Automated seismic detection of myocardial ischemia and related measurement of cardiac output parameters
First Claim
1. A method of rapid, noninvasive automated detection of myocardial ischemia secondary to coronary artery disease in a patient, comprising:
- connecting a seismic sensor to the patient and detecting a series of SCG waveforms in accordance with the patient'"'"'s heart functioning, converting each said SCG waveform into a digital waveform in the time domain, and generating a list of coefficients for said digital waveform containing the complete information resident in said waveform.
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Accused Products
Abstract
A computer-based instrument to produce a "number" for heart performance parameters and a positive-negative diagnosis of myocardial ischemia. A seismic sensor captures a substantial series of SCG waveforms within a short time frame. Digitized waveforms are created and processed to create signals in the range of 0 to 50 hertz and 0 to 100 hertz. The waveform are processed in the time domain. The 0 to 100 hertz signal is processed to determine the heart rate which is pulse adjusted and interpolated. The SCG waveforms are processed to synchronize the start point of each waveform. The 0 to 50 hertz signal is then processed for signal segmentation to produce waveform signals, each a heart beat or period in length. The segmented signals are then processed to produce linear prediction analysis (LPA) coefficients. The coefficients establish a numerical model-based representation of the waveform. The LPA coefficients in combination contain all of the information resident in the original SCG waveform. For myocardial ischemia analysis, proper LPA coefficients are used in a pattern recognition algorithm to determine a classification of the patent'"'"'s waveforms as either normal or ischemic. The Bayesian decision classifier provides an analytical framework and program for classification of SCG waveforms as represented by the LPA coefficients for myocardial ischemia, or other cardiac disease conditions represented in the SCG waveform, and produces a direct negative or positive output. For various cardiac performance parameters, estimation rather than a classification algorithm is used such as a K-Nearest Neighbor pattern recognition technology, and multiple regression estimators and produces estimation for different parameters.
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Citations
38 Claims
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1. A method of rapid, noninvasive automated detection of myocardial ischemia secondary to coronary artery disease in a patient, comprising:
connecting a seismic sensor to the patient and detecting a series of SCG waveforms in accordance with the patient'"'"'s heart functioning, converting each said SCG waveform into a digital waveform in the time domain, and generating a list of coefficients for said digital waveform containing the complete information resident in said waveform. - View Dependent Claims (2, 3)
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4. A method of rapid, noninvasive automated detection of myocardial ischemia in a patient, comprising:
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connecting a seismic sensor to the patient and detecting a series of SCG waveforms in accordance with the patient'"'"'s heart functioning, converting each said SCG waveform into a digital waveform in the time domain, and generating a list of coefficients for said digital waveform containing the complete information resident in said waveform, processing each said filtered digital waveform by segmenting said digital waveform into a substantial plurality of segments, and generating a set of linear prediction coefficients for each segment to define each waveform with the total information resident in the SCG waveform, comparing said defined waveforms using a Bayesian process with model reference SCG waveforms representative of both non-ischemic conditions and ischemic conditions for the presence of an ischemia condition and creating a direct status output for the patient, and presenting said output. - View Dependent Claims (5, 6, 7)
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8. The method of measurement of a heart performance parameter of a patient comprising connecting a seismic sensor to the patient and generating a series of SCG waveforms in accordance with the patient heart functioning,
converting each said SCG waveform into a digital waveform, filtering said digital waveforms to a frequency of 0-50 Hz., processing each said digital waveform by segmenting said digital waveform into a substantial plurality of segments and generating a set of linear prediction coefficients for each segment to define each waveform with the total information resident in the SCG waveform, and employing a selected subset of these coefficients in a statistical estimation process to estimate cardiac performance parameters.
- 13. The method of detecting a varying and cyclical occurring condition represented by a corresponding continuous complex waveform including a condition complex waveform for each cycle based upon a reference condition complex waveform, comprising generating a selected sequence of condition complex waveforms in said continuous complex waveform, segmenting each said condition complex waveform in said sequence into a substantial number of corresponding segments, developing a set of linear prediction coefficients for each said segment for each said waveform and thereby presenting each said waveform as a series of digital subsignals and presenting a corresponding digitized subsignal containing all of the information of complex waveform, and comparing in a pattern recognition system each said digitized subsignals with reference condition complex waveforms representative of a known condition reference state of said varying and cyclically occurring condition for classifying and varying of said condition with respect to said reference state and cyclically occurring condition.
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18. The method of detecting a varying and cyclically occurring condition represented by a continuous complex waveform of said condition including a plurality of like condition complex waveforms, comprising:
generating a sequence of said condition complex waveforms, segmenting each said continuous condition complex waveform into a substantial number of corresponding and like segments, development a set of linear prediction coefficients for each said segment for each said condition complex waveform whereby each said waveform includes a series of digital subsignals and thereby creating corresponding digitized subsignals containing all of the information of the continuos waveform, establishing a pattern recognition system in which each said digitized subsignal is processed with reference waveforms representative of a known reference condition state of said continuous complex waveform including the varying cyclical occurring condition. - View Dependent Claims (19, 20, 21, 22, 23)
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24. A method of non-invasive detection of the heart of a patient relative to a known class of patients;
- comprising;
generating an SCG waveform corresponding to the patient'"'"'s heart functioning, converting each of said SCG waveforms into a digital waveform, filtering each of said digital waveforms to a first output requency of 0-100 Hz. and to a second output frequency of 0-50 Hz., processing the 0-100 Hz. filtered SCG signal in a unipolar autocorrelation function to determine the repetitive heart rate, synchronizing of the starting point of each SCG waveform for subsequent processing, processing the 0-50 Hz. output frequency signal to segment the signal into a series of segments one heart beat in length and developing the LPA coefficients including the information resident in the original SCG waveforms, and comparing said coefficients with coefficients of the waveform. - View Dependent Claims (25)
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26. A computer based instrument for the noninvasive detection of the heart condition of a patient relative to a known class of patients, said known class of patient including heart waveforms identified by a set of linear prediction coefficients, comprising:
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a signal source configured to sense and generate an SCG waveform corresponding to the patient'"'"'s heart functioning for a selected period of time, a converter connected to said signal source for converting said SCG waveform into a digital signal, a computer connected to said converter, said computer including; an input connected to said converter, a filter program for filtering said digital signal to an output signal of a frequency of 0-50 Hz., a program developing LPA coefficients including the information resident in the original SCG waveform of the patient based on said 0-50 Hz. digital signal, and a program comparing said coefficients of said patient'"'"'s signal waveform with the coefficients of said known class of patients waveform for determining the heart functioning of the patient. - View Dependent Claims (27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38)
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Specification