WO2012103585A1 - Procédé et système de définition d'artefacts de mouvement dans des signaux d'électrocardiogramme (ecg) - Google Patents

Procédé et système de définition d'artefacts de mouvement dans des signaux d'électrocardiogramme (ecg) Download PDF

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WO2012103585A1
WO2012103585A1 PCT/AU2012/000087 AU2012000087W WO2012103585A1 WO 2012103585 A1 WO2012103585 A1 WO 2012103585A1 AU 2012000087 W AU2012000087 W AU 2012000087W WO 2012103585 A1 WO2012103585 A1 WO 2012103585A1
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motion
data
ecg
signal
segmented
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English (en)
Inventor
Hang Ding
Antti Sarela
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Commonwealth Scientific and Industrial Research Organisation
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Commonwealth Scientific and Industrial Research Organisation
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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
    • A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • A61B5/7207—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts
    • A61B5/721—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts using a separate sensor to detect motion or using motion information derived from signals other than the physiological signal to be measured
    • 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
    • A61B5/349—Detecting specific parameters of the electrocardiograph cycle
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Measuring devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb

Definitions

  • This invention relates generally to defining motion artifacts in corrupted signals such as Electrocardiogram (ECG) signals, and in particular to a method and system for defining motion artifacts based on a regression analysis of noise level data.
  • ECG Electrocardiogram
  • An Electrocardiogram is a graphical recording of the electrical activity of the heart of a patient, and may be used to evaluate cardiac function.
  • An ECG is usually recorded when the patient is stationary. However, many heart problems are only noticeable during physical activity. Thus it is often necessary to record an ECG during physical activity, which is known as an ambulatory ECG. Recording an ambulatory ECG has been found to be useful for various purposes such as health promotion, cardiac disease risk predictions, cardiovascular disease prevention and treatments, and sports medicine.
  • Vpp peak-to peak Voltage
  • the invention resides in a method for defining motion artifacts in an electrocardiogram (ECG) signal, the method comprising:
  • segmenting the ECG signal to provide segmented ECG data
  • segmenting the motion signal to provide segmented motion data that corresponds to the segmented ECG data
  • quantifying the segmented ECG data and the segmented motion data to define, respectively, noise level data and motion intensity data
  • quantifying the motion artifacts based on the regression model comprises performing a root mean square (RMS) analysis.
  • RMS root mean square
  • quantifying the motion artifacts based on the regression model comprises calculating and analyzing parameters of the regression model.
  • deviations of the noise level data and motion intensity data from the regression model define whether the noise level data is unrelated to the motion signal or is part of the ECG signal.
  • analyzing the deviations of the noise level data and motion intensity data from the regression model comprises performing an entropy analysis of the noise level data and the motion intensity data.
  • the entropy analysis comprises determining a probability density function (PDF) of motion artifact variation.
  • PDF probability density function
  • the ECG signal is segmented using a QRS wave detection algorithm.
  • the regression analysis comprises a linear regression analysis using a least squares method.
  • the ECG signal and the motion signal are received from a single monitoring device and the motion signal comprises an acceleration signal.
  • the method is performed in real time while the patient is wearing an ECG monitoring device to assist in positioning of the ECG monitoring device on the patient.
  • the method further comprises excluding from post ECG analysis portions of the ECG signal corresponding to the segmented ECG data that is contaminated with defined motion artifacts.
  • the method is performed to validate an ECG monitoring device that acquires the ECG signal.
  • the invention resides in a system for defining motion artifacts in an electrocardiogram (ECG) signal, the system comprising:
  • the system includes computer readable program code for quantifying the segmented ECG data and the segmented motion data comprises performing a root mean square (RMS) analysis.
  • RMS root mean square
  • deviations of the noise level data and motion intensity data from the regression model define whether the noise level data is unrelated to the motion signal or is part of the ECG signal.
  • analyzing the deviations of the noise level data and motion intensity data from the regression model comprises performing an entropy analysis of the noise level data and the motion intensity data.
  • defining the motion artifacts based on deviations from the regression line comprises performing an entropy analysis of the noise level data and the motion intensity data.
  • the entropy analysis comprises determining a probability density function (PDF) of motion artifact variation.
  • PDF probability density function
  • the ECG signal is segmented using a QRS wave detection algorithm.
  • the regression analysis comprises a linear regression analysis using a least squares method.
  • the ECG signal and the motion signal are received from a single monitoring device operatively coupled to the processor, and the motion signal comprises an acceleration signal.
  • the system operates in real time while the patient is wearing an ECG monitoring device to assist in positioning of the ECG monitoring device on the patient.
  • the system further comprises computer readable program code components for excluding from post ECG analysis portions of the ECG signal corresponding to the segmented ECG data that is contaminated with defined motion artifacts.
  • the system operates to validate an ECG monitoring device that acquires the ECG signal.
  • the present invention resides in a method for defining motion artifacts in a corrupted signal, the method comprising:
  • segmenting the corrupted signal to provide segmented corrupted data
  • segmenting the motion signal to provide segmented motion data that corresponds to the segmented corrupted data; quantifying the segmented corrupted data and the segmented motion data to define, respectively, noise level data and motion intensity data;
  • FIG 1 shows a block diagram of a system for defining motion artifacts in an electrocardiogram (ECG) signal according to an embodiment of the present invention
  • FIG 2 shows a flow diagram of a method for defining motion artifacts in an ECG signal according to an embodiment of the present invention
  • FIG 3 shows a graph of noisy ECG signal data sets according to an embodiment of the present invention
  • FIG 4 shows a graph of ECG signal data sets separated from the noisy ECG signal data sets of FIG 3 according to an embodiment of the present invention
  • FIG 5 shows a graph of noise data sets separated from the noisy ECG signal data sets of FIG 3 according to an embodiment of the present invention
  • FIG 6 shows a graph of singular values against a rank of singular values according to an embodiment of the present invention
  • FIG 7 shows a graph of Motion Artifact versus Motion Intensity and a corresponding linear regression line of a Zephyr electrode according to an embodiment of the present invention
  • FIG 8 shows a graph of Motion Artifact versus Motion Intensity and a corresponding linear regression line of a Polar electrode according to an embodiment of the present invention
  • FIG 9 shows a graph of Motion Artifact versus Motion Intensity and a corresponding linear regression line of a 3M RedDot electrode according to an embodiment of the present invention
  • FIG 10 shows a graph comparing the linear regression lines of FIGs 8, 9 and 10 according to an embodiment of the present invention
  • FIG 11 shows a graph of a probability density function of the Polar and 3M electrodes of FIGs 8 and 9 according to an embodiment of the present invention
  • FIG 12 shows a graph of entropy value under different test conditions for the Zephyr, Polar and 3M RedDot electrodes according to an embodiment of the present invention
  • FIG 13 shows a graph of a Motion Artifact quantification error and R- wave detection error against noise amplitude according to an embodiment of the present invention.
  • FIG 14 shows a graph of a Motion Artifact quantification error against noise for different singular value rank selections according to an embodiment of the present invention.
  • adjectives such as first and second, left and right, top and bottom, etc., are used solely to define one element or method step from another element or method step without necessarily requiring a specific relative position or sequence that is described by the adjectives.
  • Words such as “comprises” or “includes” are not used to define an exclusive set of elements or method steps. Rather, such words merely define a minimum set of elements or method steps included in a particular embodiment of the present invention.
  • FIG 1 shows a block diagram of a system 10 for defining motion artifacts in a corrupted signal, such as a corrupted electrocardiogram (ECG) signal, according to an embodiment of the present invention.
  • the system 10 includes a processor 12, a memory 14 operatively coupled to the processor 12 and an ECG monitoring device 16 operatively coupled to the processor 12.
  • the connection between the processor 12 and the ECG monitoring device 16 is preferably wireless, for example using Bluetooth®, ZigBee, or cellular networks. However it should be appreciated that any suitable wired connection may also be used.
  • the memory 14 includes computer readable program code components for carrying out the method of the present invention.
  • the ECG monitoring device 16 connects to one or more electrodes
  • the ECG monitoring device 16 also includes an accelerometer (not shown) that generates an acceleration signal according to movement of the patient.
  • the ECG signal and a corresponding motion signal from the accelerometer are received and processed by the processor 12 according to the computer readable program code components stored in the memory 14.
  • FIG 2 shows a flow chart 20 of a method for defining motion artifacts in an ECG signal according to an embodiment of the present invention.
  • motion artifacts may be quantified according to a type of activity, an individual patient, a type of electrode or an ECG device setup.
  • an electrode may be quantified for use in measuring stationary and ambulatory ECGs.
  • the ECG signal and the corresponding motion signal are received by the processor 12, having been obtained from the patient.
  • the ECG signal and the corresponding motion signal are converted to a digital signal and are denoted as:
  • n is a total number of sampling points
  • E represents sampled digital data of the ECG signal
  • Ej, Axj, Ayi, AZj are the i-th sampled data set
  • Ax, Ay and Az are acceleration data defined as motion intensity (Ml) data according to the acceleration (or movement) of the patient in the x, y, and z axes.
  • the sampled ECG signal is segmented using a wave detection algorithm to provide segmented ECG data.
  • the ECG signal comprising of E: ⁇ Ei , E 2 , Ej ... E n ⁇ is segmented using a wave detection algorithm, for example as is well known in the art a Pan- Tompkins method can be used to detect the QRS wave of the ECG.
  • a Pan- Tompkins method can be used to detect the QRS wave of the ECG.
  • other applicable wave detection algorithms and signal segmentation techniques as are well known in the art, may be used.
  • the output from the wave detection algorithm is denoted as:
  • Rj is the sampling point where an R wave is detected
  • m is the total number of QRS waves in the ECG data; and j represents the j-th detected R wave.
  • the ECG signal ⁇ Ei, E 2 , ... E n ⁇ is segmented and the segmented ECG data (ES) is denoted by: ES: ⁇ ESi, ES 2l ... ES j , ... ES m ⁇ T , Eq. 2
  • the time period from ER j . 30 to ERj « ⁇ 70 typically has a duration of approximately 0.33 seconds and is mainly occupied by the QRST wave segment of the ECG signal. This segment period may be selected as it is relatively stable, and it does not overlap with a neighbouring PQRST waveform for heart rates of up to 180 beats per minute (BPM). However it should be appreciated that other durations may be used depending on the application and heart rates that may be encountered.
  • the motion signal is segmented to provide segmented motion data that corresponds to the segmented ECG data.
  • the motion signal is segmented and expressed as segmented motion data as follows:
  • the ECG signals containing the MA or noise may be modelled by the equation:
  • ES is the acquired ECG data, as in Eq. 3;
  • ER is the real ECG
  • MR is the real Ml generated by the movement of the patient and obtained from the accelerometer of the ECG monitoring device 16.
  • ER and MR are separated by applying a Principle Component Analysis (PCA).
  • PCA Principle Component Analysis
  • the principle components are obtained by a Singular Vector Decomposition (SVD) method.
  • ES p*q is decomposed to: CT
  • U is a unitary matrix
  • S is a diagonal matrix with non-negative singular values on its diagonal
  • V is the eigenvector matrix where the matrix columns are eigenvectors.
  • V CT denotes the conjugate transpose of V.
  • q is the number of samples in one ES data set and is set according to a number of samples acquired. It should be appreciated that q may be any suitable number depending of the resolution of the data set.
  • the PCA for separating the ER signal and MR signal from an acquired ECG signal is demonstrated graphically in FIGs 3 to 5.
  • one hundred and fifty (150) QRS waveforms are extracted from acquired ECG signals (ES) using the PanTomkins method previously described.
  • the ECG signals are acquired from a resting patient.
  • Random noise added to the acquired ECG signals is shown in FIG 3.
  • the noisy acquired ECG signal is extracted using PCA and the extracted ECG signal (ER) is shown in FIG 4, and the noise signal (MR) is shown in FIG 5.
  • the z-axis 32, 42, 52 is the amplitude of the signal
  • the y- axis 34, 44, 54 is a data set of the extracted QRS waveforms
  • the x-axis 36, 46, 56 is a sample of the data set.
  • the singular values in the diagonal matrix S are ranked by arranging singular values from the highest values first to the lowest values last. A high singular value reflects the significance of the information contained in a corresponding Eigenvector. It is considered that ER, containing the ECG signals, represents major information in the ES and that the MR, which is close to "white noise", contains no information. Based on ranks of the singular values, ER and MR are separated and reconstructed by the equations:
  • Nr is the rank of the singular value and is determined experimentally from the data obtained in FIG 3.
  • FIG 6 shows a graph 60 of an Eigen spectral analysis of MA showing singular values 61 plotted against a sequence of singular values 62.
  • Nr 2
  • the segmented ECG data and the segmented motion data are quantified to define, respectively, noise level data and motion intensity (Ml) data.
  • a Root Mean Square (RMS) method is used to quantify the motion artifact segments and motion segments to respectively reflect the noise level and the motion intensity.
  • a regression analysis is performed on the noise level data and the motion intensity data to define a regression model.
  • the motion artifacts may be quantified based on the regression model parameters.
  • MAp A + B ⁇ Mlp Eq. 14
  • MAp is the power of MA in an extracted segment and is calculated by the root mean squared (RMS) value
  • a and B are output variables of the regression analysis model and define the characteristics of the MA.
  • Mlp is a power of Ml of a corresponding MA segment.
  • the regression line of the regression analysis model is obtained using a least squares method.
  • FIGS 7-9 show Ml 71 , 81 , 91 plotted against MA 72, 82, 92, respectively, a corresponding regression line 73, 83, 93 and a formula 74, 84, 94, respectively, of a regression line 73, 83, 93 relating to Eq. 14. Also shown is a probability of statistic significance of null hypothesis of linear correlation, p 75, 85, 95 and a square of the correlation coefficient r 2 76, 86, 96.
  • the regression line may be analysed to determine a performance of an electrode.
  • the regression lines 101 , 102, 103 for, respectively, the Polar electrode, the Zephyr electrode, and the 3M RedDot electrode are shown in FIG 10.
  • a slope of the regression line indicates the electrode or measurement set-up's suitability for use in ambulatory conditions. The lower the value of B the more suited the electrode or set-up is for ambulatory conditions. From FIG 10, the Polar Electrode is most suited for ambulatory ECG measurements as the slope of the regression line 101 is the least steep.
  • An intersection (parameter A in Eq. 14) of the regression line on the y- axis demonstrates an electrode or an ECG measurement setup's suitability for resting ECG measurements.
  • a lower value of A demonstrates that the electrode is more suited for resting ECG measurements.
  • the 3M RedDot electrode is most suited to resting ECG measurements as its regression line intersects the x-axis at the lowest value.
  • Entropy-based algorithms quantify the regularity of a control system .
  • Entropy increases with the degree of disorder and is a maximum for completely random systems.
  • entropy is used to quantify how well the MA and Ml data points align with a linear regression line, or how much the MA and Ml data points deviate from a regression model. That assists in determining whether noise level data is unrelated to a motion signal or whether such data is part of an ECG signal.
  • vj MApj - [ A + B ⁇ Mlp j ] Eq.15
  • a probability density function (PDF) of motion artifact variation is estimated by the Parzen PDF estimation method (see, e.g., Parzen, E., On Estimation of Probability Density Function and Mode. Annals of Mathematical Statistics, 962. 33: p. 1065-1076).
  • PDF probability density function
  • h is a window width parameter
  • K[u] is a weighting function, or window function.
  • a normal distribution is used as a weighting function as proposed by Parzen and is expressed as:
  • ⁇ is the standard deviation of V
  • m is the total number of calculated v.
  • FIG 1 shows a graph 110 of the probability values 111 of the probability density function (PDF) of Ml for the polar electrode 112 and a 3M electrode 113 whilst a patient is jogging.
  • PDF probability density function
  • the PDF of the Polar electrode 112 has a single dominant peak 115, whereas the PDF of the 3M electrode 113 displays a dominant peak 116 and a secondary peak 17, as well as some fluctuations between the dominant peak 116 and the secondary peak 117.
  • the performance of the 3M electrode is less consistent as the PDF shifts from one motion state to another depending on the test condition and different patients.
  • Entropy quantifies the PDF of V to reflect how well V aligns with the regression line, and can be calculated by the equation: ENT - / P(v) x log 10 (P(v)) dx Eq. 19 where:
  • a lower entropy value means that values of v are more closely aligned with the regression line, thus the performance of a type of electrode is more independent of the variation of motion intensity and individual patients.
  • FIG 12 shows a graph 120 of entropy values 121 for the Zephyr electrode 122, the Polar electrode 123 and the 3M electrode 124.
  • the entropy values 121 indicate how well the MA and Ml data points align with the linear regression line of FIGs 7, 8 and 9 when a patient is resting 125, walking 126 and jogging 127.
  • a lower entropy value indicates that the MA and Ml points are closely aligned with the regression line and are thus suitable for a particular motion state.
  • the 3M RedDot electrode is most suited for resting states, as it has the lowest entropy value in the resting motion state.
  • the Polar electrodes are more suited for ambulatory states as they have the lowest entropy values in the jogging and running motion states.
  • MA quantification error rates 134 are not significantly affected when the amplitude of noise on an ECG signal increases.
  • FIG 14 shows a graph of the mean MA quantification error 142 against noise amplitude 144 for singular value ranks 146, Nr, of 1 , 2, 3 and 4.
  • the method and system of the present invention allows ECG electrodes and ECG set-ups to be quantified.
  • electrodes and ECG set-ups can be effectively selected, from a linear regression and entropy analysis performed on acquired ECG signals, to best suit particular applications.
  • parameters of a regression model may be used to determine an electrode's suitability for resting or ambulatory ECG measurements.
  • an entropy and a Probability Density Function analysis may be used to determine an electrode's consistency for use in different motion states.
  • the teachings of the present invention also can be applied to analyse corrupted signals other than ECG signals.
  • the present invention can be used to define motion artifacts in other biometric signals such as blood pressure signals, muscle tremor signals, and electroencephalograph (EEG) signals.
  • EEG electroencephalograph
  • the present invention can be used in the analysis of various signals that are corrupted by motion artifacts and which have a characteristic pattern that can be effectively segmented as described herein.

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Abstract

La présente invention concerne un procédé de définition d'artefacts de mouvement dans un signal corrompu, tel qu'un signal d'électrocardiogramme (ECG), qui permet une meilleure acquisition de signal et une meilleure analyse. Le procédé comprend la réception du signal corrompu et d'un signal de mouvement correspondant (étape 21) ; la segmentation du signal corrompu pour fournir des données corrompues segmentées (étape 22) ; la segmentation du signal de mouvement pour fournir des données de mouvement segmentées qui correspondent aux données corrompues segmentées (étape 23) ; la quantification des données corrompues segmentées et les données de mouvement segmentées pour définir, respectivement, les données de niveau de bruit et les données d'intensité de mouvement (étape 24) ; la réalisation d'une analyse de régression sur les données de niveau de bruit et les données d'intensité de mouvement pour définir un modèle de régression (étape 25) ; et la quantification des artefacts de mouvement en se basant sur le modèle de régression (étape 26).
PCT/AU2012/000087 2011-02-02 2012-02-02 Procédé et système de définition d'artefacts de mouvement dans des signaux d'électrocardiogramme (ecg) Ceased WO2012103585A1 (fr)

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CN103720468A (zh) * 2013-12-05 2014-04-16 深圳先进技术研究院 应用于动态心电数据的伪差识别方法和装置
WO2014155230A1 (fr) * 2013-03-29 2014-10-02 Koninklijke Philips N.V. Appareil et procédé pour l'élimination d'artefacts de mouvement dans un ecg

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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2014155230A1 (fr) * 2013-03-29 2014-10-02 Koninklijke Philips N.V. Appareil et procédé pour l'élimination d'artefacts de mouvement dans un ecg
CN105101870A (zh) * 2013-03-29 2015-11-25 皇家飞利浦有限公司 用于ecg运动伪影去除的装置和方法
US9931081B2 (en) 2013-03-29 2018-04-03 Koninklijke Philips N.V. Apparatus and method for ECG motion artifact removal
RU2677007C2 (ru) * 2013-03-29 2019-01-14 Конинклейке Филипс Н.В. Устройство и способ для устранения артефактов движения при проведении экг
CN105101870B (zh) * 2013-03-29 2019-01-22 皇家飞利浦有限公司 用于ecg运动伪影去除的装置和方法
CN103720468A (zh) * 2013-12-05 2014-04-16 深圳先进技术研究院 应用于动态心电数据的伪差识别方法和装置

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