US5792062A
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Method and apparatus for detecting nonlinearity in an electrocardiographic signal
Assignee: Massachusetts Institute of Technology
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Filing: 1996-05-14
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Granted: 1998-08-11
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Expires: 2016-05-14
Abstract
Abstract A method and apparatus for detecting the presence of a nonlinear characteristic in a data sequence is provided.
Inventors
- 👤 Chi-Sang Poon
- 👤 Mauricio Barahona
- 👤 Christopher K. Merrill
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-11-05 03:39
CPC Classifications
G; G06; G06F; G06F17/00; G06F17/10; G06F17/18; G06F2218/00
Claims Analysis
Total Claims: 32
Claim 1
Independent Claim
203 words
An apparatus for analyzing an electrocardiograph signal to determine whether the electrocardiograph signal includes a nonlinear signal component the apparatus comprising: (a) a modeling system comprising: a modeling processor for receiving the electrocardiograph signal and for representing at least a portion of the electrocardiograph signal with a first linear model having a first set of parameters and a first set of coefficients and for representing at least a portion of the electrocardiograph signal with a second model corresponding to a first nonlinear model having a second set of parameters and a second set of coefficients; and a performance processor for computing a first performance measure of the first model and for computing a second performance measure of the second model; and (b) a performance measurement system coupled to said modeling system said performance measurement system comprising: a comparison processor for receiving the first and second performance measures and for comparing the first performance measure to the second performance measure; and a selection processor for receiving an indication from said comparison processor and for identifying which of the first and second performance measures is a preferred performance measure and for providing an output signal indicating whether the electrocardiograph signal includes a nonlinear signal component.
Claim 2
Independent Claim
203 words
An apparatus for analyzing an electrocardiograph signal to determine whether the electrocardiograph signal includes a nonlinear signal component the apparatus comprising: (a) a modeling system comprising: a modeling processor for receiving the electrocardiograph signal and for representing at least a portion of the electrocardiograph signal with a first linear model having a first set of parameters and a first set of coefficients and for representing at least a portion of the electrocardiograph signal with a second model corresponding to a first nonlinear model having a second set of parameters and a second set of coefficients; and a performance processor for computing a first performance measure of the first model and for computing a second performance measure of the second model; and (b) a performance measurement system coupled to said modeling system said performance measurement system comprising: a comparison processor for receiving the first and second performance measures and for comparing the first performance measure to the second performance measure; and a selection processor for receiving an indication from said comparison processor and for identifying which of the first and second performance measures is a preferred performance measure and for providing an output signal indicating whether the electrocardiograph signal includes a nonlinear signal component.
Claim 3
Dependent Claim
111 words
The apparatus of claim 1 wherein said performance measurement system further comprises: a residual sequence generator having an input port coupled to receive a sequence of values generated from a linear model and having an output port, the residual sequence generator for providing a residual data sequence at the output port thereof; and a residual modeling system having an input port coupled to the output port of said residual sequence generator and having an output port, the residual modeling system for generating a linear model and a nonlinear model of the residual data sequence fed received at the input port thereof and for comparing the nonlinear model to the linear model.
Claim 4
Dependent Claim
32 words
The apparatus of claim 1 wherein said modeling system further comprises at least one sensor having an input port and having an output port coupled to provide data to said modeling processor.
Claim 5
Dependent Claim
27 words
The apparatus of claim 1 wherein said modeling system further comprises a data storage device coupled to said modeling processor and having the electrocardiograph signal stored therein.
Claim 6
Independent Claim
113 words
A method for detecting a nonlinear pattern in a bio-signal, the method comprising the steps of: representing at least a portion of the bio-signal with a linear model having a first set of parameters; representing at least a portion of the bio signal with a nonlinear model, the first nonlinear model having a second set of parameters; computing a first performance measure of the first model; computing a second performance measure of the second model; comparing the first performance measure to the second performance measure; identifying which of the first and second performance measures is a preferred performance measure; and providing an output signal indicating whether the bio-signal includes a nonlinear signal component.
Claim 7
Independent Claim
113 words
A method for detecting a nonlinear pattern in a bio-signal, the method comprising the steps of: representing at least a portion of the bio-signal with a linear model having a first set of parameters; representing at least a portion of the bio signal with a nonlinear model, the first nonlinear model having a second set of parameters; computing a first performance measure of the first model; computing a second performance measure of the second model; comparing the first performance measure to the second performance measure; identifying which of the first and second performance measures is a preferred performance measure; and providing an output signal indicating whether the bio-signal includes a nonlinear signal component.
Claim 8
Dependent Claim
29 words
The method of claim 5 further comprising the steps of: selecting a first one of the first and second models having the preferred performance measure to represent the bio-signal.
Claim 9
Dependent Claim
66 words
The method of claim 6 wherein the comparing step includes the step of: determining a difference value between the first performance measure and the second performance measure; comparing the difference value to a predetermined threshold difference value; in response to the difference value being equal to or greater than the threshold difference value, identifying a respective one of the performance measures as the preferred performance measure.
Claim 10
Dependent Claim
131 words
The method of claim 6 further comprising the steps of: generating a surrogate data set; representing the surrogate data set with a second linear model having a third set of parameters and a third set of coefficients; representing the surrogate data set with a second nonlinear model having a fourth set of parameters and a fourth set of coefficients; comparing a performance measure of the second linear model to a performance measure of the second nonlinear model identifying which of the performance measures is a preferred performance measure; selecting a first one of the second linear model and second nonlinear model having the preferred performance measure; and comparing the first one of the second linear model and second nonlinear model to the first one of the linear model and nonlinear model.
Claim 11
Dependent Claim
35 words
The method of claim 8 wherein the performance measure corresponds to a first one of: an Akiake information criteria; a minimum description length; a log likelihood measure; and a residual sum of the square error.
Claim 12
Dependent Claim
57 words
The method of claim 6 wherein: the first set of parameters and first set of coefficients of the linear model correspond to parameters and coefficients which provide an optimum linear model; and the second set of parameters and second set of coefficients of the nonlinear model correspond to parameters and coefficients which provide an optimum nonlinear model.
Claim 13
Dependent Claim
33 words
The method of claim 10 wherein the steps of representing the bio-signal with a nonlinear model and a nonlinear model includes the step of generating linear and nonlinear models corresponding to Volterra-Wiener models.
Claim 14
Dependent Claim
54 words
The method of claim 11 wherein the step of comparing the performance measure of the linear model to the performance measure of the nonlinear model comprises the step of statistically comparing a first value of the performance measure of the linear model to a first value of the performance measure of the nonlinear model.
Claim 15
Independent Claim
145 words
A method for detecting nonlinearity in a data set generated by a biological system, the method comprising the steps of: representing the data set with a linear model having a first set of parameters and a first set of coefficients; representing the data set with a non-linear model having a first set of parameters and a first set of coefficients; computing a performance measure of the linear model; computing a performance measure of the nonlinear model: comparing the performance measure of the linear model to the performance measure of the nonlinear model; identifying which of the performance measures of the linear model and the nonlinear model is a preferred performance measure; selecting a first one of the linear and nonlinear models having the preferred performance measure:and providing an output signal indicating whether the data set generated by the biological system includes a nonlinear signal component.
Claim 16
Independent Claim
145 words
A method for detecting nonlinearity in a data set generated by a biological system, the method comprising the steps of: representing the data set with a linear model having a first set of parameters and a first set of coefficients; representing the data set with a non-linear model having a first set of parameters and a first set of coefficients; computing a performance measure of the linear model; computing a performance measure of the nonlinear model: comparing the performance measure of the linear model to the performance measure of the nonlinear model; identifying which of the performance measures of the linear model and the nonlinear model is a preferred performance measure; selecting a first one of the linear and nonlinear models having the preferred performance measure:and providing an output signal indicating whether the data set generated by the biological system includes a nonlinear signal component.
Claim 17
Dependent Claim
46 words
The method of claim 13 wherein the step of comparing the performance measure of the linear model to the performance measure of the nonlinear model includes the step of statistically comparing the performance measure of the linear model to the performance measure of the nonlinear model.
Claim 18
Dependent Claim
17 words
The method of claim 14 wherein the linear model and nonlinear model are not the optimum models.
Claim 19
Dependent Claim
25 words
The method of claim 14 wherein: the linear model corresponds to an optimum linear model; and the nonlinear model correspond to an optimum nonlinear model.
Claim 20
Independent Claim
126 words
A method for analyzing a hemodynamic signal to determine the presence of a chaotic signal component within the hemodynamic signal, the method comprising the steps of: dividing the hemodynamic signal into a plurality of time segments, each of the plurality of time segments having a discrete value; representing each of the plurality of time segments of the hemodynamic signal with a first model corresponding to a first linear model; representing each of the plurality of time segments of the hemodynamic signal with a second model corresponding to a first non-linear model; computing a performance measure of the first lineary model, computing a performance measure of the first non-linear model; and comparing the performance measure of the first model to the performance measure of the second model.
Claim 21
Independent Claim
126 words
A method for analyzing a hemodynamic signal to determine the presence of a chaotic signal component within the hemodynamic signal, the method comprising the steps of: dividing the hemodynamic signal into a plurality of time segments, each of the plurality of time segments having a discrete value; representing each of the plurality of time segments of the hemodynamic signal with a first model corresponding to a first linear model; representing each of the plurality of time segments of the hemodynamic signal with a second model corresponding to a first non-linear model; computing a performance measure of the first lineary model, computing a performance measure of the first non-linear model; and comparing the performance measure of the first model to the performance measure of the second model.
Claim 22
Dependent Claim
40 words
The method of claim 17 further comprising the steps of: identifying which of the first and second performance measures is a preferred performance measure; and selecting a first one of the first and second models having the preferred performance measure.
Claim 23
Dependent Claim
26 words
The method of claim 18 wherein: the hemodynamic signal corresponds to an electrocardiograph signal; and the time segments correspond to RR intervals of the electrocardiograph signal.
Claim 24
Dependent Claim
46 words
The method of claim 17 wherein the step of comparing the performance measure of the first model to the performance measure of the second model includes the step of statistically comparing the performance measure of the first model to the performance measure of the second model.
Claim 25
Independent Claim
125 words
A method for analyzing an electrocardiograph signal to detect the presence of a chaotic signal component in the electrocardiograph signal, the method comprising the steps of: generating a linear model to represent a first sequence of data values corresponding to measured RR intervals of the electrocardiograph signal, the linear model having a first predetermined number of terms; and generating a non-linear model to represent the first sequence of data values corresponding to the series of measured RR intervals of the electrocardiograph signal, the non-linear model having a second predetermined number of terms, computing a performance measure of the linear model; computing a performance measure of the nonlinear model; and comparing the performance measure of the linear model to the performance measure of the nonlinear model.
Claim 26
Independent Claim
125 words
A method for analyzing an electrocardiograph signal to detect the presence of a chaotic signal component in the electrocardiograph signal, the method comprising the steps of: generating a linear model to represent a first sequence of data values corresponding to measured RR intervals of the electrocardiograph signal, the linear model having a first predetermined number of terms; and generating a non-linear model to represent the first sequence of data values corresponding to the series of measured RR intervals of the electrocardiograph signal, the non-linear model having a second predetermined number of terms, computing a performance measure of the linear model; computing a performance measure of the nonlinear model; and comparing the performance measure of the linear model to the performance measure of the nonlinear model.
Claim 27
Dependent Claim
176 words
The method of claim 21 further comprising the steps of: identifying one of the performance measure of the linear model and the performance measure of the nonlinear model as a preferred performance measure; in response to the preferred performance measure corresponding to the performance measure of the linear model performing the steps of: generating a predicted sequence of data values using the linear model; subtracting the predicted linear sequence of data values from the first sequence of data values to generate a residual sequence of data values; generating a linear model to represent the residual sequence of data values; generating a nonlinear model to represent the residual sequence of data values; computing a performance measure of the linear model which represents the residual sequence of data values; computing a performance measure of the nonlinear model which represents the residual sequence of data values; and comparing the performance measure of the linear model which represents the residual sequence of data values to the performance measure of the nonlinear model which represents the residual sequence of data values.
Claim 28
Dependent Claim
34 words
The method of claim 22 further comprising the step of identifying one of the performance measures of the linear and nonlinear models which represent the residual sequence of data values having a preferred value.
Claim 29
Dependent Claim
65 words
The method of claim 23 further comprising the steps of: providing a first medical diagnosis in response to the performance measure of the linear model which represents the residual sequence of data values having the preferred value; and providing a second medical diagnosis in response to the performance measure of the nonlinear model which represents the residual sequence of data values having the preferred value.
Claim 30
Independent Claim
277 words
A method for selecting samples from a continuous parameter signal, the method comprising the steps of: (a) dividing the continuous parameter signal into a first plurality of discrete time intervals each of the discrete time intervals including a plurality of discrete data points; (b) selecting a first plurality of discrete data points from predetermined ones of the first plurality of discrete time intervals, each of the first plurality of discrete data points spaced by a first predetermined time interval; (c) representing the continuous parameter signal using the first plurality of discrete data points with a linear model; (d) representing the continuous parameter signal using the first plurality of discrete data points with a nonlinear model; (e) determining how well the linear model represents the continuous parameter signal by computing a performance measure of the linear model; (f) determining how well the nonlinear model represents the continuous parameter signal by computing a performance measure of the non-linear model; (g) comparing the performance measure of the linear model to the performance measure of the non-linear model; and (h) in response to the performance measure of the linear model being preferred over the performance measure of the nonlinear model, then performing the steps of: (1) selecting a next plurality of discrete data points from predetermined ones of the first plurality of discrete time intervals, each of the next plurality of discrete data points spaced by a next predetermined time interval; and (2) repeating steps (c)-(h) until a first one of the following conditions occurs: (i) the performance measure of the non-linear sequence is preferred over the performance measure of the linear sequence; and (ii) a maximum time interval is reached.
Claim 31
Independent Claim
277 words
A method for selecting samples from a continuous parameter signal, the method comprising the steps of: (a) dividing the continuous parameter signal into a first plurality of discrete time intervals each of the discrete time intervals including a plurality of discrete data points; (b) selecting a first plurality of discrete data points from predetermined ones of the first plurality of discrete time intervals, each of the first plurality of discrete data points spaced by a first predetermined time interval; (c) representing the continuous parameter signal using the first plurality of discrete data points with a linear model; (d) representing the continuous parameter signal using the first plurality of discrete data points with a nonlinear model; (e) determining how well the linear model represents the continuous parameter signal by computing a performance measure of the linear model; (f) determining how well the nonlinear model represents the continuous parameter signal by computing a performance measure of the non-linear model; (g) comparing the performance measure of the linear model to the performance measure of the non-linear model; and (h) in response to the performance measure of the linear model being preferred over the performance measure of the nonlinear model, then performing the steps of: (1) selecting a next plurality of discrete data points from predetermined ones of the first plurality of discrete time intervals, each of the next plurality of discrete data points spaced by a next predetermined time interval; and (2) repeating steps (c)-(h) until a first one of the following conditions occurs: (i) the performance measure of the non-linear sequence is preferred over the performance measure of the linear sequence; and (ii) a maximum time interval is reached.
Claim 32
Dependent Claim
52 words
The method of claim 25 further comprising the steps of: selecting a sequence of data points used to represent the continuous parameter signal, each of the data points spaced by the time interval at which the performance measure of the non-linear sequence is preferred over the performance measure of the linear sequence.
References Cited (Backward Citations)
WO1992013273A1
Cited
US5201321A
Cited
US5342401A
Cited
US5421576A
Cited
US5404298A
Cited
US5321409A
Cited
US5439004A
Cited
USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
|---|---|---|---|---|---|
| 3.5 Years (Year 4) | 2001-08-10 | 2002-02-09 | 2002-08-11 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2005-08-10 | 2006-02-09 | 2006-08-11 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2009-08-10 | 2010-02-09 | 2010-08-11 | $7700.00 | Window Closed / Lapsed |