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EP-4444178-B1 - DETECTION OF FRACTIONATED SIGNALS IN STABLE ARRHYTHMIAS

EP4444178B1EP 4444178 B1EP4444178 B1EP 4444178B1EP-4444178-B1

Inventors

  • RAVUNA, ELIYAHU
  • YANOVICH, Nir
  • ETIN ZAIT, Natalia
  • ZAIDES, Leonid
  • SEGEV, Meytal
  • NAKAR, ELAD

Dates

Publication Date
20260506
Application Date
20221120

Claims (15)

  1. A system (21), comprising: a memory (33) configured to store cardiac signals; and a processor (28), which is configured to: receive a cardiac signal that is sensed by an electrode (22) at a tissue location inside the heart; identify fractionations in the cardiac signal; compare the fractionations, identified at the tissue location, between first and second cardiac cycles of the cardiac signal; based on the comparing, estimate a likelihood that the tissue location is causing a stable arrhythmia; and based on the estimated likelihood, indicate the tissue location to a user as likely to be causing the stable arrhythmia.
  2. The system according to claim 1, wherein the processor is configured to indicate the tissue location by indicating to the user a score of the likelihood that the tissue location is causing the stable arrhythmia.
  3. The system according to claim 1, wherein the processor is configured to indicate the tissue location by recommending ablating the tissue location.
  4. The system according to claim 1, wherein the processor is configured to receive the cardiac signal by receiving a bipolar electrogram.
  5. The system according to claim 1, wherein the processor is configured to identify the fractionations by estimating baseline noise of the cardiac signal.
  6. The system according to claim 5, wherein the processor is configured to identify the fractionations by dynamically adjusting the threshold for a non-linear energy operator (NLEO) according to a ratio between a baseline noise and a peak-to-peak value of the EGM signal, the baseline noise given by a minimum value of a sliding maximum window of an absolute value of the signal.
  7. The system according to claim 1, wherein the processor is configured to identify the fractionations by comparing time periods of the cardiac signal in which at least a minimal predefined portion of the cardiac cycle does not contain any fractionation.
  8. The system according to claim 1, wherein the processor is configured to identify the fractionations by calculating, over a time interval, a ratio between peak-to-peak voltage of the cardiac signal and a duration of the time interval, and, using the ratio, to discriminate the fractionations from noise.
  9. The system according to claim 1, wherein the processor is configured to estimate the likelihood by applying a logistic-regression-based classifier.
  10. The system according to claim 1, wherein the processor is configured to compare the fractionations by adjusting time-onset and time-offset of at least one of the fractionations.
  11. The system according to claim 1, wherein the processor is configured to identify the fractionations in a given cardiac cycle by using an extended window-of-interest (WOI) to capture all fractionations starting within a cardiac cycle.
  12. The system according to claim 1, wherein the processor is configured to estimate the likelihood based on a number of zero crossings occurring in the cardiac signal within each fractionation.
  13. The system according to claim 1, wherein the processor is configured to estimate the likelihood based on a number of local extrema occurring in a low-pass filtered cardiac signal within each fractionation.
  14. The system according to claim 1, wherein the processor is configured to compare the fractionations by representing fractionation windows as triangles.
  15. The system according to claim 1, wherein the processor is configured to indicate the tissue location by generating and displaying an electrophysiological (EP) map (800) to the user, overlaid with a score of the likelihood that the tissue location is causing the stable arrhythmia, and optionally comprising a scale (804) on a graphical user interface (GUI) that the user can use to adjust a threshold score above which only tissue locations whose score is above that threshold are observed on the EP map.

Description

FIELD OF THE DISCLOSURE This disclosure relates generally to analysis of electrophysiological (EP) signals, and specifically to a method for detecting fractionated electrograms in stable arrhythmias. BACKGROUND OF THE DISCLOSURE Isolating complex fractionated atrial electrograms (CFAEs) to characterize an arrythmia was previously suggested in the literature. For example, in the conference paper "A new approach for automated location of active segments in intracardiac electrograms," World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany, Nguyen M.P. et al. describe a method to locate signal complexes corresponding to electrophysiological activity. The disclosed method identifies CFAE by applying a Non-Linear Energy Operator (NLEO) followed by Gaussian smoothing to time-discrete atrial EGM signals. The method can this way identify areas in atrium tissue with complex fractionated atrial electrograms (CFAEs) that are, among others, responsible for the maintenance of atrial fibrillation (AFib). Those areas are ideal target sites for ablation to eliminate AFib and restore normal rhythm. The method introduces automated identification of CFAEs with signal processing algorithms to assist to develop an objective strategy for AFib ablation. The idea behind this algorithm is based on the idea of Pan-Tompkins' QRS-detection algorithm. However in this approach, the extracted signal feature is the signal energy and therefore the algorithm takes into account not only information of the frequency but also of the amplitude. With adaptive thresholding the algorithm is capable to manage changes in the signal dynamics. The results were validated by experts and the algorithm shows a robust performance. As another example, Frontera A et al.. in "Electrogram signature of specific activation patterns: Analysis of atrial tachycardias at high-density endocardial mapping," Heart Rhythm. 2018 Jan;15(1):28-37, describe results from twenty-five consecutive patients that underwent high-density atrial mapping during atrial tachycardias. Bipolar EGMs were recorded with a 64-electrode basket catheter. The following atrial phenomena were identified: slow conduction (SC) areas, lines of block (LB), wavefront collisions (WFC), pivot sites (PS), and gaps. EGMs collected at these predefined areas were analyzed in terms of amplitude, duration, and morphology. It was found that specific EGM characteristics in atrial tachycardia can be reproducibly linked to electrophysiological mechanisms. High-voltage and short-duration EGMs are associated with collision sites and PS that are unlikely to form critical sites for ablation; long-duration, low-voltage EGMs are associated with SC. However, not all SC regions will lie within the critical circuit and identification by only EGM characteristics cannot guide ablation. In US2017202515A1, there is described a method of atrial rotational activity pattern (RAP) source detection which includes detecting, via a plurality of sensors, electro-cardiogram (ECG) signals over time, each ECG signal detected via one of the plurality of sensors and indicating electrical activity of a heart. BRIEF DESCRIPTION OF THE DRAWINGS The present disclosure will be more fully understood from the following detailed description of the examples thereof, taken together with the drawings, in which: Fig. 1 is a schematic, pictorial illustration of a catheter-based electrophysiological (EP) mapping system, according to an example of the present disclosure;Fig. 2 is a flow chart that schematically illustrates a method and algorithm for the detection of fractionated signals in stable arrhythmias, according to an example of the present disclosure;Fig. 3 shows an example graph of an electrogram acquired using the system of Fig. 1, the electrogram superimposed with an extended window of interest (WOI) in a step described in Fig. 2, according to an example of the present disclosure;Fig. 4 shows example graphs of an electrogram acquired using the system of Fig. 1, and the signal after applying the NLEO and Gaussian filter step described in Fig. 2, according to an example of the present disclosure;Fig. 5 is a graph that schematically shows two fractionations with respective windows found in a step described in Fig. 2, and an intersection region of the windows, according to an example of the present disclosure;Fig. 6 is a graph that schematically illustrates the fine-tuning step of a fractionation duration described in Fig. 2, according to an example of the present disclosure;Figs. 7A and 7B are illustrations of two types of signals, with the top line being schematic graphs of fractionations and the bottom line schematic graphs of noises, according to an example of the present disclosure; andFig. 8 is a schematic, pictorial volume rendering of an EP map of a ventricle indicating tissue locations and magnitude of fractionations that may cause a stable arrhythmia, according to an example of the present d