WO2020148163A1 - Apprentissage de dynamique temporelle pour extraction de caractéristiques et compression de formes d'onde physiologiques - Google Patents

Apprentissage de dynamique temporelle pour extraction de caractéristiques et compression de formes d'onde physiologiques Download PDF

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Publication number
WO2020148163A1
WO2020148163A1 PCT/EP2020/050473 EP2020050473W WO2020148163A1 WO 2020148163 A1 WO2020148163 A1 WO 2020148163A1 EP 2020050473 W EP2020050473 W EP 2020050473W WO 2020148163 A1 WO2020148163 A1 WO 2020148163A1
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Prior art keywords
filters
instructions
optimizing
input signals
associated weights
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PCT/EP2020/050473
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English (en)
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Bryan CONROY
Asif Rahman
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Koninklijke Philips N.V.
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Publication of WO2020148163A1 publication Critical patent/WO2020148163A1/fr

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    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
    • H03M7/00—Conversion of a code where information is represented by a given sequence or number of digits to a code where the same, similar or subset of information is represented by a different sequence or number of digits
    • H03M7/30—Compression; Expansion; Suppression of unnecessary data, e.g. redundancy reduction
    • H03M7/3059—Digital compression and data reduction techniques where the original information is represented by a subset or similar information, e.g. lossy compression
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A61B5/346—Analysis of electrocardiograms
    • A61B5/347—Detecting the frequency distribution of signals
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
    • H03M7/00—Conversion of a code where information is represented by a given sequence or number of digits to a code where the same, similar or subset of information is represented by a different sequence or number of digits
    • H03M7/30—Compression; Expansion; Suppression of unnecessary data, e.g. redundancy reduction
    • H03M7/3068—Precoding preceding compression, e.g. Burrows-Wheeler transformation
    • H03M7/3071—Prediction
    • H03M7/3073—Time
    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
    • H03M7/00—Conversion of a code where information is represented by a given sequence or number of digits to a code where the same, similar or subset of information is represented by a different sequence or number of digits
    • H03M7/30—Compression; Expansion; Suppression of unnecessary data, e.g. redundancy reduction
    • H03M7/3068—Precoding preceding compression, e.g. Burrows-Wheeler transformation
    • H03M7/3079—Context modeling
    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
    • H03M7/00—Conversion of a code where information is represented by a given sequence or number of digits to a code where the same, similar or subset of information is represented by a different sequence or number of digits
    • H03M7/30—Compression; Expansion; Suppression of unnecessary data, e.g. redundancy reduction
    • H03M7/55—Compression Theory, e.g. compression of random number, repeated compression

Definitions

  • Various exemplary embodiments disclosed herein relate generally to a system and method for learning of temporal dynamics for feature extraction and compression of physiological waveforms.
  • Continuous-time physiological signals such as the electrocardiogram (EKG), photoplethysmogram (PPG), and arterial blood pressure (ABP) signals, may be very useful in characterizing patient condition, particularly as part of a larger clinical decision support algorithm.
  • EKG electrocardiogram
  • PPG photoplethysmogram
  • ABSP arterial blood pressure
  • Various embodiments relate to a method for determining the parameters of a sparse coding method wherein the parameters include a set of filters, the method including: defining an objective function that produces the set of filters and a set of associated weights based upon a set of input signals, a set of white-noise processes, and a regularization parameter; determining the set of associated weights and set of filters that produce an optimal solution of the objective function by iterating the following steps until convergence: optimizing the set of associated weights while holding the set of filters fixed based upon the set of input signals; and optimizing the set of filters while holding set of associated weights fixed based upon the set of input signals, wherein the input signal is reconstructed based upon the set of associated weights, the set of filters, and the set of white noise processes.
  • the set of filters are an inverse fast Fourier transform (IFFT) of the power spectral density of a stationary process.
  • IFFT inverse fast Fourier transform
  • optimizing the set of associated weights while holding the set of filters fixed based upon the set of input signals includes solving a set of decoupled sparse regression problems using a convex optimization algorithm.
  • optimizing the set of filters while holding set of associated weights fixed based upon the set of input signals includes using a cyclical block-coordinate decent algorithm.
  • Various embodiments are described, further including computing a specific set of weights for a specific input by solving a sparse regression problem based upon the specific input, the optimized set of filters, and the set of white noise processes.
  • optimizing the set of associated weights a, a 2 ,...,a N while holding the set of filters s 1 , s 2 , s K fixed based upon the set of input signals X L includes solving a set of decoupled sparse regression problems using a convex optimization algorithm.
  • optimizing the set of filters s x , s 2 , s K includes optimizing the function: (S- L ,
  • FIG. 1 For various embodiments, relate to a non -transitory machine-readable storage medium encoded with instructions for determining the parameters of a sparse coding method wherein the parameters include a set of filters, including: instructions for defining an objective function that produces the set of filters and a set of associated weights based upon a set of input signals, a set of white-noise processes, and a regularization parameter; instructions for determining the set of associated weights and set of filters that produce an optimal solution of the objective function by iterating the following steps until convergence: instructions for optimizing the set of associated weights while holding the set of filters fixed based upon the set of input signals; and instructions for optimizing the set of filters while holding set of associated weights fixed based upon the set of input signals, wherein the input signal is reconstructed based upon the set of associated weights, the set of filters, and the set of white noise processes.
  • the parameters include a set of filters, including: instructions for defining an objective function that produces the set of filters and a set of associated weights
  • the set of filters are an inverse fast Fourier transform (IFFT) of the power spectral density of a stationary process.
  • IFFT inverse fast Fourier transform
  • instructions for optimizing the set of associated weights while holding the set of filters fixed based upon the set of input signals includes instructions for solving a set of decoupled sparse regression problems using a convex optimization algorithm.
  • instructions for optimizing the set of filters while holding set of associated weights fixed based upon the set of input signals includes using a cyclical block-coordinate decent algorithm.
  • FIG. 1 For various embodiments, relate to a non-transitory machine-readable storage medium encoded with instructions for determining the parameters of a sparse coding method wherein the parameters include a set of filters s x , s 2 , ... , s K , including: instructions for defining an objective function that produces the set of filters s x , s 2 , ... , s K and a set of associated weights a lr a 2 , . . . , a N based upon a set of input signals X L , white-noise processes W jfe , and a regularization parameter l; instructions for determining the set of associated weights a 1 a 2 , . . .
  • instructions for optimizing the set of associated weights a lr a 2 , . . . , a N while holding the set of filters s 1 , s 2 , ... , s K fixed based upon the set of input signals X L includes instructions for solving a set of decoupled sparse regression problems using a convex optimization algorithm.
  • instructions for optimizing the set of filters s x , s 2 , ... , s K while holding set of associated weights a lr a 2 , . . . , a N fixed based upon the set of input signals includes using a cyclical block-coordinate decent algorithm.
  • instructions for optimizing the set of filters s x , s 2 , ... , s K includes instructions for optimizing the function: [0028] Various embodiments are described, further including instructions for iteratively minimizing for each of the K filters s x , s 2 , s K until convergence the function:
  • FIG. 1 illustrates a flow chart of the method of producing the parameters for the sparse encoding method
  • FIG. 2 illustrates feature extraction from an input signal.
  • Continuous-time physiological signals such as the electrocardiogram (EKG), photoplethysmogram (PPG), and arterial blood pressure (ABP) signals, may be very useful in characterizing patient condition, particularly as part of a larger clinical decision support algorithm.
  • EKG electrocardiogram
  • PPG photoplethysmogram
  • ABSP arterial blood pressure
  • Embodiments of a sparse coding method that learns a representation of a class of physiological signals (e.g ., EKG) by learning a dictionary of temporal dynamic processes that are expressive of the physiological signal examples from a patient dataset will be describe herein.
  • Each dictionary element is an auto-regressive model, and each example signal is expressed as a sparse linear combination of autoregressive models.
  • the linear combination weighting assigned to each signal may be used as a feature vector for downstream machine learning tasks.
  • the representation is useful in that it provides invariance to phase distortions (including translation invariance), and is less sensitive to the particular type of EKG lead.
  • the sparse encoding method seeks to learn a representation for continuous -time physiological signals such as, for example, an EKG.
  • a common approach to such a problem is to apply a neural network architecture (e.g., convolutional neural network) to extract features from temporal filters applied to the signals.
  • Convolutional networks provide shift invariance to the representation.
  • the sparse encoding method described herein provides additional invariances to the derived representation by allowing each temporal filter to contour to the phase response profile of the time-series signal.
  • the representation allows for nonlinear phase distortions.
  • One benefit of this is that the representation may be common to different types of EKG leads or variations in other sorts of measurement sensors. This is because it is known that the temporal dynamics (power spectral density) is quite stable across different EKG leads. This may be true of other sorts of measurement sensors as well.
  • each element of the representation may be associated with a distinct power spectral density, which may provide additional interpretability for a clinician.
  • the sparse coding method includes an algorithm that may be implemented as a software package that may integrate with a patient monitoring system collecting continuous-time physiological signals.
  • the sparse coding method includes multiple components, which are briefly mentioned here and then described in more detail in the following sections: 1) a representation learning method; and 2) feature extraction and downstream machine learning.
  • the representation learning method involves learning a representation of the physiological time-series data that will enable features to be extracted for downstream processing.
  • the representation seeks to learn the temporal dynamics of time-series by decomposing it into a sum of auto-regressive processes.
  • ⁇ X 2 , ... 3 ⁇ 4 denote a collection of N time-series signals, which are allowed to be of different lengths.
  • each X L will include one or more segmented beats from an EKG signal.
  • EKG signals are used as an example, herein, the sparse encoding method may be applied to other measured physiological signals as well.
  • the goal of the representation learning is to learn a dictionary of K stationary processes that may be used to reconstruct the original collection of the time-series. In order for the representation to be meaningful, usually K « N.
  • s k the power spectral density of the stationary process (as a function of frequency)
  • IFFT denotes the inverse discrete Fourier transform.
  • Xi is to be reconstructed as: where w ik is a realization from a white-noise stationary process, * denotes convolution, and a ik is the associated weighting.
  • a ik characterizes the extent to which the temporal dynamics of X L resemble the temporal dynamics of the kth stationary process.
  • the white-noise process w ik allows the phase response of the filter to be contoured to each individual example without altering the magnitude spectrum. This gives the a ik coefficients invariance to a wide range of phase distortions (including linear phase distortions which yields translation invariance). This is in contrast to a typical convolutional network, which uses a fixed template filter and matches it along temporal shifts of the input example.
  • FIG. 1 illustrates a flow chart of the method of producing the parameters for the sparse encoding method 100.
  • the goal of the representation learning is to simultaneously learn the K global dictionary filters s x , s 2 , ... , s K and the process weights (a Ll, a i2 , ... , a iK ) for each input example X L. This may be accomplished by defining a variant of sparse coding, which formulates the overall objective function 110 as:
  • the first term of the optimization is a model-fitting term that measures how well each example is reconstructed from the dictionary of stationary processes.
  • the second term is an 11 -norm on the coefficient weighting vectors, which biases the solution to produce sparse solutions (e.g ., each CLi contains many zero entries).
  • the two terms are balanced by a regularization parameter l which may be tuned by a variety of means (e.g., cross-validation).
  • the above optimization problem may be solved by an alternating minimization strategy which iterates between two steps until convergence 125: 1) the canonical filters are held fixed while optimizing for the process coefficient vectors (this results in N decoupled sparse regression problems which may be solved via a variety of convex optimization algorithms) 115; and 2) holding the process coefficient vectors fixed and optimizing for the canonical filters 120.
  • the second step 120 may be achieved by a cyclical block-coordinate descent algorithm.
  • the optimization for the canonical filters simplifies to: The above may optimized by iteratively minimizing for each of the K canonical filters until convergence.
  • Equation (1) may then be solved by first marginalizing out the white noise process by equating it to the inverse Fourier Transform of the phase response of X tj .
  • the canonical filter may then be updated by solving a quadratic optimization problem.
  • the result of the representation learning stage is the set of canonical filters s 1 . . . , s K , whose power spectral densities characterize the temporal dynamics of the K dictionary stationary processes. These will be used for feature extraction in the next stage of the method.
  • FIG. 2 illustrates feature extraction from an input signal.
  • An input signal 205 is input to the representation learning feature extraction model 210 and a set of features a , 215 describing the input signal are output.
  • the learned dictionary stationary processes are held fixed (as learned by previous training and optimization) and the representation is sought for a new example time-series X.
  • the corresponding feature vector to extract is the coefficient vector“a” which weights the contribution of each stationary process to the reconstruction. This may be achieved by, after marginalizing out the white noise process analogously to that described above with respect to equation (1), solving a sparse regression problem of the form:
  • the sparse coding method may be useful for a wide range of clinical decision support algorithms related to physiological signals.
  • the representation learning stage may be used as a first-stage feature extraction engine for training predictive clinical or therapy decision support algorithms.
  • the sparse coding method described herein solves various technological problems.
  • the sparse coding method representation may be particularly useful in cases in which the EKG lead types are unknown, or there is a wide variability in the EKG leads collected across a population of patients. This may apply to other sorts of measurement sensors as well.
  • the sparse coding method provides a representation of an input signal that provides invariance to phase distortions (including translation invariance).
  • the sparse coding method also provides a representation of an input signal that allows for nonlinear phase distortions.
  • the sparse coding method may also be used for lossy/lossless compression of physiological signals. Another potential application of the sparse coding method is denoising.
  • the embodiments described herein may be implemented as software running on a processor with an associated memory and storage.
  • the processor may be any hardware device capable of executing instructions stored in memory or storage or otherwise processing data.
  • the processor may include a microprocessor, field programmable gate array (FPGA), application-specific integrated circuit (ASIC), graphics processing units (GPU), specialized neural network processors, cloud computing systems, or other similar devices.
  • FPGA field programmable gate array
  • ASIC application-specific integrated circuit
  • GPU graphics processing units
  • specialized neural network processors cloud computing systems, or other similar devices.
  • the memory may include various memories such as, for example LI, L2, or L3 cache or system memory.
  • the memory may include static random-access memory (SRAM), dynamic RAM (DRAM), flash memory, read only memory (ROM), or other similar memory devices.
  • SRAM static random-access memory
  • DRAM dynamic RAM
  • ROM read only memory
  • the storage may include one or more machine-readable storage media such as read-only memory (ROM), random-access memory (RAM), magnetic disk storage media, optical storage media, flash-memory devices, or similar storage media.
  • ROM read-only memory
  • RAM random-access memory
  • magnetic disk storage media magnetic disk storage media
  • optical storage media optical storage media
  • flash-memory devices or similar storage media.
  • the storage may store instructions for execution by the processor or data upon with the processor may operate. This software may implement the various embodiments described above.
  • embodiments may be implemented on multiprocessor computer systems, distributed computer systems, and cloud computing systems.
  • the embodiments may be implemented as software on a server, a specific computer, on a cloud computing, or other computing platform.
  • non-transitory machine-readable storage medium will be understood to exclude a transitory propagation signal but to include all forms of volatile and non-volatile memory.

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Abstract

La présente invention concerne un procédé de détermination des paramètres d'un procédé de codage épars dans lequel les paramètres comprennent un ensemble de filtres, le procédé consistant à : définir une fonction objective qui produit l'ensemble de filtres et un ensemble de poids associés sur la base d'un ensemble de signaux d'entrée, un ensemble de processus de bruit blanc, et un paramètre de régularisation ; déterminer l'ensemble de poids associés et l'ensemble de filtres qui produisent une solution optimale de la fonction objective par itération des étapes suivantes jusqu'à convergence : optimiser l'ensemble de poids associés tout en maintenant l'ensemble de filtres fixés sur la base de l'ensemble de signaux d'entrée ; et optimiser l'ensemble de filtres tout en maintenant un ensemble de poids associés fixés sur la base de l'ensemble de signaux d'entrée, le signal d'entrée étant reconstruit sur la base de l'ensemble de poids associés, de l'ensemble de filtres et de l'ensemble de processus de bruit blanc.
PCT/EP2020/050473 2019-01-16 2020-01-10 Apprentissage de dynamique temporelle pour extraction de caractéristiques et compression de formes d'onde physiologiques WO2020148163A1 (fr)

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KR20160098960A (ko) * 2015-02-11 2016-08-19 삼성전자주식회사 심전도에 기초한 인증 방법, 인증 장치, 심전도 기반 인증을 위한 학습 방법 및 학습 장치
US20160242690A1 (en) * 2013-12-17 2016-08-25 University Of Florida Research Foundation, Inc. Brain state advisory system using calibrated metrics and optimal time-series decomposition
US20160335224A1 (en) * 2014-03-31 2016-11-17 Los Alamos National Security, Llc Efficient convolutional sparse coding
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US20160242690A1 (en) * 2013-12-17 2016-08-25 University Of Florida Research Foundation, Inc. Brain state advisory system using calibrated metrics and optimal time-series decomposition
US20160335224A1 (en) * 2014-03-31 2016-11-17 Los Alamos National Security, Llc Efficient convolutional sparse coding
EP3132742A1 (fr) * 2014-04-30 2017-02-22 Samsung Electronics Co., Ltd. Dispositif d'imagerie à résonance magnétique et procédé de génération d'image de résonance magnétique
KR20160098960A (ko) * 2015-02-11 2016-08-19 삼성전자주식회사 심전도에 기초한 인증 방법, 인증 장치, 심전도 기반 인증을 위한 학습 방법 및 학습 장치

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