# US 12,004,862 B2 · Removing far-field from intracardiac signals > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,004,862 B2 - Document ID: US12004862B2 - Status: Granted - Kind: B2 - Application number: 17/352,747 - Filed: 2021-06-21 - Published: 2022-03-03 - Granted: 2024-06-11 - Earliest claimed: 2020-08-27 - Assignees: Biosense Webster (Israel) Ltd. - Applicants: Biosense Webster (Israel) Ltd. - Inventors: Vadim Gliner - Web page: https://patentgrep.com/patent/US12004862B2 ## CPC - A61B 5/346: Analysis of electrocardiograms - A61B 5/367: Electrophysiological study [EPS], e.g. electrical activation mapping or electro-anatomical mapping - A61B 5/287: Holders for multiple electrodes, e.g. electrode catheters for electrophysiological study [EPS] - A61B 5/343: Potential distribution indication - A61B 5/6852: {Catheters} - A61B 5/6858: {with a distal basket, e.g. expandable basket} - A61B 5/7203: {for noise prevention, reduction or removal} - A61B 5/7267: {involving training the classification device} - G06N 3/04: Architecture, e.g. interconnection topology - G06N 3/0455: Auto-encoder networks; Encoder-decoder networks - G06N 3/0499: Feedforward networks - G06N 3/08: Learning methods - G06N 3/09: Supervised learning - G16H 15/00: ICT specially adapted for medical reports, e.g. generation or transmission thereof - G16H 40/67: for remote operation - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - A61B 5/065: {Determining position of the probe employing exclusively positioning means located on or in the probe, e.g. using position sensors arranged on the probe} - A61B 18/1492: {having a flexible, catheter-like structure, e.g. for heart ablation (A61B18/1477 takes precedence)} - A61B 2018/0016: {Energy applicators arranged in a two- or three dimensional array} - A61B 2018/00196: {reciprocating lengthwise} - A61B 2018/00267: {having a basket shaped structure} - A61B 2018/00357: {Endocardium} - A61B 2018/00577: {Ablation} - A61B 2018/00839: {Bioelectrical parameters, e.g. ECG, EEG} - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract In one embodiment, a method includes receiving first intracardiac signals including first far-field components captured by at least one first sensing electrode of a first catheter, the at least one sensing electrode being in contact with tissue of a cardiac chamber of a first living subject, and at least one far-field signal captured from at least one far-field electrode inserted into the cardiac chamber and not in contact with the tissue of the cardiac chamber, training a neural network to remove far-field components from intracardiac signals responsively to the first intracardiac signals and the at least one far-field signal, receiving second intracardiac signals captured by at least one second sensing electrode of a second catheter inserted into a cardiac chamber of a second living subject, and applying the trained neural network to the second intracardiac signals to remove respective second far-field components from the second intracardiac signals. ## Claims ### Claim 1 A method for analyzing signals, comprising: receiving: first intracardiac signals comprising first far-field components captured by at least one first sensing electrode of a first catheter, the at least one sensing electrode being in contact with tissue of a cardiac chamber of a first living subject; and at least one far-field signal captured from at least one far-field electrode inserted into the cardiac chamber and not in contact with the tissue of the cardiac chamber; training an artificial neural network to remove far-field components from intracardiac signals responsively to the received first intracardiac signals and the at least one far-field signal; receiving second intracardiac signals captured by at least one second sensing electrode of a second catheter inserted into a cardiac chamber of a second living subject; and applying the trained artificial neural network to the second intracardiac signals to remove respective second far-field components from the second intracardiac signals. ### Claim 2 (depends on 1) The method according to claim 1, further comprising computing the first intracardiac signals with the respective first far-field components removed responsively to the at least one far-field signal, wherein the training includes training the artificial neural network responsively to the computed first intracardiac signals with the respective first far-field components removed. ### Claim 3 (depends on 1) The method according to claim 1, wherein the training comprises training an autoencoder comprising an encoder and a decoder. ### Claim 4 (depends on 1) The method according to claim 1, further comprising rendering to a display a representation of at least one of the second intracardiac signals with a respective one of the second far-field components removed. ### Claim 5 (depends on 1) The method according to claim 1, further comprising generating and rendering to a display, an electroanatomic map responsively to at least one of the second intracardiac signals with a respective one of the second far-field components removed. ### Claim 6 (depends on 1) The method according to claim 1, wherein the first catheter includes the at least one far-field electrode. ### Claim 7 A software product, comprising a non-transient computer-readable medium in which program instructions are stored, which instructions, when read by a central processing unit (CPU), cause the CPU to: receive: first intracardiac signals comprising first far-field components captured by at least one first sensing electrode of a first catheter, the at least one sensing electrode being in contact with tissue of a cardiac chamber of a first living subject; and at least one far-field signal captured from at least one far-field electrode inserted into the cardiac chamber and not in contact with the tissue of the cardiac chamber; train an artificial neural network to remove far-field components from intracardiac signals responsively to the received first intracardiac signals and the at least one far-field signal receive second intracardiac signals captured by at least one second sensing electrode of a second catheter inserted into a cardiac chamber of a second living subject; and apply the trained artificial neural network to the second intracardiac signals to remove respective second far-field components from the second intracardiac signals. ### Claim 8 (depends on 7) The software product according to claim 7, wherein the instructions, when read by the CPU, also cause the CPU to: compute the first intracardiac signals with the respective first far-field components removed responsively to the at least one far-field signal; and train the artificial neural network responsively to the computed first intracardiac signals with the respective first far-field components removed. ### Claim 9 (depends on 7) The software product according to claim 7, wherein the instructions, when read by the CPU, also cause the CPU to render to a display a representation of at least one of the second intracardiac signals with a respective one of the second far-field components removed. ### Claim 10 (depends on 7) The software product according to claim 7, wherein the instructions, when read by the CPU, also cause the CPU to generate and render to a display, an electroanatomic map responsively to at least one of the second intracardiac signals with a respective one of the second far-field components removed. ### Claim 11 A medical system, comprising: a first catheter including at least one first sensing electrode configured to be inserted into a cardiac chamber of a first living subject; and a second catheter comprising at least one second sensing electrode configured to be inserted into a cardiac chamber of a second living subject; a processor configured to: receive: first intracardiac signals comprising first far-field components captured by the at least one first sensing electrode of the first catheter, the at least one sensing electrode being in contact with tissue of the cardiac chamber of the first living subject; and at least one far-field signal captured from the at least one far-field electrode inserted into the cardiac chamber and not in contact with the tissue of the cardiac chamber; train an artificial neural network to remove far-field components from intracardiac signals responsively to the received first intracardiac signals and the at least one far-field signal; receive second intracardiac signals captured by the at least one second sensing electrode of the second catheter inserted into the cardiac chamber of the second living subject; and apply the trained artificial neural network to the second intracardiac signals to remove respective second far-field components from the second intracardiac signals. ### Claim 12 (depends on 11) The system according to claim 11, wherein the processor is configured to: compute the first intracardiac signals with the respective first far-field components removed responsively to the at least one far-field signal; and train the artificial neural network responsively to the computed first intracardiac signals with the respective first far-field components removed. ### Claim 13 (depends on 11) The system according to claim 11, wherein the artificial neural network comprises an autoencoder including an encoder and a decoder, the processor being configured to train the autoencoder to remove the far-field components from intracardiac signals responsively to the received first intracardiac signals and the at least one far-field signal. ### Claim 14 (depends on 11) The system according to claim 11, wherein the trained artificial neural network comprises an autoencoder including an encoder and a decoder, the processor being configured to apply the autoencoder to the second intracardiac signals to remove the respective second far-field components from the second intracardiac signals. ### Claim 15 (depends on 11) The system according to claim 11, further comprising a display, wherein the processor is configured to render to the display a representation of at least one of the second intracardiac signals with a respective one of the second far-field components removed. ### Claim 16 (depends on 11) The system according to claim 11, further comprising a display, wherein the processor is configured to generate and render to the display, an electroanatomic map responsively to at least one of the second intracardiac signals with a respective one of the second far-field components removed. ### Claim 17 (depends on 11) The system according to claim 11, wherein the first catheter includes the at least one far-field electrode. ### Claim 18 (depends on 17) The system according to claim 17, wherein the first catheter comprises an expandable distal end basket assembly, the at least one first sensing electrode being disposed on the basket assembly, the at least one far-field electrode being disposed in the basket assembly to prevent the at least one far-field electrode from contacting the tissue of the cardiac chamber of the first living subject.