WO2013052944A1 - Réduction d'artéfact périodique à partir de signaux biomédicaux - Google Patents

Réduction d'artéfact périodique à partir de signaux biomédicaux Download PDF

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Publication number
WO2013052944A1
WO2013052944A1 PCT/US2012/059217 US2012059217W WO2013052944A1 WO 2013052944 A1 WO2013052944 A1 WO 2013052944A1 US 2012059217 W US2012059217 W US 2012059217W WO 2013052944 A1 WO2013052944 A1 WO 2013052944A1
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signal
biomedical
biomedical signal
ecg
estimated
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PCT/US2012/059217
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English (en)
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Gari CLIFFORD
Julien Oster
Olivier Pietquin
Matthieu GEIST
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Isis Innovation Limited
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    • 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
    • A61B5/7235—Details of waveform analysis
    • A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves 
    • A61B5/055—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves  involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
    • 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]
    • 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]
    • A61B5/344—Foetal cardiography
    • 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
    • A61B5/369—Electroencephalography [EEG]
    • 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
    • A61B5/389—Electromyography [EMG]

Definitions

  • the ECG signal can be distorted because of various effects associated with the MRI.
  • the present disclosure provides systems and methods for reducing or separating artifacts such as magnetohydrodynamic (MHD) effects from electrocardiograms acquired during a magnetic resonance imaging (MRI) examination.
  • MHD magnetohydrodynamic
  • ballistocardiogram effects can be reduced or separated from electroencephalogram (EEG) acquisitions during functional MRI (fMRI).
  • EEG electroencephalogram
  • the reduction or separation of an artifact is
  • the method can includes: acquiring a first biomedical signal apart from the noisy environment; modeling the first biomedical signal; acquiring a second biomedical signal of the same type but acquired in the noisy environment, the second biomedical signal including the artifact; modeling the second biomedical signal; and filtering the biomedical signal by determining an estimated signal from a combination of the modeled first and second biomedical signals and then separating the estimated combination of the first and second biomedical signals into an estimated first biomedical signal and an estimated second biomedical signal.
  • the method can be a computer implemented method.
  • a system for reducing an artifact from a biomedical signal acquired in a noisy environment.
  • the artifact can be periodic or semi-periodic.
  • the system can includes: a data acquisition system configured to acquire a first biomedical signal apart from the noisy environment, and to acquire a second biomedical signal of the same type but acquired in the noisy environment, the second biomedical signal including the artifact; and a processing system coupled to the data acquisition system, the processing system being configured to model the first biomedical signal, to model the second biomedical signal, and to filter the biomedical signal by determining an estimated signal from a combination of the modeled first and second biomedical signals and then separating the estimated combination of the first and second biomedical signals into an estimated first biomedical signal and an estimated second biomedical signal.
  • the biomedical signal can be selected from one or more of an electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG), fetal ECG, blood pressure waveform or photoplethysmograph (PPG).
  • ECG electrocardiogram
  • EEG electroencephalogram
  • EMG electromyogram
  • fetal ECG blood pressure waveform or photoplethysmograph
  • the noisy environment can be a magnetic resonance imaging (MRI) scanner and the first biomedical signal can be acquired outside of the bore of the MRI scanner and the second biomedical signal can be acquired inside the bore of the MRI scanner.
  • the first and second biomedical signals can be each modeled as a pseudo periodic signal.
  • the first and second biomedical signals can be modeled by fitting Gaussian parameters to the biomedical signals.
  • the filtering can involve nonlinear Bayesian filtering.
  • the filtering can include Kalman filtering, particle filtering or Gaussian processes.
  • the filtering step can include use of the following observation equations to provide an estimation of a periodic artifact to be reduced or separated from the biomedical signal:
  • the artifact can be a
  • FIG. 1 is a flowchart showing one embodiment of a process for separating magnetohydrodynamic (MHD) effects from an electrocardiogram (ECG).
  • MHD magnetohydrodynamic
  • ECG electrocardiogram
  • FIGS. 2A and 2B are flowcharts showing one embodiment of a process for
  • FIG. 4 is an example of ECG data contaminated by MHD effects, obtained from a patient in the presence of a magnetic resonance imaging (MRI) magnetic field.
  • MRI magnetic resonance imaging
  • FIG. 5 is an example of superimposed data, showing ECG data contaminated by MHD effects in a 1.5 Tesla (T) magnetic field, the MHD effects, and ECG data from which MHD effects are removed.
  • FIG. 6 is an example of superimposed data, showing ECG data contaminated by MHD effects in a 3.0 T magnetic field, the MHD effects, and ECG data from which MHD effects are removed.
  • Electrocardiograms are important physiological signals used in diagnosing cardiovascular pathologies.
  • ECGs are electrical recordings resulting from successive depolarization of cardiac cells.
  • Magnetic resonance imaging is a non-invasive radiological technique that allows for depiction of soft tissue and organs of the body. Using MRI it is possible to distinguish pathological tissue from healthy tissue, often without using a contrast agent.
  • ECG electrocardial potential
  • cardiac abnormalities e.g., cardiac CAD, cardiac CAD, cardiac CAD, cardiac CAD, etc.
  • acquisition of an ECG during an MRI scan allows for synchronization of MRI acquisition with heart motion, which accounts for organ motion during the scan.
  • ECG relies on electromagnetic fields naturally induced by the body.
  • the MRI environment has three major physical characteristics, which affect the ECG signal: high static magnetic field; fast varying magnetic fields (gradients); and radio frequency (RF) pulses.
  • RF pulses induce an electrical field inside the body that can also interfere with the electrical components involved in measuring the ECG signal.
  • Gradients induce electrical fields inside the body whose frequency range overlaps an ECG signal causing ECG signal distortion.
  • the presence of the high static magnetic field in MRI scanners induces another, somewhat-indirect artifact on the ECG signal.
  • This indirect effect arises from blood carrying electrically charged particles, such as iron, whose motion inside a magnetic field creates a current source.
  • the strength of the MRI magnetic field is such that the current source created by the blood flow is about the same magnitude as that of the heart natural electrical activity. It has been shown that the main contribution of the MHD effect is induced by the blood ejection through the aortic arch, because of the geometry of the arch, the diameter of the artery and the blood velocity.
  • Various embodiments of the present disclosure therefore, provide systems and methods for removing contaminants from a time series that is similar in frequency, morphology, amplitude, and timing to the signal of interest. Due to the contaminant's similarity to the signal of interest, these types of contaminants are difficult to remove.
  • existing techniques employ simple template matching (and subtraction) or simple adaptive filtering. However, in the case of template subtraction, the changing morphology and timing of the beat is not modeled well. So, residual errors cause clinically significant distortions in the electrocardiogram.
  • Adaptive filtering carries little understanding of either the signal of interest or the contaminant signal. Thus, adaptive filtering techniques per se may not be ideal.
  • Various embodiments of the present systems and methods combine a model of both the artifact and the signal of interest, which adapts to sample-by- sample changing dynamics to make estimates of underlying sources of information.
  • embodiments described herein use models of both a biomedical signal of interest, such as ECG, and artifact sources from the individual whose physiological parameters are being measured.
  • the resulting signal is custom tailored to each individual.
  • the separation of the two contributions for example, the separation of the two contributions
  • the ECG is modeled (1002).
  • the ECG can be modeled by fitting Gaussian parameters to the ECG.
  • the Gaussian parameters can be fit to the ECG using the methods described in the aforementioned Clifford Application (US published application no. US 2007/026015), which is incorporated by reference as if expressly set forth herein in its entirety.
  • the ECG signal can be modeled as a pseudo periodic signal, whose period cycle is a sum of Gaussians, such that:
  • the ECG can be modeled with a dynamical vector cardiograph (VCG) model, that represents each of the three axes of the VCG by a sum of Gaussians and then applies a Dower transform.
  • VCG dynamical vector cardiograph
  • G. D. Clifford et al An artificial vector model for generating abnormal electrocardiographic rhythms. Phys. Meas., 31 :595-609, 2010.
  • the parameters of the Gaussian representing the T wave can be evolving.
  • the T wave inversion can be modeled by inverting the amplitude of the T wave Gaussians with a logistic function over ten cycles.
  • the prolonged QT interval can be modeled by moving forward angular position of the T wave Gaussians with a logistic function over 10 cycles with an amplitude of OArad.
  • the number of Gaussians can vary between the subjects as the effect is strongly influenced by blood flow characteristics.
  • the blood flow can be more or less laminar, and the presence of vortices induces the presence of high frequencies on the MHD effect and increases the number of Gaussians required for its approximation.
  • the out-of-bore ECG acquisition (1001), the ECG modeling (1002), the in- bore ECG acquisition (1003), and the MHD modeling (1004) can be seen as the initialization process, which is performed at the beginning of an MRI examination.
  • the patient's ECG signal can be filtered.
  • the patient's ECG signal can be filtered using Bayesian filtering (1005) or other adaptive processes.
  • Bayesian filtering processes include the Kalman Filter (such as an Extended Kalman Filter), the particle filter and Gaussian processes.
  • Kalman Filter such as an Extended Kalman Filter
  • particle filter such as an Extended Kalman Filter
  • Gaussian processes One embodiment of a Bayesian filtering (1005) process is shown in FIG. 2.
  • Bayesian filtering aims at recursively estimating a set of hidden variables, x, given a sequence of noisy observation, ⁇ .
  • the observations are related to the hidden state (the artifact to be removed) by a supposedly known observation equation and the evolution state can be determined by the evolution equation, described in more detail below.
  • one embodiment of a Bayesian filtering (1005) process begins with the inputting (2000) of data. This data includes patient ECG acquired (2005) during an MRI scan, the modeled (1002) ECG signal, and the modeled (1004) MHD signal.
  • an estimated ECG+MHD signal is calculated (2001) from the acquired (2005) ECG, using the modeled (1002) ECG signal and the modeled (1004) MHD signals, respectively.
  • the resulting output (2002) from the calculated (2001) ECG+MHD signals is then separated (2003) into an estimated ECG signal and an estimated MHD signal, which are subsequently outputted (2004).
  • Results from such a Bayesian filtering (1005) process are shown in FIGS. 5 and 6, for field strengths of 1.5T and 3.0T, respectively.
  • the ECG signal can be first recorded during
  • a number of cycles for example 10 ECG cycles, can be recorded outside of the MRI bore and used in order to compute an ECG template and initialize Gaussian parameters by, for example, computing the Gaussian parameters of the mean ECG cycle. See, e.g., R. Sameni, M. B. Shamsollahi, C. Jutten, and G. D. Clifford, "A Nonlinear Bayesian Filtering Framework for ECG Denoising.” IEEE Trans. Biomed. Eng., vol. 54, pp. 2172-2185, 2007.
  • a number of cycles for example 10 cycles, can be extracted and used to compute a mean template.
  • This template corresponds to the sum of the ECG and MHD.
  • An MHD template can be estimated by subtracting the ECG template.
  • the MHD Gaussian parameters can then be initialized in the same way as for the ECG.
  • a Bayesian filter for example an Extended Kalman Filter (EKF)
  • EKF Extended Kalman Filter
  • the MHD effect overlaps the ECG signal in the frequency domain.
  • the Kalman Filter based technique can be used to separate MHD and ECG dynamics, since they are mathematically modeled. This approach takes into consideration that both ECG and MHD are non- stationary. Since the MHD contribution occurs simultaneously with the clinically important ECG waves, the separation of the dynamics is difficult, if its dynamics are not temporally separated.
  • a new observation equation is introduced in which a synthetic signal is created by subtracting from the raw ECG observation the prior information on the ECG signal (e.g., the sum of Gaussians of the ECG model given the synthetic phase signal). This new observation gives an approximation of the MHD effect.
  • Gaussian parameters are not included even though the signal is non-stationary, however the uncertainty in the noise allows its consideration in this model.
  • Bayesian filtering techniques rely on some parameter adjustments. Their initialization should be done such that they reflect properly the problem encountered.
  • the observation noise covariance matrix of the Bayesian filter reflects the level of trust in the measurements. In the case where the measurements are biomedical signal acquisitions, which are non-stationary, the level of trust in the measurements can vary dramatically during an examination given patient motion and other external factors.
  • signal quality indices SQI's
  • SQI's can be used to adjust automatically the observation noise covariance matrix for optimal Bayesian filtering.
  • EKF Extended Kalman Filter
  • the energy of is a linear function of with
  • FIG. 7 is a flow chart that depicts an aspect of use of a Signal Quality Index to automatically adjust the observation noise covariance matrix to optimize Gaussian Filtering.
  • An acquired biomedical signal is input (701) into the filtering scheme.
  • a Signal Quality Index (SQI) can be computed and used to estimate a noise level (702) in the inputted biomedical signal.
  • the biomedical signal can be analyzed (703) with a Gaussian Filtering technique, such as an Extended Kalman Filter (EKF).
  • EKF Extended Kalman Filter
  • the observation noise covariance matrix of the filter can be adjusted (704) to the estimated noise level. For example, the adaption of the noise level can be done with the above amplification signal. This technique, thus, allows for optimal analysis of biomedical signals even when the conditions of recording are changing (and thus also the level of noise).
  • model-based Bayesian filtering techniques rely on the prior knowledge of a system.
  • this prior knowledge is the dynamics of the ECG signal represented in one aspect by a sum of Gaussians. The occurrence of pathological rhythms can be followed by a drastic change of the ECG morphology in which case the prior knowledge will not correspond to the signal acquired in the noisy environment.
  • a multiple model approach during which the best-fitted model will be automatically selected, can be employed for the analysis of such biomedical signals.
  • different models can reflect a normal case, a recurrent pathologic rhythm such as Premature Ventricular Rhythm, and an unexpected pathological rhythm or any abnormality which can be represented by a dummy model and can allow detection of abnormalities in the signal.
  • a switching Kalman Filter can be used to allow estimation of the different model parameters and the selection of the best model in parallel. The best model can then be applied in the filtering step.
  • the ECG signal acquired (1001) outside of the noisy environment can be modeled (1002) not just for a normal case, but also for example for Premature Ventricular Contraction (PVC) and for an X factor mode, i.e. and unknown beat (whether artifacts or unknown pathological beat).
  • PVC Premature Ventricular Contraction
  • X factor mode i.e. and unknown beat (whether artifacts or unknown pathological beat).
  • the acquired ECG signal (1001) is modeled for not just one mode but for the three different modes.
  • the types of modes and the number of modes modeled are not limited to these particular three modes. Instead any number and types of modes can be modeled.
  • a Kalman filter for each new ECG sample acquired from the noisy environment the Kalman filter is computed for each of the three modes and the likelihood is then used to choose which of the three modes is best suited to the observation. The switching Kalman filter is then allowed to switch to the most likely mode and filter (1005) the ECG signal using the most appropriate prior knowledge (i.e., the modeled signal acquired outside of the noisy environment of the most likely of the three modes).
  • a switching Kalman Filter is described in greater detail in Appendix D hereto, "Tracking arrhythmias in the ECG using a switching Kalman filter.”
  • these processes are implemented in hardware, software, firmware, or a combination thereof.
  • these processes are implemented in software or firmware that is stored in a memory and that is executed by a suitable instruction execution system. If implemented in hardware, as in an alternative embodiment, these processes can be implemented with any or a combination of the following technologies, which are all well known in the art: a discrete logic circuit(s) having logic gates for implementing logic functions upon data signals, an application specific integrated circuit (ASIC) having appropriate combinational logic gates, a programmable gate array(s) (PGA), a field programmable gate array (FPGA), etc.
  • ASIC application specific integrated circuit
  • PGA programmable gate array
  • FPGA field programmable gate array
  • 1 and 2 may be implemented as a computer program, which comprises an ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions.
  • a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
  • the computer-readable medium can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium.
  • the computer-readable medium would include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM) (electronic), a readonly memory (ROM) (electronic), an erasable programmable read-only memory (EPROM or Flash memory) (electronic), an optical fiber (optical), and a portable compact disc read-only memory (CDROM) (optical).
  • an electrical connection having one or more wires
  • a portable computer diskette magnetic
  • RAM random access memory
  • ROM readonly memory
  • EPROM or Flash memory erasable programmable read-only memory
  • CDROM portable compact disc read-only memory
  • the computer- readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
  • the disclosed systems and methods can be used to remove ballistocardiogram effects from electroencephalogram (EEG) acquisitions during functional MRI (fMRI), or any other bioelectric signals acquired during MRI (e.g., electrooculogram (EOG), electromyogram (EMG), fetal ECG, etc).
  • EEG electroencephalogram
  • EMG electromyogram
  • fetal ECG fetal ECG

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Abstract

La présente invention concerne des systèmes et des procédés pour la séparation d'artéfacts périodiques ou pseudo-périodiques (par exemple des effets magnétohydrodynamiques (MHD)) à partir de signaux biomédicaux d'intérêt (par exemple des électrocardiogrammes (ECG)) par des procédés de filtration Bayesienne non linéaires sur la base d'un modèle ou d'autres procédés adaptatifs.
PCT/US2012/059217 2011-10-06 2012-10-08 Réduction d'artéfact périodique à partir de signaux biomédicaux WO2013052944A1 (fr)

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Cited By (8)

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EP3025639A1 (fr) * 2014-11-26 2016-06-01 BIOTRONIK SE & Co. KG Systeme electrocardiographique
WO2019069148A1 (fr) * 2017-10-06 2019-04-11 Florida Atlantic University Board Of Trustees Systèmes et procédés de guidage d'un cathéter à capteur multipolaire afin de localiser des sources d'arythmie cardiaque
CN109754448A (zh) * 2018-12-29 2019-05-14 深圳安科高技术股份有限公司 一种ct心脏扫描伪影校正方法及其系统
US10398346B2 (en) 2017-05-15 2019-09-03 Florida Atlantic University Board Of Trustees Systems and methods for localizing signal resources using multi-pole sensors
US10572637B2 (en) 2014-09-01 2020-02-25 Samsung Electronics Co., Ltd. User authentication method and apparatus based on electrocardiogram (ECG) signal
CN111751750A (zh) * 2020-06-19 2020-10-09 杭州电子科技大学 基于模糊ekf的多阶段闭环锂电池soc估算方法
CN112509074A (zh) * 2020-11-09 2021-03-16 成都易检医疗科技有限公司 伪影消除方法、系统、终端及存储介质
CN112932440A (zh) * 2019-11-25 2021-06-11 上海联影医疗科技股份有限公司 流速编码方法、磁共振成像方法和磁共振成像系统

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US20080004537A1 (en) * 2006-06-30 2008-01-03 Kimmo Uutela Method and system for multi-channel biosignal processing

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Cited By (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10572637B2 (en) 2014-09-01 2020-02-25 Samsung Electronics Co., Ltd. User authentication method and apparatus based on electrocardiogram (ECG) signal
EP3025639A1 (fr) * 2014-11-26 2016-06-01 BIOTRONIK SE & Co. KG Systeme electrocardiographique
US10398346B2 (en) 2017-05-15 2019-09-03 Florida Atlantic University Board Of Trustees Systems and methods for localizing signal resources using multi-pole sensors
WO2019069148A1 (fr) * 2017-10-06 2019-04-11 Florida Atlantic University Board Of Trustees Systèmes et procédés de guidage d'un cathéter à capteur multipolaire afin de localiser des sources d'arythmie cardiaque
US10398338B2 (en) 2017-10-06 2019-09-03 Florida Atlantic University Board Of Trustees Systems and methods for guiding a multi-pole sensor catheter to locate cardiac arrhythmia sources
CN109754448A (zh) * 2018-12-29 2019-05-14 深圳安科高技术股份有限公司 一种ct心脏扫描伪影校正方法及其系统
CN109754448B (zh) * 2018-12-29 2023-01-17 深圳安科高技术股份有限公司 一种ct心脏扫描伪影校正方法及其系统
CN112932440A (zh) * 2019-11-25 2021-06-11 上海联影医疗科技股份有限公司 流速编码方法、磁共振成像方法和磁共振成像系统
CN112932440B (zh) * 2019-11-25 2023-07-11 上海联影医疗科技股份有限公司 流速编码方法、磁共振成像方法和磁共振成像系统
CN111751750A (zh) * 2020-06-19 2020-10-09 杭州电子科技大学 基于模糊ekf的多阶段闭环锂电池soc估算方法
CN112509074A (zh) * 2020-11-09 2021-03-16 成都易检医疗科技有限公司 伪影消除方法、系统、终端及存储介质

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