Patents by Inventor Peter A. Noseworthy
Peter A. Noseworthy has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
-
Patent number: 12387847Abstract: An apparatus and method for detecting a level of cardiovascular disease. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to: receive a plurality of voltage-time data, generate at least a feature vector from the voltage-time data by at least a feature model, input the at least feature vector into a cardiovascular classification model, generate at least a disease indication in a subject using the classification model, wherein the disease indication comprises a level of myocarditis, and display the at least a disease indication.Type: GrantFiled: April 26, 2024Date of Patent: August 12, 2025Assignee: Mayo Foundation for Medical Education and ResearchInventors: Itzhak Zachi Attia, Paul A. Friedman, Peter A. Noseworthy, Joerg Herrmann, Yash Gupta, John Rincón-Hekking, Ashim Prasad, Rakesh Barve, Samir Awasthi
-
Publication number: 20240374221Abstract: Apparatuses and methods are provided to predict or diagnose an ischemic event, such as a stroke or a transient ischemic attack (TIA). A machine-learning model such as a neural network is generated that allows for recognition of an ECG consistent with an ischemic event. A system is trained and used to process a recording of ECG data from a patient to generate a prediction indicating a likelihood that the patient will experience a stroke. In other examples, a system is trained and used to process a recording of ECG data from a patient and detect an ischemic event for the patient who did not appear to have such an ischemic event.Type: ApplicationFiled: July 23, 2024Publication date: November 14, 2024Applicant: Mayo Foundation for Medical Education and ResearchInventors: Itzhak Zachi Attia, Paul A. Friedman, Samuel J. Asirvatham, Peter A. Noseworthy, Suraj Kapa, Francisco Lopez-Jimenez
-
Publication number: 20240358311Abstract: Systems, methods, devices, and other techniques for processing an ECG recording to assess a condition of a mammal. Assessing the condition of the mammal can include screening for atrial fibrillation, and screening for atrial fibrillation can include obtaining a first neural network input, the first neural network input representing an electrocardiogram (ECG) recording of the mammal, and processing the first neural network input with a neural network to generate an atrial fibrillation prediction for the mammal.Type: ApplicationFiled: July 10, 2024Publication date: October 31, 2024Applicant: Mayo Foundation for Medical Education and ResearchInventors: Itzhak Zachi Attia, Paul A. Friedman, Peter A. Noseworthy
-
Publication number: 20240363247Abstract: An apparatus and method for detecting a level of cardiovascular disease. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to: receive a plurality of voltage-time data, generate at least a feature vector from the voltage-time data by at least a feature model, input the at least feature vector into a cardiovascular classification model, generate at least a disease indication in a subject using the classification model, wherein the disease indication comprises a level of myocarditis, and display the at least a disease indication.Type: ApplicationFiled: April 26, 2024Publication date: October 31, 2024Applicant: Mayo Foundation for Medical Education and ResearchInventors: Itzhak Zachi Attia, Paul A. Friedman, Peter A. Noseworthy, Joerg Herrmann, Yash Gupta, John Rincón-Hekking, Ashim Prasad, Rakesh Barve, Samir Awasthi
-
Patent number: 12053287Abstract: Systems, methods, devices, and other techniques for processing an ECG recording to assess a condition of a mammal. Assessing the condition of the mammal can include screening for atrial fibrillation, and screening for atrial fibrillation can include obtaining a first neural network input, the first neural network input representing an electrocardiogram (ECG) recording of the mammal, and processing the first neural network input with a neural network to generate an atrial fibrillation prediction for the mammal.Type: GrantFiled: October 24, 2019Date of Patent: August 6, 2024Assignee: Mayo Foundation for Medical Education and ResearchInventors: Itzhak Zachi Attia, Paul A. Friedman, Peter A. Noseworthy
-
Patent number: 12053305Abstract: Apparatuses and methods are provided to predict or diagnose an ischemic event, such as a stroke or a transient ischemic attack (TIA). A machine-learning model such as a neural network is generated that allows for recognition of an ECG consistent with an ischemic event. A system is trained and used to process a recording of ECG data from a patient to generate a prediction indicating a likelihood that the patient will experience a stroke. In other examples, a system is trained and used to process a recording of ECG data from a patient and detect an ischemic event for the patient who did not appear to have such an ischemic event.Type: GrantFiled: March 31, 2022Date of Patent: August 6, 2024Assignee: Mayo Foundation for Medical Education and ResearchInventors: Itzhak Zachi Attia, Paul A. Friedman, Samuel J. Asirvatham, Peter A. Noseworthy, Suraj Kapa, Francisco Lopez-Jimenez
-
Publication number: 20230218238Abstract: Systems and methods are provided for quantifying and monitoring liver disease in a patient from voltage-time data. A method comprises receiving voltage-time data of a subject, the voltage-time data comprising voltage data of a plurality of leads of an electrocardiograph; generating a feature vector from the voltage-time data; providing the feature vector to a pretrained learning system; receiving from the pretrained learning system a status of liver disease in the subject.Type: ApplicationFiled: January 9, 2023Publication date: July 13, 2023Inventors: Puru Rattan, Peter A. Noseworthy, Itzhak Zachi Attia, Paul A. Friedman, Douglas A. Simonetto, Vijay Shah, Joseph C. Ahn, Patrick S. Kamath
-
Publication number: 20220218289Abstract: Apparatuses and methods are provided to predict or diagnose an ischemic event, such as a stroke or a transient ischemic attack (TIA). A machine-learning model such as a neural network is generated that allows for recognition of an ECG consistent with an ischemic event. A system is trained and used to process a recording of ECG data from a patient to generate a prediction indicating a likelihood that the patient will experience a stroke. In other examples, a system is trained and used to process a recording of ECG data from a patient and detect an ischemic event for the patient who did not appear to have such an ischemic event.Type: ApplicationFiled: March 31, 2022Publication date: July 14, 2022Applicant: Mayo Foundation for Medical Education and ResearchInventors: Itzhak Zachi Attia, Paul A. Friedman, Samuel J. Asirvatham, Peter A. Noseworthy, Suraj Kapa, Francisco Lopez-Jimenez
-
Patent number: 11337637Abstract: Systems, methods, devices, and techniques for analyzing and applying features of a T-wave derived from an electrocardiogram. A computing system can receive a set of data that characterizes an electrocardiogram of a patient. The system can analyze the set of data to identify a T-wave that occurs in the electrocardiogram. The system can determine values of one or more features of the T-wave and provide the information that identifies the values of the one or more features of the T-wave to a user.Type: GrantFiled: August 31, 2017Date of Patent: May 24, 2022Assignee: Mayo Foundation for Medical Education and ResearchInventors: Peter A. Noseworthy, Bo Qiang, Paul A. Friedman, Alan M. Sugrue, Vaclav Kremen, Bryan L. Striemer, Charles J. Bruce, Virend K. Somers, Kevin E. Bennet, Michael J. Ackerman, Samuel J. Asirvatham, John J. Dillon, Christopher V. DeSimone
-
Patent number: 11317872Abstract: Apparatuses and methods are provided to predict or diagnose an ischemic event, such as a stroke or a transient ischemic attack (TIA). A machine-learning model such as a neural network is generated that allows for recognition of an ECG consistent with an ischemic event. A system is trained and used to process a recording of ECG data from a patient to generate a prediction indicating a likelihood that the patient will experience a stroke. In other examples, a system is trained and used to process a recording of ECG data from a patient and detect an ischemic event for the patient who did not appear to have such an ischemic event.Type: GrantFiled: December 14, 2018Date of Patent: May 3, 2022Assignee: Mayo Foundation for Medical Education and ResearchInventors: Itzhak Zachi Attia, Paul A. Friedman, Samuel J. Asirvatham, Peter A. Noseworthy, Suraj Kapa, Francisco Lopez-Jimenez
-
Publication number: 20220047201Abstract: Systems, methods, devices, and other techniques for processing an ECG recording to assess a condition of a mammal.Type: ApplicationFiled: October 24, 2019Publication date: February 17, 2022Inventors: Itzhak Zachi Attia, Paul A. Friedman, Peter A. Noseworthy
-
Publication number: 20210275080Abstract: Systems, methods, devices, and techniques for analyzing and applying features of a T-wave derived from an electrocardiogram. A computing system can receive a set of data that characterizes an electrocardiogram of a patient. The system can analyze the set of data to identify a T-wave that occurs in the electrocardiogram. The system can determine values of one or more features of the T-wave and provide the information that identifies the values of the one or more features of the T-wave to a user.Type: ApplicationFiled: August 31, 2017Publication date: September 9, 2021Applicant: Mayo Foundation for Medical Education and ResearchInventors: Peter A. Noseworthy, Bo Qiang, Paul A. Friedman, Alan M. Sugrue, Vaclav Kremen, Bryan L. Striemer, Charles J. Bruce, Virend K. Somers, Kevin E. Bennet, Michael J. Ackerman, Samuel J. Asirvatham, John J. Dillon, Christopher V. DeSimone
-
Publication number: 20210204858Abstract: Data is generated that describes features of an ECG of a subject. This generation can include receiving ECG data that was generated to reflect cardiac activity of a particular mammal; submitting the ECG data to a plurality of cardiac classifiers, each cardiac classifier configured to identify, in the ECG, at least some of a plurality of cardiac features that are within a particular feature-class; receiving, from each of the plurality of cardiac classifiers, a classification message containing data of the cardiac classifiers identifying of cardiac features in the ECG; and assembling, from the received classification messages, ECG features for the ECG, the ECG features identifying at least some features of different feature-classes.Type: ApplicationFiled: May 23, 2019Publication date: July 8, 2021Inventors: Itzhak Zachi Attia, Paul A. Friedman, Peter A. Noseworthy
-
Publication number: 20190183431Abstract: Apparatuses and methods are provided to predict or diagnose an ischemic event, such as a stroke or a transient ischemic attack (TIA). A machine-learning model such as a neural network is generated that allows for recognition of an ECG consistent with an ischemic event. A system is trained and used to process a recording of ECG data from a patient to generate a prediction indicating a likelihood that the patient will experience a stroke. In other examples, a system is trained and used to process a recording of ECG data from a patient and detect an ischemic event for the patient who did not appear to have such an ischemic event.Type: ApplicationFiled: December 14, 2018Publication date: June 20, 2019Applicant: Mayo Foundation for Medical Education and ResearchInventors: Itzhak Zachi Attia, Paul A. Friedman, Samuel J. Asirvatham, Peter A. Noseworthy, Suraj Kapa, Francisco Lopez-Jimenez