established electrophysiological activation patterns, in this case it is known that if the physiological signal presents a behavior that follows a rotational pattern, an isochronous reference map is available with a pattern that arranges the direction vectors of the nodes so that they form a circle. Both the direction vectors that make up a propagation wave and the isochronous reference map are respectively divided into matrices [U, V] and [0, í ].
    [0216]
    [0217] Example of the equations to obtain one of the patterns, in this case we are dealing with the vectors that make up the rotational isochronous map:
    [0218]
    [0219]
    [0220]
    [0221]
    [0222] More specifically, in step b) to compare both maps the point-to-point scalar product is computed ie pixel to pixel of the map obtained with the circular pattern defined by Cj, k = [dj, k, eg, k T such and as shown in figure 6, and that generates the signal T [n] according to the following equation:
    [0223]
    [0224]
    [0225]
    [0226]
    [0227] Where this equation is normalized with respect to the number of nodes forming the following equation grid type J:
    [0228]
    [0229]
    [0230] Since this signal T [n is simply a value at a certain moment of time, it is not able to capture the direction of a rotor, which must be maintained in time, so to capture it an integral is made using a sliding window which adds the last ones, by means of the equation:
    [0231]
    [0232]
    [0233]
    [0234]
    [0235] Additionally, in this signal r [n] we apply a first and a second threshold, the values of the signal r [n] above a positive value said first threshold allow us to detect rotors that match the circular pattern and the values below from a negative value of the second threshold rotors with rotation contrary to the reference. That is, if the reference pattern is a circle in a clockwise direction, if there is a positive value above the first threshold it will be a rotor in the clockwise direction, if the value is below the second threshold it will be an activation with a pattern anti-clock Once detected, the information is sent to the warning unit (4) to indicate where and when there is a rotor.
    [0236]
    [0237] Additionally, step b) may include obtaining and representing other indicators such as indicators used to assist electrophysiological doctors during the ablation procedure. Some of these indices are the dominant frequency, the regularity index that indicates the frequency domain of the power ratio, or the organization index that indicates the degree of ordering of the FA with respect to the harmonics in the frequency domain.
    [0238]
    [0239] Finally, stage c) generates a warning signal that is transferred to the monitor to reproduce the electrophysiological activation pattern, indicate information of anomalous electrophysiology activation nodes, as well as other other indicators and be able to guide the user in real time.
    权利要求:
    Claims (15)
    [1]
    1. - System (1) for automatic detection in real time of activation nodes characteristic of abnormal electrophysiological patterns, where the system is designed to receive a first signal comprising an electrophysiological signal and coming from a first set of electrodes , wherein the system is characterized by comprising:
    • a data processing unit (3), configured to receive the first signal and which in turn comprises:
    • a memory with instructions and a plurality of pre-established electrophysiological activation patterns, and
    • a microprocessor linked to the memory, where the instructions enable the microprocessor to:
    or generate an electrophysiological activation pattern of the first signal,
    or compare the electrophysiological activation pattern with at least one pre-established electrophysiological activation pattern to detect the activation nodes, and
    or generating, when the activation nodes are detected, a warning signal comprising information of the activation nodes.
    [2]
    2. - System (1), according to claim 1, characterized in that it additionally comprises an analog / digital converter (2) linked to the data processing unit (3) and linked to at least the first set of electrodes, wherein the analog / digital converter (2) comprises a receiving element for receiving the first signal from the first set of electrodes, a conversion element for converting the first signal to digital format and a transmission element for transmitting it in digital format to the unit of data processing (3).
    [3]
    3. System (1), according to claim 1, characterized in that it additionally comprises a warning unit (4) linked to the data processing unit (3) and configured to receive the warning signal and reproduce it, wherein the Warning unit is selected from: auditory indicators, light indicators, visual indicators, sensory indicators or quantitative indicators.
    [4]
    4. System (1), according to claim 3, characterized in that said warning unit (4) is a monitor and reproduces the position of the electrophysiology activation nodes with respect to the first set of electrodes.
    [5]
    5. - System (1), according to claim 2, characterized in that the analog / digital converter (2) comprises an input intended to be linked with a second set of electrodes configured to generate a reference signal.
    [6]
    6. - System (1), according to claim 1, characterized in that the plurality of pre-established electrophysiological activation patterns comprise at least one isochronous map comprising pre-established propagation vectors whose shape and distribution represents the anomaly produced by an electrophysiological activation.
    [7]
    7. - System (1), according to claim 1, characterized in that the pre-established electrophysiological activation patterns are selected among linear, non-linear, polynomial, fixed-value isochronous maps based on an activation extracted from the electrophysiological signal, of fixed value based on a mathematical model of electrophysiological signal or a variable value based on machine learning.
    [8]
    8. - System (1), according to claim 1, characterized in that additionally the instructions enable the microprocessor to pre-process the first signal and eliminate noise.
    [9]
    9. - Method for automatic real-time detection of activation nodes in electrophysiological patterns that makes use of the system described in claims 1-8, characterized in that it comprises the following steps:
    a) generate an electrophysiological activation pattern of the first pre-processed signal, b) compare the electrophysiological activation pattern with at least one pre-established electrophysiological activation pattern to detect activation nodes, and c) generate a warning signal comprising information of the activation nodes.
    [10]
    10. - Method according to claim 9, characterized in that it additionally comprises a previous stage of pre-processing of the first signal to eliminate noise,
    [11]
    11. - Method according to claim 9, characterized in that the previous step comprises:
    • eliminate electrical noise by filtering, and
    • eliminate electrophysiological noise by detecting and subtracting unwanted electrophysiological signals,
    [12]
    12. - Method according to claim 9, characterized in that step a) comprises:
    • generate a second signal comprising the approximation of the first pre-processed signal,
    • generate a third signal comprising the interpolation of the second signal,
    • detect local activation times included in the third signal,
    • generate, from the third signal and activation times, a fourth signal, • represent the fourth signal in the form of an isochronous map,
    • detect the presence of activation nodes,
    • generate a propagation vector for each activation node, and
    • generate an electrophysiological pattern that includes the isochronous map, the activation nodes and their propagation vectors.
    [13]
    13. Method according to claim 9, characterized in that step b) comprises:
    • compare the electrophysiological pattern with at least one of the preset electrophysiological activation patterns by the point-to-point scalar product, and
    • detect the direction of the propagation vector by applying a first and a second threshold in the electrophysiological pattern.
    [14]
    14. - Method according to claim 9, characterized in that step c) comprises:
    • generate a warning signal when the activation node is detected and indicate the direction of the sector.
    [15]
    15. Method according to claim 9, characterized in that the method sends the warning signal to the monitor so that it reproduces it graphically.
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    同族专利:
    公开号 | 公开日
    ES2706537B2|2020-08-05|
    WO2019063861A1|2019-04-04|
    引用文献:
    公开号 | 申请日 | 公开日 | 申请人 | 专利标题
    US20060122526A1|2004-12-02|2006-06-08|Omer Berenfeld|Method and algorithm for spatially identifying sources of cardiac fibrillation|
    US20140243641A1|2012-08-27|2014-08-28|Birinder Robert Boveja|Methods and system for real-time cardiac mapping|
    US20150080752A1|2013-09-16|2015-03-19|Biotronik Se & Co. Kg|Device for automatic mapping of complex fractionated atrial electrogram|
    EP3092944A1|2015-05-13|2016-11-16|EP Solutions SA|Combined electrophysiological mapping and cardiac ablation methods, systems, components and devices|
    CN107019507A|2016-01-14|2017-08-08|韦伯斯特生物官能有限公司|System and method for detecting the focal source of atrial fibrillation|
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    PCT/ES2018/070612| WO2019063861A1|2017-09-29|2018-09-21|System and method for automatically detecting anomalous electrophysiological patterns|
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