Patents by Inventor Derek Vincent Exner
Derek Vincent Exner 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).
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Patent number: 10905345Abstract: The present disclosure uses physiological data, ECG signals as an example, to evaluate cardiac structure and function in mammals. Two approaches are presented, e.g., a model-based analysis and a space-time analysis. The first method uses a modified Matching Pursuit (MMP) algorithm to find a noiseless model of the ECG data that is sparse and does not assume periodicity of the signal. After the model is derived, various metrics and subspaces are extracted to image and characterize cardiovascular tissues using complex-sub-harmonic-frequencies (CSF) quasi-periodic and other mathematical methods. In the second method, space-time domain is divided into a number of regions, the density of the ECG signal is computed in each region and inputted into a learning algorithm to image and characterize the tissues.Type: GrantFiled: February 13, 2019Date of Patent: February 2, 2021Assignee: Analytics for Life Inc.Inventors: Sunny Gupta, Mohsen Najafi Yazdi, Timothy William Fawcett Burton, Shyamlal Ramchandani, Derek Vincent Exner
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Publication number: 20190313926Abstract: The present disclosure uses physiological data, ECG signals as an example, to evaluate cardiac structure and function in mammals. Two approaches are presented, e.g., a model-based analysis and a space-time analysis. The first method uses a modified Matching Pursuit (MMP) algorithm to find a noiseless model of the ECG data that is sparse and does not assume periodicity of the signal. After the model is derived, various metrics and subspaces are extracted to image and characterize cardiovascular tissues using complex-sub-harmonic-frequencies (CSF) quasi-periodic and other mathematical methods. In the second method, space-time domain is divided into a number of regions, the density of the ECG signal is computed in each region and inputted into a learning algorithm to image and characterize the tissues.Type: ApplicationFiled: February 13, 2019Publication date: October 17, 2019Inventors: Sunny Gupta, Mohsen Najafi Yazdi, Timothy William Fawcett Burton, Shyamlal Ramchandani, Derek Vincent Exner
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Patent number: 10383535Abstract: Methods and systems for evaluating the electrical activity of the heart to identify novel ECG patterns closely linked to the subsequent development of serious heart rhythm disturbances and fatal cardiac events. Two approaches are describe, for example a model-based analysis and space-time analysis, which are used to study the dynamical and geometrical properties of the ECG data. In the first a model is derived using a modified Matching Pursuit (MMP) algorithm. Various metrics and subspaces are extracted to characterize the risk for serious heart rhythm disturbances, sudden cardiac death, other modes of death, and all-cause mortality linked to different electrical abnormalities of the heart. In the second method, space-time domain is divided into a number of regions (e.g., 12 regions), the density of the ECG signal is computed in each region and input to a learning algorithm to associate them with these events.Type: GrantFiled: March 30, 2018Date of Patent: August 20, 2019Assignee: Analytics For Life Inc.Inventors: Sunny Gupta, Mohsen Najafi Yazdi, Timothy William Fawcett Burton, Shyamlal Ramchandani, Derek Vincent Exner
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Patent number: 10362951Abstract: The present disclosure uses physiological data, ECG signals as an example, to evaluate cardiac structure and function in mammals. Two approaches are presented, e.g., a model-based analysis and a space-time analysis. The first method uses a modified Matching Pursuit (MMP) algorithm to find a noiseless model of the ECG data that is sparse and does not assume periodicity of the signal. After the model is derived, various metrics and subspaces are extracted to image and characterize cardiovascular tissues using complex-sub-harmonic-frequencies (CSF) quasi-periodic and other mathematical methods. In the second method, space-time domain is divided into a number of regions, the density of the ECG signal is computed in each region and inputted into a learning algorithm to image and characterize the tissues.Type: GrantFiled: April 24, 2018Date of Patent: July 30, 2019Assignee: Analytics For Life Inc.Inventors: Sunny Gupta, Mohsen Najafi Yazdi, Timothy William Fawcett Burton, Shyamlal Ramchandani, Derek Vincent Exner
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Publication number: 20180325403Abstract: Methods and systems for evaluating the electrical activity of the heart to identify novel ECG patterns closely linked to the subsequent development of serious heart rhythm disturbances and fatal cardiac events. Two approaches are describe, for example a model-based analysis and space-time analysis, which are used to study the dynamical and geometrical properties of the ECG data. In the first a model is derived using a modified Matching Pursuit (MMP) algorithm. Various metrics and subspaces are extracted to characterize the risk for serious heart rhythm disturbances, sudden cardiac death, other modes of death, and all-cause mortality linked to different electrical abnormalities of the heart. In the second method, space-time domain is divided into a number of regions (e.g., 12 regions), the density of the ECG signal is computed in each region and input to a learning algorithm to associate them with these events.Type: ApplicationFiled: March 30, 2018Publication date: November 15, 2018Inventors: Sunny Gupta, Mohsen Najafi Yazdi, Timothy William Fawcett Burton, Shyamlal Ramchandani, Derek Vincent Exner
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Publication number: 20180303360Abstract: The present disclosure uses physiological data, ECG signals as an example, to evaluate cardiac structure and function in mammals. Two approaches are presented, e.g., a model-based analysis and a space-time analysis. The first method uses a modified Matching Pursuit (MMP) algorithm to find a noiseless model of the ECG data that is sparse and does not assume periodicity of the signal. After the model is derived, various metrics and subspaces are extracted to image and characterize cardiovascular tissues using complex-sub-harmonic-frequencies (CSF) quasi-periodic and other mathematical methods. In the second method, space-time domain is divided into a number of regions, the density of the ECG signal is computed in each region and inputted into a learning algorithm to image and characterize the tissues.Type: ApplicationFiled: April 24, 2018Publication date: October 25, 2018Inventors: Sunny Gupta, Mohsen Najafi Yazdi, Timothy William Fawcett Burton, Shyamlal Ramchandani, Derek Vincent Exner
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Patent number: 10039468Abstract: The present disclosure generally relates to systems and method of a noninvasive electrocardiographic (ECG) technique for characterizing cardiac chamber size and cardiac mechanical function. A mathematical analysis of three-dimensional (3D) high resolution ECG data may be used to estimate chamber size and cardiac mechanical function. For example, high-resolution mammalian ECG signals are analyzed across multiple leads, as 3D orthogonal (X,Y,Z) or 10-channel data for 30 to 1400 seconds to derive estimates of cardiac chamber size and cardiac mechanical function. Multiple mathematical approaches may be used to analyze the dynamical and geometrical properties of the ECG data.Type: GrantFiled: November 12, 2013Date of Patent: August 7, 2018Assignee: Analytics For Life Inc.Inventors: Sunny Gupta, Timothy William Fawcett Burton, Shyamlal Ramchandani, Derek Vincent Exner
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Patent number: 9968275Abstract: The present disclosure uses physiological data, ECG signals as an example, to evaluate cardiac structure and function in mammals. Two approaches are presented, e.g., a model-based analysis and a space-time analysis. The first method uses a modified Matching Pursuit (MMP) algorithm to find a noiseless model of the ECG data that is sparse and does not assume periodicity of the signal. After the model is derived, various metrics and subspaces are extracted to image and characterize cardiovascular tissues using complex-sub-harmonic-frequencies (CSF) quasi-periodic and other mathematical methods. In the second method, space-time domain is divided into a number of regions, the density of the ECG signal is computed in each region and inputted into a learning algorithm to image and characterize the tissues.Type: GrantFiled: May 5, 2017Date of Patent: May 15, 2018Assignee: Analytics For Life Inc.Inventors: Sunny Gupta, Mohsen Najafi Yazdi, Timothy William Fawcett Burton, Shyamlal Ramchandani, Derek Vincent Exner
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Patent number: 9955883Abstract: Methods and systems for evaluating the electrical activity of the heart to identify novel ECG patterns closely linked to the subsequent development of serious heart rhythm disturbances and fatal cardiac events. Two approaches are describe, for example a model-based analysis and space-time analysis, which are used to study the dynamical and geometrical properties of the ECG data. In the first a model is derived using a modified Matching Pursuit (MMP) algorithm. Various metrics and subspaces are extracted to characterize the risk for serious heart rhythm disturbances, sudden cardiac death, other modes of death, and all-cause mortality linked to different electrical abnormalities of the heart. In the second method, space-time domain is divided into a number of regions (e.g., 12 regions), the density of the ECG signal is computed in each region and input to a learning algorithm to associate them with these events.Type: GrantFiled: July 11, 2016Date of Patent: May 1, 2018Assignee: Analytics for Life Inc.Inventors: Sunny Gupta, Mohsen Najafi Yazdi, Timothy William Fawcett Burton, Shyamlal Ramchandani, Derek Vincent Exner
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Publication number: 20170332927Abstract: The present disclosure uses physiological data, ECG signals as an example, to evaluate cardiac structure and function in mammals. Two approaches are presented, e.g., a model-based analysis and a space-time analysis. The first method uses a modified Matching Pursuit (MMP) algorithm to find a noiseless model of the ECG data that is sparse and does not assume periodicity of the signal. After the model is derived, various metrics and subspaces are extracted to image and characterize cardiovascular tissues using complex-sub-harmonic-frequencies (CSF) quasi-periodic and other mathematical methods. In the second method, space-time domain is divided into a number of regions, the density of the ECG signal is computed in each region and inputted into a learning algorithm to image and characterize the tissues.Type: ApplicationFiled: May 5, 2017Publication date: November 23, 2017Inventors: Sunny Gupta, Mohsen Najafi Yazdi, Timothy William Fawcett Burton, Shyamlal Ramchandani, Derek Vincent Exner
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Patent number: 9655536Abstract: The present disclosure uses physiological data, ECG signals as an example, to evaluate cardiac structure and function in mammals. Two approaches are presented, e.g., a model-based analysis and a space-time analysis. The first method uses a modified Matching Pursuit (MMP) algorithm to find a noiseless model of the ECG data that is sparse and does not assume periodicity of the signal. After the model is derived, various metrics and subspaces are extracted to image and characterize cardiovascular tissues using complex-sub-harmonic-frequencies (CSF) quasi-periodic and other mathematical methods. In the second method, space-time domain is divided into a number of regions, the density of the ECG signal is computed in each region and inputted into a learning algorithm to image and characterize the tissues.Type: GrantFiled: March 4, 2016Date of Patent: May 23, 2017Assignee: Analytics for LifeInventors: Sunny Gupta, Mohsen Najafi Yazdi, Timothy William Fawcett Burton, Shyamlal Ramchandani, Derek Vincent Exner
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Publication number: 20170035312Abstract: Methods and systems for evaluating the electrical activity of the heart to identify novel ECG patterns closely linked to the subsequent development of serious heart rhythm disturbances and fatal cardiac events. Two approaches are describe, for example a model-based analysis and space-time analysis, which are used to study the dynamical and geometrical properties of the ECG data. In the first a model is derived using a modified Matching Pursuit (MMP) algorithm. Various metrics and subspaces are extracted to characterize the risk for serious heart rhythm disturbances, sudden cardiac death, other modes of death, and all-cause mortality linked to different electrical abnormalities of the heart. In the second method, space-time domain is divided into a number of regions (e.g., 12 regions), the density of the ECG signal is computed in each region and input to a learning algorithm to associate them with these events.Type: ApplicationFiled: July 11, 2016Publication date: February 9, 2017Inventors: Sunny Gupta, Mohsen Najafi Yazdi, Timothy William Fawcett Burton, Shyamlal Ramchandani, Derek Vincent Exner
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Patent number: 9408543Abstract: Methods and systems for evaluating the electrical activity of the heart to identify novel ECG patterns closely linked to the subsequent development of serious heart rhythm disturbances and fatal cardiac events. Two approaches are describe, for example a model-based analysis and space-time analysis, which are used to study the dynamical and geometrical properties of the ECG data. In the first a model is derived using a modified Matching Pursuit (MMP) algorithm. Various metrics and subspaces are extracted to characterize the risk for serious heart rhythm disturbances, sudden cardiac death, other modes of death, and all-cause mortality linked to different electrical abnormalities of the heart. In the second method, space-time domain is divided into a number of regions (e.g., 12 regions), the density of the ECG signal is computed in each region and input to a learning algorithm to associate them with these events.Type: GrantFiled: August 19, 2013Date of Patent: August 9, 2016Assignee: Analytics For LifeInventors: Sunny Gupta, Mohsen Najafi Yazdi, Timothy William Fawcett Burton, Shyamlal Ramchandani, Derek Vincent Exner
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Publication number: 20160183822Abstract: The present disclosure uses physiological data, ECG signals as an example, to evaluate cardiac structure and function in mammals. Two approaches are presented, e.g., a model-based analysis and a space-time analysis. The first method uses a modified Matching Pursuit (MMP) algorithm to find a noiseless model of the ECG data that is sparse and does not assume periodicity of the signal. After the model is derived, various metrics and subspaces are extracted to image and characterize cardiovascular tissues using complex-sub-harmonic-frequencies (CSF) quasi-periodic and other mathematical methods. In the second method, space-time domain is divided into a number of regions, the density of the ECG signal is computed in each region and inputted into a learning algorithm to image and characterize the tissues.Type: ApplicationFiled: March 4, 2016Publication date: June 30, 2016Inventors: Sunny Gupta, Mohsen Najafi Yazdi, Timothy William Fawcett Burton, Shyamlal Ramchandani, Derek Vincent Exner
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Patent number: 9289150Abstract: The present disclosure uses physiological data, ECG signals as an example, to evaluate cardiac structure and function in mammals. Two approaches are presented, e.g., a model-based analysis and a space-time analysis. The first method uses a modified Matching Pursuit (MMP) algorithm to find a noiseless model of the ECG data that is sparse and does not assume periodicity of the signal. After the model is derived, various metrics and subspaces are extracted to image and characterize cardiovascular tissues using complex-sub-harmonic-frequencies (CSF) quasi-periodic and other mathematical methods. In the second method, space-time domain is divided into a number of regions, the density of the ECG signal is computed in each region and inputted into a learning algorithm to image and characterize the tissues.Type: GrantFiled: August 19, 2013Date of Patent: March 22, 2016Assignee: ANALYTICS FOR LIFEInventors: Sunny Gupta, Mohsen Najafi Yazdi, Timothy William Fawcett Burton, Shyamlal Ramchandani, Derek Vincent Exner
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Publication number: 20150133803Abstract: The present disclosure generally relates to systems and method of a noninvasive electrocardiographic (ECG) technique for characterizing cardiac chamber size and cardiac mechanical function. A mathematical analysis of three-dimensional (3D) high resolution ECG data may be used to estimate chamber size and cardiac mechanical function. For example, high-resolution mammalian ECG signals are analyzed across multiple leads, as 3D orthogonal (X,Y,Z) or 10-channel data for 30 to 1400 seconds to derive estimates of cardiac chamber size and cardiac mechanical function. Multiple mathematical approaches may be used to analyze the dynamical and geometrical properties of the ECG data.Type: ApplicationFiled: November 12, 2013Publication date: May 14, 2015Applicant: ANALYTICS FOR LIFEInventors: Sunny Gupta, Timothy William Fawcett Burton, Shyamlal Ramchandani, Derek Vincent Exner