Abstract: Electrical activity is sensed from a plurality of electrodes of a multi-electrode catheter. For each of a plurality of surface locations of cardiac tissue, a processor identifies first electrical signals originating at a first depth and second electrical signals originating at a second depth, estimates a first near-field signal originating at the first depth for each of the first electrical signals and detects scar borders at the first depth based on the first near-field signals, and estimates a second near-field signal originating at the second depth for each of the second electrical signals and detects scar borders at the second depth based on the second near-field signals. Based on the scar borders detected at the first and second depths, the processor visually displays first and second maps of the heart depicting scar tissue at the first and second depths, respectively.

Abstract: An ablation procedure guidance method is provided herein. The ablation procedure guidance method is implemented by a generation engine executing on a processor. The ablation procedure guidance method includes receiving inputs including images and conduction velocity vector estimations and generating a digital twin of an anatomical structure utilizing the images and the conduction velocity vector estimations. The ablation procedure guidance method also includes presenting, via a user interface of the generation engine, the digital twin to provide precision ablation guidance of the anatomical structure and provide electrophysiology information of the anatomical structure.

Abstract: A system and method include a memory storing processor executable code for a denoised autoencoder, and one or more processors coupled to the memory to execute the processor executable code to receive raw signal data comprising signal noise, encode, by the denoised autoencoder, the raw signal data by performing a denoising autoencoder operation to produce a latent representation, and decode, by the denoised autoencoder, the latent representation to produce clean signal data reconstructed without the signal noise. A first filter is applied to a signal to emphasize activity within the signal and to produce a first modified signal, a rectifier and a second filter are applied to the first modified signal to smooth areas of the first modified signal with clinical importance and to produce a second modified signal, and high frequency energy zones of the second modified signal are automatically detected using an energy threshold to produce a weights vector.

Abstract: Multiple signal mappings of cardiac tissue are created based on signals derived from different sets of electrodes in a multi-electrode catheter within a heart of a patient. A first set of electrodes is selected, each of which is within a first distance of a selected electrode, and a first spatial electrode signal analysis is performed on the electrical activity of the selected electrode based on signals from the first set of electrodes. A second set of electrodes is selected each of which is within a second distance of the selected electrode, and a second spatial electrode signal analysis is performed of the electrical activity of the selected electrode in accordance with signals from the second set of electrodes. This process is repeated for a plurality of further locations within the heart, and used to generate at least first and second electro-anatomical maps of the heart.

Abstract: A multi-electrode catheter with a first plurality of electrodes on a first side and a second plurality of electrodes on a second side is used to detect electrical activity within a heart of a patient. One of the first plurality of electrodes faces toward cardiac tissue at a first location and one of the second plurality of electrodes faces away from the cardiac tissue. A signal analysis is performed for the first location based on first signals received from the electrode facing toward cardiac tissue and second signals received from the electrode facing away from the cardiac tissue. The signal analysis includes application of a weighting to at least one of the first signals and the second signals. This process is repeated for a plurality of further locations of cardiac tissue in the heart and, based on the signal analysis, at least one electro-anatomical map of the heart is generated.

Abstract: The present disclosure provides a method and system for analyzing unipolar electrophysiological (EP) signals acquired by a multi-electrode catheter placed in a ventricle of a patient's heart. The method includes receiving unipolar EP signals, extracting unipolar far-field signals from the unipolar EP signals, and analyzing the extracted unipolar far-field signals to estimate the distribution of scar regions across a thickness of wall tissue of the ventricle. Using the estimated distribution, a unipolar far-field EP map showing the scar regions is generated. The method further includes displaying the unipolar far-field EP map including the scar regions to a user. The system comprises a display device and a processor configured to perform the steps of the method.

Abstract: A system and method include a memory storing processor executable code for a denoised autoencoder, and one or more processors coupled to the memory to execute the processor executable code to receive raw signal data comprising signal noise, encode, by the denoised autoencoder, the raw signal data by performing a denoising autoencoder operation to produce a latent representation, and decode, by the denoised autoencoder, the latent representation to produce clean signal data reconstructed without the signal noise. A first filter is applied to a signal to emphasize activity within the signal and to produce a first modified signal, a rectifier and a second filter are applied to the first modified signal to smooth areas of the first modified signal with clinical importance and to produce a second modified signal, and high frequency energy zones of the second modified signal are automatically detected using an energy threshold to produce a weights vector.

Abstract: A system trains a set of machine-learning models to predict which atrial fibrillation patients would respond to which treatments using ablation lines. The models identify an ablation line treatment for a patient based on atrial fibrillation-related features associated with the patient. The models estimate reconnection probabilities for ablation sites associated with at least one ablation line treatment. The models identify patient atrial fibrillation predictors associated with the patient, based on demographics and heart component dimension parameters associated with the patient, and use the at least one ablation line treatment, reconnection probabilities, patient atrial fibrillation predictors, and patient demographics to predict whether the patient would not respond to pulmonary vein isolation only treatment.

Abstract: Apparatus and methods are described for use of a sensor for monitoring a subject generate a sensor signal in response to the monitoring. Also disclosed is a computer processor to analyze the signal, and in response thereto, identify a correspondence between positions of the subject and occurrences of health events of the subject, and generate an output on the output device, in response to the identified correspondence. Other applications are also described.

Abstract: Apparatus and methods are described for use of a sensor for monitoring a subject generate a sensor signal in response to the monitoring. Also disclosed is a computer processor to analyze the signal, and in response thereto, identify a correspondence between positions of the subject and occurrences of health events of the subject, and generate an output on the output device, in response to the identified correspondence. Other applications are also described.

Abstract: The presently disclosed subject matter includes computer methods and computer systems that enable to select and provide a suitable treatment for AF patients that increases the likelihood of long-term amelioration of AF conditions, and thereby enhances treatment of AF patients. The disclosure provides methods and systems for analysis of the condition of AF patients and the tailoring of personalized treatment regimens based on various AF features obtained from the patients, and the determination of a treatment selected from at least PVI only and PVI plus. To overcome technical difficulties resulting from scarcity of data a machine learning model that is trained using physician recommendation rather than observed treatment outcome is disclosed.

Abstract: The presently disclosed subject matter includes computer methods and computer systems that enable to select and provide a suitable treatment for AF patients that increases the likelihood of long-term amelioration of AF conditions, and thereby enhances treatment of AF patients. The disclosure provides methods and systems for analysis of the condition of AF patients and the tailoring of personalized treatment regimens based on various AF features obtained from the patients, and the determination of a treatment selected from at least PVI only and PVI plus.

Abstract: A system for improving a cardiac ablation procedure includes a recommendation unit configured to provide an initial recommendation for at least one proposed ablation line for an ablation procedure on an anatomy of a patient. The system displays the at least one proposed ablation line on an anatomical map of the anatomy. The recommendation unit comprises a first and a second trained machine-learning model. Both the first and second trained machine-learning models have the same structure.

Abstract: Apparatus and methods are described for use of a sensor for monitoring a subject generate a sensor signal in response to the monitoring. Also disclosed is a computer processor to analyze the signal, and in response thereto, identify a correspondence between positions of the subject and occurrences of health events of the subject, and generate an output on the output device, in response to the identified correspondence. Other applications are also described.

Abstract: An ablation procedure guidance method is provided herein. The ablation procedure guidance method is implemented by a generation engine executing on a processor. The ablation procedure guidance method includes receiving inputs including images and conduction velocity vector estimations and generating a digital twin of an anatomical structure utilizing the images and the conduction velocity vector estimations. The ablation procedure guidance method also includes presenting, via a user interface of the generation engine, the digital twin to provide precision ablation guidance of the anatomical structure and provide electrophysiology information of the anatomical structure.

Abstract: A method includes receiving a data set including medical information of respective patients, respective types of a clinical procedure performed on the patients, and respective survival rates of the patients. A survival tree graph is generated by maximizing a cost function of differences in the survival rates between the types of the clinical treatment procedure. A type of the clinical procedure is selected for a given patient, based on the survival tree graph.

Abstract: Apparatus and methods are described for monitoring a subject. A sensor monitors the subject and generates a sensor signal in response thereto and a plurality of filters are used to filter the sensor signal using respective filter parameters. A computer processor receives the sensor signal, filters the signal with each of two or more of the filters, and in response to a quality of each of the filtered signals, selects one of the plurality of filters to filter the sensor signal. Subsequently, the computer processor detects that the subject has undergone motion, by analyzing the sensor signal, and in response thereto, filters the signal with each of two or more of the filters, and in response to a quality of each of the filtered signals, selects one of the plurality of filters to filter the sensor signal. Other applications are also described.

Abstract: A method and apparatus of aiding a physician in locating an area to perform an ablation on patients with atrial fibrillation (AFIB) includes receiving data at a machine, from at least one device, the data including information relating to a desired location for performing an ablation, generating, by the machine, an optimal location for performing the ablation based upon the data and inputs, and providing an optimal set of ablation parameters for performing the ablation at the location output by the model, or at a location specified by the physician.

Abstract: A method is provided. The method includes receiving input intracardiac signals from a monitoring and processing apparatus. Each of the input intracardiac signals includes artifacts. The method includes encoding, by an autoencoder, the input intracardiac signals utilizing an intracardiac dataset to produce a latent representation. The method also includes decoding, by an autoencoder, the latent representation to produce output intracardiac signals. The output intracardiac signals include the input intracardiac signals reconstructed without the signal artifacts.

Abstract: A system and method include a memory storing processor executable code for a denoised autoencoder, and one or more processors coupled to the memory to execute the processor executable code to receive raw signal data comprising signal noise, encode, by the denoised autoencoder, the raw signal data by performing a denoising autoencoder operation to produce a latent representation, and decode, by the denoised autoencoder, the latent representation to produce clean signal data reconstructed without the signal noise. A first filter is applied to a signal to emphasize activity within the signal and to produce a first modified signal, a rectifier and a second filter are applied to the first modified signal to smooth areas of the first modified signal with clinical importance and to produce a second modified signal, and high frequency energy zones of the second modified signal are automatically detected using an energy threshold to produce a weights vector.