Blood vessel mechanical signal analysis
First Claim
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1. A system for patient signal analysis, comprising:
- a sensor system including sensors that non-invasively acquire at least first and second types of mechanical signal data from a patient, wherein the at least first and second types of mechanical signal data is generated in response to contraction of blood vessels; and
a computer system communicatively coupled to the sensor system, wherein the sensor system is configured to continuously and wirelessly transmits the first and second types of mechanical signal data to the computer system, wherein the computer system includesa non-transitory memory device for storing computer readable program code, anda processor in communication with the memory device, the processor being operative with the computer readable program code to perform steps includingsegmenting a first region of interest from each of the first and second types of mechanical signal data into first and second different portions by using a predetermined percentage of first and second maximum amplitudes of the mechanical signal data, wherein the first region of interest corresponds to a cardiac cycle,determining first and second mechanical signal ratios based on first and second parameters extracted from the first and second portions of the first and second types of mechanical signal data, wherein the first and second mechanical signal ratios characterize waveform changes, wherein the first mechanical signal ratio comprises a time integration ratio of an integral of time domain magnitudes of the first portion to an integral of time domain magnitudes of the second portion,determining a third mechanical signal ratio comprising a frequency energy integration ratio that compares integrals of frequency spectral magnitudes of first and second portions of a second region of interest of the mechanical signal data in a frequency domain,integrating, via an artificial neural network, at least the first, second and third mechanical signal ratios to generate output results for detecting a cardiac pathology, andgenerating a report based at least in part on the output results.
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Abstract
Disclosed herein is a framework for facilitating patient signal analysis. In accordance with one aspect, the framework receives signal data including mechanical signal data, wherein the mechanical signal data is generated in response to mechanical contraction of blood vessels. A region of interest is segmented from the mechanical signal data. One or more mechanical signal ratios may be determined based on parameters extracted from the segmented region of interest to characterize waveform changes. A report may then be generated based at least in part on the one or more mechanical signal ratios.
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Citations
19 Claims
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1. A system for patient signal analysis, comprising:
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a sensor system including sensors that non-invasively acquire at least first and second types of mechanical signal data from a patient, wherein the at least first and second types of mechanical signal data is generated in response to contraction of blood vessels; and a computer system communicatively coupled to the sensor system, wherein the sensor system is configured to continuously and wirelessly transmits the first and second types of mechanical signal data to the computer system, wherein the computer system includes a non-transitory memory device for storing computer readable program code, and a processor in communication with the memory device, the processor being operative with the computer readable program code to perform steps including segmenting a first region of interest from each of the first and second types of mechanical signal data into first and second different portions by using a predetermined percentage of first and second maximum amplitudes of the mechanical signal data, wherein the first region of interest corresponds to a cardiac cycle, determining first and second mechanical signal ratios based on first and second parameters extracted from the first and second portions of the first and second types of mechanical signal data, wherein the first and second mechanical signal ratios characterize waveform changes, wherein the first mechanical signal ratio comprises a time integration ratio of an integral of time domain magnitudes of the first portion to an integral of time domain magnitudes of the second portion, determining a third mechanical signal ratio comprising a frequency energy integration ratio that compares integrals of frequency spectral magnitudes of first and second portions of a second region of interest of the mechanical signal data in a frequency domain, integrating, via an artificial neural network, at least the first, second and third mechanical signal ratios to generate output results for detecting a cardiac pathology, and generating a report based at least in part on the output results. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A method of patient signal analysis, comprising:
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continuously and wirelessly receiving, by a processor device from a sensor system, patient signal data including at least first and second types of mechanical signal data generated in response to contraction of blood vessels; segmenting, by the processor device, a first region of interest from each of the first and second types of mechanical signal data into first and second different portions by using a predetermined percentage of first and second maximum amplitudes of the mechanical signal data, wherein the first region of interest corresponds to a cardiac cycle; determining, by the processor device, first and second mechanical signal ratios based on first and second parameters extracted from the first and second portions of the first and second types of mechanical signal data, wherein the first and second mechanical signal ratios characterize waveform changes, wherein the first mechanical signal ratio comprises a time integration ratio of an integral of time domain magnitudes of the first portion to an integral of time domain magnitudes of the second portion; determining, by the processor device, a third mechanical signal ratio comprising a frequency energy integration ratio that compares integrals of frequency spectral magnitudes of first and second portions of a second region of interest of the mechanical signal data in a frequency domain; integrating, via an artificial neural network, at least the first, second and third mechanical signal ratios to generate output results for detecting a cardiac pathology; and generating, by the processor device, a report based at least in part on the output results. - View Dependent Claims (8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18)
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19. A non-transitory computer readable medium embodying a program of instructions executable by machine to perform steps for patient signal analysis, the steps comprising:
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continuously and wirelessly receiving, by a processor device from a sensor system, patient signal data including at least first and second types of mechanical signal data generated in response to contraction of blood vessels; segmenting, by the processor device, a first region of interest from each of the first and second types of mechanical signal data into first and second different portions by using a predetermined percentage of first and second maximum amplitudes of the mechanical signal data, wherein the first region of interest corresponds to a cardiac cycle; determining, by the processor device, first and second mechanical signal ratios based on first and second parameters extracted from the first and second portions of the first and second types of mechanical signal data, wherein the first and second mechanical signal ratios characterize waveform changes, wherein the first mechanical signal ratio comprises a time integration ratio of an integral of time domain magnitudes of the first portion to an integral of time domain magnitudes of the second portion; determining, by the processor device, a third mechanical signal ratio comprising a frequency energy integration ratio that compares integrals of frequency spectral magnitudes of first and second portions of a second region of interest of the mechanical signal data in a frequency domain; integrating, via an artificial neural network, at least the first, second and third mechanical signal ratios to generate output results for detecting a cardiac pathology; and generating, by the processor device, a report based at least in part on the output results.
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Specification