EP1292900A1 - Method, computer program, and system for automated real-time signal analysis for detection, quantification, and prediction of signal changes - Google Patents
Method, computer program, and system for automated real-time signal analysis for detection, quantification, and prediction of signal changesInfo
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- EP1292900A1 EP1292900A1 EP01923052A EP01923052A EP1292900A1 EP 1292900 A1 EP1292900 A1 EP 1292900A1 EP 01923052 A EP01923052 A EP 01923052A EP 01923052 A EP01923052 A EP 01923052A EP 1292900 A1 EP1292900 A1 EP 1292900A1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
Definitions
- the present invention relates generally to methods and systems for automated signal analysis providing rapid and accurate detection, prediction, or quantification of changes in one or more signal features, characteristics, or properties as they occur. More particularly, the present invention relates to a method or system for automated real-time signal analysis providing characterization of temporally-evolving densities and distributions of signal features of arbitrary-type signals in a moving time window by tracking output of order statistic filters (also known as percentile, quantile, or rank-order filters).
- order statistic filters also known as percentile, quantile, or rank-order filters
- Order statistics are advantageous because they are directly related to the underlying distribution and are robust in the presence of outliers.
- a method of signal analysis that enables the detection of state changes in the brain through automated analysis of recorded signal changes is disclosed in U.S. Patent No. 5,995,868. This method addresses the problem of robustness in the presence of outliers through novel use of order-statistic filtering.
- this method provides for real-time comparison thereof with a reference obtained from past data derived, e.g., from a longer time scale window, referred to as the "background.”
- This approach thereby addresses some of the normalization problems associated with complex, non-stationary signals.
- the prior invention disclosed in U.S. Patent No. 5,995,868 has successfully addressed many of the above-mentioned limitations, including normalization problems associated with complex non-stationary signals, it is lacking in breadth of scope. Detection of changes, for example, is limited to a particular order statistic of the signal. Additionally, the order statistic filter employed to detect signal changes requires large amounts of processing ability, memory, and power when used on digital signals for which sorting procedures are performed at each point in time. Furthermore, the method does not enable full analog implementation.
- the present invention solves the above-described and other problems to provide a distinct advance in the art of automated signal analysis. More specifically, the present invention comprises a method and system for real-time signal analysis providing characterization of temporally-evolving densities and distributions of signal features of arbitrary-type signals in a moving time window by tracking output of order statistic filters (also called percentile, quantile, or rank-order filters).
- order statistic filters also called percentile, quantile, or rank-order filters
- the present invention is operable to analyze input signals of arbitrary type, origin and scale, including, for example, continuous-time or discrete-time, analog or digital, scalar or multi-dimensional, deterministic or stochastic (i.e., containing a random component), stationary (i.e., time invariant) or non-stationary (i.e., time varying), linear or nonlinear.
- the present invention has broad applicability to analysis of many different types of complex signals and sequences of data, including but not limited to biological signals such as those produced by brain, heart, or muscle activity; physical signals such as seismic, oceanographic, or meteorological; financial signals such as prices of various financial instruments; communication signals such as recorded speech or video or network traffic signals; mechanical signals such as jet engine vibration; target tracking and recognition; signals describing population dynamics, ecosystems or bio-systems; signals derived from manufacturing or other queuing systems; chemical signals such as spectroscopic signals; and sequences of data such as word lists, documents, or gene sequences.
- biological signals such as those produced by brain, heart, or muscle activity
- physical signals such as seismic, oceanographic, or meteorological
- financial signals such as prices of various financial instruments
- communication signals such as recorded speech or video or network traffic signals
- mechanical signals such as jet engine vibration
- target tracking and recognition signals describing population dynamics, ecosystems or bio-systems
- signals derived from manufacturing or other queuing systems chemical signals such as spectroscopic signals
- the present invention enables automated detection and quantification " of " changes in the distribution of any set of quantifiable features of a raw input signal as they occur in time.
- the input signal denoted as ⁇ x(t) ⁇
- the input signal may be optionally preprocessed in order to produce a new signal, the feature signal, denoted as ⁇ X(t) ⁇ quantifies a set of features of the input signal that the system will use in detecting and quantifying changes.
- X(t) is called the signal feature vector at time ..
- the feature vector has as many components as there are signal features.
- the desirability of preprocessing will depend upon the nature of the raw input signal and the nature of the features of interest.
- the present invention also introduces a useful new object called the time- weighted feature density of a signal, ⁇ f(t,X) ⁇ , which can be computed from the feature signal at each point in time.
- This object allows access to estimates of the full time- dependent density and cumulative distribution function of varying signal features with any desired degree of accuracy, but confines these estimates to any desired time- scale through the use of time-weighting (time localization of feature density).
- This time-weighted feature density describes the raw input signal features measured in moving windows of time specified by the time-weight function, which allows a user to apply different significances to portions of available information (e.g., to consider recent information as more relevant than older information; or to weight information according to its reliability, etc.).
- the present invention allows for rapidly obtaining these estimates in a computationally efficient manner that can be implemented in digital or analog form, and a method for detecting, quantifying, and comparing changes of arbitrary type in the density/distribution of the feature vector as it changes.
- the significance of this increase in computational efficiency, along with analog implementability, becomes especially clear when considering medical device applications where, for example, the present invention enables currently used externally-worn devices that require daily battery recharging to become fully implantable devices with an operational lifetime of several years, thereby improving safety and convenience.
- a raw time-varying input signal of arbitrary type, origin, and scale is received for analysis.
- pre-processing occurs to produce a feature signal more amenable to further analysis.
- time-weighted density or distribution functions are determined for both a foreground or current time window portion of the signal and a background portion of the signal or reference signal (which also may be evolving with time, but potentially on a different timescale) in order to emphasize, as desired, certain data.
- Percentile values for both foreground and background signals are then accurately estimated and compared so as to detect and quantify feature changes on any timescale and to any desired degree of precision as the raw input signal evolves in time. Density and distribution approximations may also be compared.
- the state of the existing art requires that the data be laboriously sorted in order to determine these percentile values.
- percentile values are accurately estimated without sorting or stacking, thereby increasing processing speed and efficiency while reducing computation, memory, and power needs.
- the present invention is able to perform in a highly computationally efficient manner that can be implemented in a low power consumption apparatus consisting of an analog system, a digital processor, or a hybrid combination thereof, thereby providing tremendous system power savings.
- the present invention is also operable to facilitate real-time signal normalization with respect to the density/distribution approximations, which is useful in processing and analysis of series of different orders. This is particularly useful where the features or characteristics of interest are invariant to a monotonic transformation of the signal's amplitude.
- the present invention 's ability to rapidly and accurately detect changes in certain features of the input signal can enable prediction in cases where the changes it detects are associated with an increased likelihood of future signal changes.
- the method when applied to seismic signals, the method can enable prediction of an earthquake or volcanic eruption; when applied to meteorological signals, the method can enable prediction of severe weather; when applied to financial data, the method can enable prediction of an impending price change in a stock; when applied to brain waves or heart signals, the method can enable prediction of an epileptic seizure or ventricular fibrillation; and when applied to brain wave or electromyographic signals, it can enable prediction of movement of a body part.
- FIG. 1 is a block diagram illustrating a first portion of steps involved in performing a preferred embodiment of the present invention
- FIG. 2 is a block diagram illustrating a second portion of steps involved in performing a preferred embodiment of the present invention
- FIG. 3 is a block diagram illustrating a third portion of steps involved in performing a preferred embodiment of the present invention.
- FIG. 4 is a graph of an exemplary raw input signal, x(t), as might be received for analysis by a preferred embodiment of the present invention
- FIG. 5 is a graph of a feature signal, X(t), resulting from preprocessing the raw input signal shown in FIG. 4;
- FIG. 6 is a graph showing the calculated 0.25, 0.50, and 0.75 percentiles of the feature signal shown in FIG. 5;
- FIG. 7 is a graph showing the true feature density, f(t,w) of the feature signal shown in FIG. 5 calculated at times and t 2 ;
- FIG. 8 is a graph showing the true feature distribution, F(t,w), of the feature signal shown in FIG. 5 calculated at times -y and t ⁇ ;
- FIG. 9 shows a graph of an evolving first approximation of the time-weighted feature density of the feature signal shown in FIG. 5, calculated at times ti and t 2
- FIG. 10 shows a graph of an evolving first approximation of the time-weighted distribution function of the feature signal shown in FIG. 5, calculated at times and f ⁇
- FIG. 11 is a graph showing calculated percentile tracking filter outputs for 0.25, 0.50 , and 0.75 percentiles of the feature signal shown in FIG. 5;
- FIG. 12 shows a graph of an evolving second approximation of the time- weighted feature density of the feature signal shown in FIG. 5, calculated at times and t 2 ;
- FIG. 13 shows a graph of an evolving second approximation of the time- weighted distribution function of the feature signal shown in FIG. 5, calculated at times ti and t 2
- FIG. 14 shows a graph of a A(t) measured from the feature signal shown in FIG. 5;
- FIG. 15 is a block diagram of a preferred embodiment of an analog implementation of a percentile tracking filter component of the present invention.
- FIG. 16 is a detailed circuit schematic of the percentile tracking filter component shown in FIG. 15;
- FIG. 17A shows an exemplary feature signal that for analysis by the present invention
- FIG. 17B shows an output of the detailed circuit schematic shown in FIG. 16 and a true median output associated with the feature signal of FIG. 17A;
- FIG. 18 is a block diagram of a preferred embodiment of an analog implementation of a Lambda estimator component of the present invention.
- FIG. 19 is a detailed circuit schematic of the Lambda estimator component shown in FIG. 18.
- the present invention comprises a method and system for real-time signal analysis providing characterization of temporally-evolving densities and distributions of signal features of arbitrary-type signals in a moving time window by tracking output of order statistic (e.g., percentile, quantile, rank-order) filters.
- order statistic e.g., percentile, quantile, rank-order
- the present invention enables automated quantification and detection of changes in the distribution of any set of quantifiable features of that signal as they occur in time.
- the present invention's ability to rapidly and accurately detect changes in certain features of an input signal can also enable prediction in cases when the detected changes are associated with an increased likelihood of future signal changes.
- Step 1 Receive Raw Input Signal
- the raw input signal is received from a system under study 22.
- This signal denoted as ⁇ -(-) ⁇
- the raw input signals may be of arbitrary type, including, for example, continuous-time or discrete- time, analog or digital, scalar or multi-dimensional, deterministic or stochastic (i.e., containing a random component), stationary (i.e., time invariant) or non-stationary (i.e., time varying), and linear or nonlinear.
- the raw input signals may also be of arbitrary origin, including, for example, biological signals such as those produced by brain, heart, or muscle activity; financial signals such as prices of various financial instruments; physical signals such as seismic, oceanographic, and meteorological; communication signals such as recorded speech or video or network traffic signals; mechanical signals such as jet engine vibration; chemical signals such as those obtained in spectroscopy; and sequences of data such as word lists or gene sequences.
- biological signals such as those produced by brain, heart, or muscle activity
- financial signals such as prices of various financial instruments
- physical signals such as seismic, oceanographic, and meteorological
- communication signals such as recorded speech or video or network traffic signals
- mechanical signals such as jet engine vibration
- chemical signals such as those obtained in spectroscopy
- sequences of data such as word lists or gene sequences.
- Step 2 (Optional) Preprocess Raw Input Signal to Derive Feature Signal
- the raw input signal may be optionally pre-processed, as shown in box 24, in order to produce a new signal, the feature signal, denoted as ⁇ X(t) ⁇ .
- the feature signal denoted as ⁇ X(t) ⁇ .
- Common examples include derivatives of any order; integrals or any order; various moments and related properties such as variance, skewness, kurtosis; wavespeed and related measures such as inter-zero- crossing intervals and inter-peak intervals; signal power in a time window and/or in a particular frequency band; measures derived from Fourier analysis such as those involving signal phase or power spectral density; measures from nonlinear dynamics such as correlation dimension, fractal dimension, magnitude of Lyapunov exponents; phase delay embeddings; and measures of rhythmicity, wave shape, or amplitude.
- the feature signal quantifies a set of features of the input signal that the system will use in detecting changes.
- X(t) is called the signal feature vector at time t.
- the desirability of preprocessing will depend upon the nature of the raw input signal and the nature of the features of interest.
- Step 3 Determine Time-Weighted Distribution and Density Functions of the Feature Signal
- a time-weighted feature density (TWFD) of a signal, ⁇ f(t,X) ⁇ , is computed from the raw input signal or feature signal at each point in time, as shown in box 26.
- the TWFD allows access to estimates of the full time-dependent density and cumulative distribution functions of varying signal features with any desired degree of accuracy, but confines these estimates to any desired time-scale through the use of time- weighting (time localization of feature density). Time-weighting allows the user to apply different significance to portions of available information (e.g., to consider more recent information as more relevant than older information; or to weigh information according to its reliability, etc.).
- the TWFD describes the raw input signal features measured in moving windows of time specified by a time-weight function, thereby allowing for detection, quantification, and comparison of changes of arbitrary type in the density/distribution of the feature vector as it evolves.
- the concept of instantaneous feature density can then be extended to a time- weighted window.
- the TWFD of the feature signal x(t) in a time window w(t,s) is defined as
- f(t,D) jw ⁇ t,s) ⁇ [D -X ⁇ S ) ls , (2)
- ⁇ (x) is the Dirac ⁇ - function
- D is the amplitude
- w(t,s) often called a "time-weight”
- w(t,s) 0 for all t,s.
- w(t,s) may attain its dependence upon t or s through an explicit dependence upon other signals (e.g., X(t), in which case it may be referred to as a "state-weight").
- Eq. (2) becomes a convolution integral, and the time- weighting function is interpreted as a moving window. If J is a characteristic time, or duration of w, the dependence may be expressed as w(t,s; T), and a shorthand notation used for the integral
- Eq. (4) defines a time average on a time scale T .
- the independent variable, s is used as the domain of the function
- the independent variable, t represents the current time, and is used to parameterize the choice of w, enabling the time window to change or move as time changes or evolves.
- This dependence on t also allows the user to change the shape of time-weighting as t changes.
- the weight function may also depend upon other information besides time (e.g., the raw signal, the feature signal, signals derived from the feature signal (such as it's time derivatives), other "auxiliary" signals, S(t), or control signals, U(t)).
- This generalization allows for state-weighting as mentioned above, or to, e.g., include other "outside” information into the analysis, emphasizing feature signal information accordingly.
- the notation of Eq. (4) may be used for both continuous and discrete time averages.
- the time window may be an exponential moving window with time constant T, such that
- TWCDF time-weighted cumulative distribution function
- percentile values are the very building blocks of probability distributions and enable a robust statistical description thereof. In many applications they produce significantly better information than other more commonly utilized statistics such as the mean and standard deviation. The set of all percentile values completely describes the distribution from which they are derived.
- X p (t) is the p th percentile of the time-varying cumulative distribution function, F(t,X;w), generated by the variations of the feature signal, X(t), in the temporal window defined by w.
- F(t,X;w) the time-varying cumulative distribution function
- One method of estimating the TWCDF is accomplished by partitioning the state space (containing the range of the feature signal) and computing time-weighted histograms that keep track of how often each bin is visited by the feature signal.
- This non-parametric approach to obtaining F(t,x;w) has the advantage (over the parametric approach described later herein) of allowing greater flexibility in approximating the TWCDF with any level of precision desired (although as precision improves, complexity of the implementation increases).
- the feature density for discrete (digitally sampled) data can be computed in finite differences as follows (suppressing dependence on w):
- ⁇ [a (19) can be utilized to compute the feature density.
- w if D J ⁇ X ⁇ t_) ⁇ D J+l , D J ⁇ X(t M )D J+l
- Another method of estimating the TWCDF is accomplished by approximating F(t,x; w) by a model distribution function, F(t, ⁇ ;w,v(t)) , that may depend upon a vector of parameters, v(t).
- the true feature density is typically unavailable for online analysis but may be well-approximated, assuming the distribution is well-modeled by a parametric distribution, through estimation of the parameters upon which the distribution depends. Often the moments of the distribution may be computed and used as input to the model to determine the approximation.
- a Gaussian approximation can be achieved using the first two moments (the mean, ⁇ , and the variance, ⁇ ) of the feature signal. More precisely, the model density
- the uniform distribution makes the implementation stable, unlike the Gaussian approximation which requires signal limiting (clipping) to prevent output from becoming unbounded due to the exponential function.
- the use of a triangular distribution model for data that is strictly positive simplifies the implementation (assuming the left end and vertex of the density are at x 0, the distribution is completely specified by the right endpoint, i.e., the maximal x value).
- the triangular distribution approximation can also be used to estimate the distribution of the signal from a single percentile calculation.
- similar parametric modeling and estimation may be performed using any other distribution model and parameter estimation scheme meant to approximate the underlying true density/distribution.
- Step 4 Compute the Percentile Tracking Filter (PTF)
- X p ⁇ is an output of a rank-order (also order statistic, quantile, or percentile) filter.
- X (t) is the output of a median filter, producing at each moment in time the median of the w-weighted distribution of feature signal information in the most recent window.
- Numerical rank-order filtering is a computationally expensive operation. First, it requires knowing, at any given time, the values of N latest data points, where N is the length of the moving window, and the numerical and chronological order of these data points. This memory requirement is a major obstacle to implementing an analog order statistic filter. Another computational burden on different (numerical) rank-order filtering algorithms results from the necessity to update the numerically ordered/sorted list, i.e., to conduct a search. Overall performance of a rank-order filter is a trade-off between performance, speed, and memory requirements.
- time-weight function is a rectangular moving window of length 7
- the braced expression is merely the total number of upward crossings of the threshold, X (t) , by the signal minus the total number of downward crossings, in agreement with Eq. (29).
- time-weight function is an exponential moving window of length T
- ⁇ enters the equation forX p (t) explicitly rather than through the initial condition only, as in a general case of Eq. (27).
- the exponential window is causal, and has an advantageously low computational cost (including low memory requirements) and easy analog implementation. For these reasons this choice of weight function is preferred.
- F(t, ⁇ ; w) for the TWCDF (and TWFD) by standard interpolation or extrapolation means (e.g., linear interpolation and extrapolation to enable evaluation of the distribution function approximation at values between ordered pairs
- the PTF output (and the TWCDF estimates) make accessible important information about the level of quantified features present in the specialized time- window defined by the time-weight function. Prior to this invention, none of this information could be obtained in a completely analog system, and the computational cost of deriving this information in digital implementations was significantly more expensive and less efficient.
- Step 5 (Optional) Normalize the Signal
- the present invention facilitates an optional normalization technique which may be performed at this point, as shown in box 30.
- the desirability of performing this optional step will depend upon the nature of the signals of interest, particularly their respective scales.
- This new signal may be referred to as the normalization of signal z with respect to the distribution F.
- the signal, y(t) is deemed "normalized” because, no matter what the values/range taken by the input signal, the resulting signal values are always in the interval [0,1].
- Step 6 Compare Foreground and Background or Reference Distributions/Densities in Order to Detect and Quantify Changes in Signal Features
- the foreground signal and background or reference signal may now be compared in order to detect or quantify feature changes in the foreground signal. That is, the ability to extract information from (or restrict the influence of information to) different time scales through weighting functions, and the ability to precisely control the set of features under study, allows further use of feature density analysis method as a component of a system for detection and quantification of feature changes. Referring also to FIG. 3, such detection and quantification is accomplished by comparing the PTF outputs (or entire time- weighted feature densities) in the moving foreground window with that of a background or established reference from which a specified change is to be detected.
- TWCDF approximates and associated PTF signals for various percentiles. These are determined in part by the time-weight function used in their definition and computation, which describes the way information is weighted and utilized in the production of these approximations. These approximations can be further analyzed and utilized to produce new and highly valuable means for detection and quantification of changes in the feature signal, and therefore in the underlying system that provided the raw signal input.
- the concept is one of comparison between results obtained for different time-weighting of the information provided by the percentile tracking filter outputs or approximations of the TWFD or corresponding TWCDF of the feature signal.
- the user may specify two (or more) time-weight functions, w-, and w 2 , with different characteristic timescales, Ti and T 2 , respectively, and thereby obtain two sets of PTF outputs and TWCDF approximations:
- Tt is preferably chosen to be much larger than T 2 so that a comparison may be performed of the above quantities, interpreting the former as representing the background or reference information (i.e., obtained from a large time window of past or historical information) and the latter set as being representative of the foreground or more current, test information (i.e., obtained from a small time window of recent information).
- the existence of a reference for the comparison allows a built-in type of normalization that ensures comparison of "apples to apples" in the resulting analysis.
- the reference information need not necessarily be continually updated (the time between updates could, e.g., be proportional to the timescale analyzed), or a constant set of information (e.g., a constant, C, and fixed distribution, F ref (t,x)) may be used as a reference for the comparison with X p (t;w 2 ) and F 2 (t,x;w 2 ) or F 2 (t,x;w 2 ) respectively.
- a constant set of information e.g., a constant, C, and fixed distribution, F ref (t,x)
- a time-varying signal referred to as a ⁇ -estimator, is defined to quantify the difference between distributions by functioning as a type of distance measure between them as
- A(t;W,G) A(F l (t,x;w),F 2 (t,x;w)) G(F l (t,y;w) -F 2 (t,y;w)) dF x (y)
- the ⁇ -estimator through choice of weighting functions, is able to quantify a wide array of differences in the two distributions being compared.
- ⁇ q. (35) reduces to
- Step 7 (Optional) Predict Future Changes
- the present invention's ability to rapidly and accurately detect changes in certain features of an input signal can be used to predict future changes in cases when the detected changes are associated with an increased likelihood of these future changes.
- the method when applied to seismic signals, the method can enable prediction of an earthquake or volcanic eruption; when applied to meteorological signals, the method can enable prediction of severe weather; when applied to financial data, the method can enable prediction of an impending price change in a stock; when applied to brain waves or heart signals, the method can enable prediction of an epileptic seizure or ventricular fibrillation; and when applied to brain wave or electromyographic signals, the method can enable prediction of movement of a body part.
- a three-dimensional raw input signal, x(t) is received such that, as shown in FIG. 4, x(t) is the price at time t of three stocks (IBM, GM, and MRK) during a 360 minute period on October 23, 1994.
- time-weight function w(t,s)
- auxiliary signal such as oil prices and supplies which may have a direct impact in GM stock prices by influencing consumer preferences and cash availability.
- Another time-weight for example is the average temperature across the U.S. for the past week (thus incorporating information that could have an effect on the feature signal if, e.g., a freeze altered the price of wheat futures or heat wave altered the price of California energy companies) that could directly or indirectly affect these or other stocks.
- the true 0.25, 0.50 , and 0.75 percentiles are shown in FIG. 6 as, respectively, Xo. ⁇ sft), Xo.5o(t), and Xo.7s(t).
- the true feature density, f(t,w), and distribution, F(t,w) are calculated from the data at two different times, and - 2 , as shown in FIGs. 7 and 8. The parameters are calculated for a time-scale of 100 minutes.
- the feature signal and time-weight are used to compute/update an evolving approximation to the time-weighted density and corresponding distribution function of the feature signal: /(t,x;w) andE(t,x;w) .
- f(t,x;w ⁇ ) wa.dF(t,x;-w ⁇ ) are evaluated assuming a Gaussian (normal) density approximation (bell-shaped) for the data over the past 100 minutes. These approximations can be compared to the true distributions shown in the previous figure. Again, these were evaluated at two different times, and t 2 , as shown in FIGs. 9 and 10.
- the feature signal a specified set of one or more percentile values, p, and approximations /(t,x;w) andE(t,x;w)are then used to compute/update the PTF output, X p (t;w) .
- the PTF output, X p (t;w) is shown in
- FIG. 11 An interpolation/extrapolation scheme is then used to compute/update a second set of (evolving) approximations to the time-weighted density and corresponding distribution function of the feature signal, f(t,x;w) andE(t,x; w) .
- f(t,x;w x ) aadF(t,x;w ) were determined using the outputs of the PTF by linear interpolation. These were again evaluated at the times, and t 2 , as shown in FIGs. 12 and 13.
- the PTF output and approximations to the time-weighted density/distribution of the feature signal are analyzed to detect, quantify, or predict changes in the system that produced the raw signal.
- This analysis may consist, for example, of establishing or computing a reference against which to compare the information being generated.
- One preferred approach is to use a fixed reference value and a fixed density/distribution and compare them to the PTF and the density/distribution approximations, respectively.
- a second preferred approach involves performing the prior method steps simultaneously with two differing choices of time-weight function, one to establish a reference PTF and density/distribution approximation and the other to generate a test PTF and density/distribution approximation, then comparing the two resulting sets of information.
- Described above is a method for comparing two PTF outputs (e.g., computing their ratio) and for comparing test-to-reference distributions ( ⁇ -estimators).
- the ratio and/or ⁇ - estimators are used to compare the feature content in one time-window/scale to another. Changes may be detected, e.g., by applying thresholds to either ratio and/or ⁇ -estimators.
- the above calculations were performed on short time-scale of 100 min.
- the output may be utilized.
- signal normalization of any signal
- Another use involves prediction of future changes if it happens that the detected changes are associated with an increased likelihood of certain future signal changes.
- the portfolio value on the particular day did not demonstrate any significant change over the background distribution, so the owner may have simply decided to maintain his holdings at that time.
- FIGs. 15 and 17 show block diagrams of a preferred analog system
- FIGs. 16 and 18 show detailed circuit schematics of a preferred embodiment of the analog system 100 shown described generally in FIGs 15 and 17.
- the analog system 100 comprises two major components, an analog PTF circuit 102 and an analog Lambda circuit 104.
- This implementation is based upon a recognition that the use of approximations in determining feature density facilitate tracking of the percentile in real-time with minimal errors.
- the use of a uniform density distribution makes the implementation simpler by an order of magnitude over implementations involving distributions with exponentiation, while providing and output that closely tracks the actual median obtained by sorting.
- the implementation outputs the percentile value of the input signal in real time while retaining the controllability and flexibility of a digital algorithm.
- the analog PTF circuit 102 broadly includes a peak detector stage 108; a comparator stage 1 10; a scaling and shifting amplifier stage 112; an adder stage 1 14; a multiplier and divider stage 116; and an integrator stage 118.
- the comparator stage 110 performs the step of computing: ⁇ [x(s)
- the output of the comparator stage 110 is a voltage equal to a saturation voltage (+15 V) of an amplifier component of the comparator stage 110.
- the scaling and shifting amplifier stage 112 brings the voltage to 0-10 V. This enables direct subtraction from the input 10 * p in the adder 114.
- the adder stage 114 subtracts the output of the scaling and shifting amplifier stage 112 from 10 * p. Thus the output of the adder stage 114 is
- the multiplier and divider stage 116 multiplies the output of the adder stage 114 and divides the result by 10.
- the output of this stage 116 is i(p - ⁇ [x(s)-x p (s)]j, which is ⁇ x p (t) .
- the integrator stage 118 has an input-output relationship defined by the
- V 0 , where V[ and V 0 ate the input and the output respectively.
- V[ and V 0 ate the input and the output respectively.
- the output of this stage 118 is — X p (t) .
- the peak detector stage 108 has a natural exponential forgetting factor due to discharging of the capacitor C p .
- the time- window of exponential forgetting for the peak can be controlled by varying the resistor R p .
- the time-window over which the percentile is calculated, is controlled by the integrator stage 118.
- Resistor Rj and a capacitor C,- of the integrator stage 110 control the time factor T.
- the output of the PTF circuit 102 with these parameters to an example feature signal input is shown in FIG. 17A. Also shown in FIG. 17B is the true median obtained by performing a heap sort of sliding 2.2-second window on the input data. Note that the PTF circuit 102 responds to changes in the signal faster than the true median. This is because the true N-sample median does not respond until N/2 (or [N+1]/2 if N is odd) samples have passed through the filter.
- the analog Lambda circuit 104 broadly comprises a bank
- the bank 122 of PTF circuits 102 comprises one or more PTF circuits 102, each applied on the original signal, x(t) or feature signal X(t). The output of this bank
- the bank 124 of reference signals can be PTF circuits 102 applied to the original raw input or to the feature signal.
- the reference signals are typically generated by using large time-scales during the integration step of the PTF operation. Otherwise, the reference signals can be simple constant voltage sources.
- the adder stage 126 adds the output of the bank 122 of PTF circuits 102 to the negative output of the bank 124 of reference signals. This stage 126 can also selectively amplify or attenuate the result of each subtraction.
- the output of the adder stage 126 is a summation of the weighted difference between the PTF circuit 102 output signal and the reference signal, which is the Lambda parameter:
- ⁇ (t) ⁇ w(t, x)(F fg (t, x) - F bg (t, x)) .
- the thresholding stage 128 detects increases in the Lambda parameter beyond a particular threshold.
- This stage 128 may be implemented as a comparator.
- the alarm stage 130 communicates threshold crossing detected by the thresholding stage 128, and can be implemented as in many different ways, from a simple light (or beep) to a complicated circuit that can perform a sequence of steps, such as a computer that can perform some pre-determined operation.
- an analog Lambda circuit is 104 shown operable to compute the ⁇ parameters from the percentiles of the input signal and constant reference signals.
- the inputs to the analog Lambda circuit 104 are the outputs PTF1 , PTF2, and PTF3 provided by parallel PTF circuits 102.
- PTF1 , PTF2, and PTF3 correspond to the p-i, p and p 3 percentile of the input signal such that p ⁇ > p 2 > p 3 .
- the reference signals in the circuit 104 are Vr-t, Vr 2 , and Vr 3 such that Vr-i>Vr 2 >Vr 3 .
- the Lambda circuit 104 comprises first and second stages 134,136, wherein the first stage 134 is operable to provide the calculated ⁇ parameter, and the second stage 136 is operable to perform threshold detection and alarm. The following equations hold for the Lambda circuit 104 shown:
- the Lambda circuit 104 can be tuned to output a particular parameter by changing the values of resistances R 5 -R 9 . For example, if the circuit 104 is to respond to changes in the pi percentile (say the 75 th percentile), then the gain of that particular input line can be increased while the other two input lines can be attenuated. In the second stage 136, this voltage can be compared to a specific threshold and an alarm can be triggered.
- the present invention provides computationally efficient characterization of temporally-evolving densities and distributions of signal features of arbitrary-type signals in a moving time window by tracking output of order statistic (e.g., percentile, quantile, rank-order) filters. As noted, the present invention's ability to rapidly and accurately detect changes in certain features of an input signal can also enable prediction in cases when the detected changes are associated with an increased likelihood of certain of future signal changes.
- order statistic e.g., percentile, quantile, rank-order
- any sequence of data may be used as a signal in the present invention.
- t is interpreted simply as an index that determines the order in which the data appears in the list, ⁇ x(t) ⁇ , or its structure. This enhances the understanding of how the invention may be utilized in the analysis of lists or sequences.
Abstract
Description
Claims
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US19413000P | 2000-04-03 | 2000-04-03 | |
US194130P | 2000-04-03 | ||
PCT/US2001/010677 WO2001075660A1 (en) | 2000-04-03 | 2001-04-03 | Method, computer program, and system for automated real-time signal analysis for detection, quantification, and prediction of signal changes |
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Families Citing this family (15)
Publication number | Priority date | Publication date | Assignee | Title |
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US7133568B2 (en) | 2000-08-04 | 2006-11-07 | Nikitin Alexei V | Method and apparatus for analysis of variables |
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US8588933B2 (en) | 2009-01-09 | 2013-11-19 | Cyberonics, Inc. | Medical lead termination sleeve for implantable medical devices |
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US9643019B2 (en) | 2010-02-12 | 2017-05-09 | Cyberonics, Inc. | Neurological monitoring and alerts |
Family Cites Families (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPS61243505A (en) * | 1985-04-19 | 1986-10-29 | Omron Tateisi Electronics Co | Discrete time controller |
US4663703A (en) * | 1985-10-02 | 1987-05-05 | Westinghouse Electric Corp. | Predictive model reference adaptive controller |
US4998051A (en) * | 1989-03-23 | 1991-03-05 | Matsushita Electric Industrial Co., Ltd. | Adaptive control system |
US5347446A (en) * | 1991-02-08 | 1994-09-13 | Kabushiki Kaisha Toshiba | Model predictive control apparatus |
FR2700632B1 (en) * | 1993-01-21 | 1995-03-24 | France Telecom | Predictive coding-decoding system for a digital speech signal by adaptive transform with nested codes. |
US5519605A (en) * | 1994-10-24 | 1996-05-21 | Olin Corporation | Model predictive control apparatus and method |
JP3903588B2 (en) * | 1997-07-31 | 2007-04-11 | ソニー株式会社 | Signal change detection circuit |
-
2001
- 2001-04-03 AU AU2001249785A patent/AU2001249785A1/en not_active Abandoned
- 2001-04-03 EP EP01923052A patent/EP1292900A4/en not_active Ceased
- 2001-04-03 WO PCT/US2001/010677 patent/WO2001075660A1/en active Application Filing
Non-Patent Citations (3)
Title |
---|
NIKITIN A V ET AL: "Analog implementation of seizure detection algorithm", BMES/EMBS CONFERENCE, 1999. PROCEEDINGS OF THE FIRST JOINT ATLANTA, GA, USA 13-16 OCT. 1999, PISCATAWAY, NJ, USA,IEEE, US, vol. 2, 13 October 1999 (1999-10-13), page 860, XP010357838, DOI: 10.1109/IEMBS.1999.804015 ISBN: 978-0-7803-5674-0 * |
OSORIO I ET AL: "REAL-TIME AUTOMATED DETECTION AND QUANTITATIVE ANALYSIS OF SEIZURES AND SHORT-TERM PREDICTION OF CLINICAL ONSET", EPILEPSIA, RAVEN PRESS LTD, NEW YORK, US, vol. 39, no. 6, 1 June 1998 (1998-06-01), pages 615-627, XP008012666, ISSN: 0013-9580, DOI: 10.1111/J.1528-1157.1998.TB01430.X * |
See also references of WO0175660A1 * |
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WO2001075660A1 (en) | 2001-10-11 |
AU2001249785A1 (en) | 2001-10-15 |
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