WO2016034203A1 - Procédé et dispositif pour la classification automatique de battements de cœur, produit-programme d'ordinateur et appareil ecg pour la mise en œuvre du procédé - Google Patents

Procédé et dispositif pour la classification automatique de battements de cœur, produit-programme d'ordinateur et appareil ecg pour la mise en œuvre du procédé Download PDF

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Publication number
WO2016034203A1
WO2016034203A1 PCT/EP2014/068534 EP2014068534W WO2016034203A1 WO 2016034203 A1 WO2016034203 A1 WO 2016034203A1 EP 2014068534 W EP2014068534 W EP 2014068534W WO 2016034203 A1 WO2016034203 A1 WO 2016034203A1
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WO
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Prior art keywords
qrs
heartbeat
sequence
decision tree
template
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PCT/EP2014/068534
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German (de)
English (en)
Inventor
Roger Abächerli
Remo Leber
Ramun SCHMID
Johann-Jakob Schmid
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Schiller Ag
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Priority to PCT/EP2014/068534 priority Critical patent/WO2016034203A1/fr
Publication of WO2016034203A1 publication Critical patent/WO2016034203A1/fr

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Classifications

    • 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
    • A61B5/7246—Details of waveform analysis using correlation, e.g. template matching or determination of similarity
    • 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
    • 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
    • A61B5/30—Input circuits therefor
    • A61B5/307—Input circuits therefor specially adapted for particular uses
    • A61B5/308—Input circuits therefor specially adapted for particular uses for 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]
    • A61B5/346—Analysis of electrocardiograms
    • A61B5/349—Detecting specific parameters of the electrocardiograph cycle
    • A61B5/35—Detecting specific parameters of the electrocardiograph cycle by template matching
    • 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
    • A61B5/364—Detecting abnormal ECG interval, e.g. extrasystoles, ectopic heartbeats
    • 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
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • 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
    • A61B5/30—Input circuits therefor
    • 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]
    • A61B5/339—Displays specially adapted therefor
    • A61B5/341—Vectorcardiography [VCG]
    • 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
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/74—Details of notification to user or communication with user or patient ; user input means
    • A61B5/742—Details of notification to user or communication with user or patient ; user input means using visual displays

Definitions

  • the present invention relates to a method and an on ⁇ direction for the automatic classification of heart beats, and a computer program product and an ECG device for imple ⁇ out the method according to the preambles of the independent claims.
  • US 5,817,027 discloses a method for classification of heart beats, the heart beats are divided into at least four main Klas ⁇ sen. Heartbeats are iteratively compared with multiple templates until a minimum area difference between the heartbeat signal and the template reaches a threshold. If this threshold is not reached for any of the templates, a new template is created. This method is time consuming and at a plurality of different single or irregular beats of the heart, the accuracy decreases, because each will always new templates it provides ⁇ and has long existing templates on cried ⁇ ben. Also, frequent iterations delay the output of the classification. It is an object of the invention to overcome these and other disadvantages of
  • a method and a device for automatic ⁇ tables classification should especially supraventricular and made ventricular heartbeats in the electrocardiogram are available which preferably ximal work with a delay of ma ⁇ a heartbeat, and after an initial learning phase, the classification in many cases even directly after the detection of the heartbeat should be possible without additional delay.
  • a division of the heartbeats in two or more heartbeat classes is made. The method comprises at least the steps
  • the QRS sequence is then ⁇ fied classic means of a decision tree.
  • the decision tree is based on a plurality of features of the single beat. These features are included alone and / or in combination in the application of the decision tree.
  • bung also features the surrounding environ- of the single shock have, for example, the current or prior ⁇ subsequent blow type, the current or prior blow correlation reference values, such as duration or amplitude of the current or antecedent QRS Sequence or other specific values of the current, subsequent or previous QRS sequence.
  • a newly detected heartbeat can thus be assigned to an existing QRS template if the correlation threshold is exceeded.
  • the QRS templates are a limited collection of beat morphologies.
  • the most important QRS template is the so-called predominant normal ⁇ strike, referred to herein as a reference template. All heart batches that can be assigned to this reference template are automatically also standard beats.
  • they can only receive a classification if the individual strokes assigned to them have been largely classified, for example, as supraventricular (' ⁇ ') or ventricular ('V').
  • the QRS templates can be created first and then because of them zugeord ⁇ Neten heartbeats continuously updated after an initial learning phase, for example, 10s.
  • the classification means of an additional decision ⁇ tree makes it possible to classify a single impact with correspondingly gewünsch ⁇ ter accuracy and make a very fast classification in clear cases.
  • the distinctive features of the QRS sequence it is possible to perform a classification based on different characteristics.
  • the decision tree may be based on a set of typically 20 features of the current heartbeat, the reference template and their combination.
  • the features may be included as potential features both alone and in combination of two or more features. Higher-significance features can be used in the decision tree with a higher weighting. This allows a quick classification of a single beat.
  • a two-stage structure of the classification as described here makes it possible, especially after an initial learning phase, to classify many heartbeats in a first step. This is eg for most normal hits and monomorphic ventricular extrasystoles the case. If necessary, the decision tree is used in a second step, which makes a robust final classification with a small delay of a single heartbeat.
  • the QRS sequence is preferably subdivided into further subclasses by means of the decision tree and / or additional logic such as, for example, a neural network, a fuzzy logic or K-means clustering.
  • the classification of the QRS sequence is forwarded to an output device for display.
  • the output can be displayed in ⁇ example in the form of a label or by coloring an ECG waveform on a screen.
  • the properties of the classified single beat are used to update the existing QRS template in the class corresponding to the classification. It is also possible to make ⁇ on the basis of a new classification QRS template to it.
  • QRS template can thus further ver ⁇ be refined. This is advantageous since thus patientenspezifi ⁇ specific templates can be generated that allow individual monitoring.
  • the creation of new templates also makes it possible to make specific and individual classifications.
  • a further aspect of the present invention relates to a device for automatic classification of heartbeats in the electrocardiogram and in particular of supraventricular and ventricular heartbeats, in particular for a method as described herein.
  • the apparatus comprises a Rechenano ⁇ rd Vietnamese for comparing a heart beat with at least one existing QRS template at least one heartbeat class and for determining at least one correlation value between the QRS template and the QRS sequence of the heartbeat.
  • the Vorrich ⁇ tung comprises a classification element for allocating the QRS sequence to a class.
  • the device also includes a computing arrangement for classifying the QSR sequence
  • the decision tree is based on a plurality of features of the single beat, these features alone
  • a device as described herein makes it possible to carry out a classification both in the immediate vicinity of the patient and a remote monitoring and classification, for example in the monitoring center of a hospital, and to carry out the classification quickly and reliably.
  • the device can be designed to subdivide the QRS sequence into subclasses.
  • the division into subclasses can be carried out in a common arithmetic unit or in an additional arithmetic unit.
  • the device comprises a display unit and more preferably a screen for outputting or displaying the heartbeat class to the user in the form of a label or by means of a coloring of an ECG curve.
  • the device is designed for the classification of a heart rhythm containing several QRS sequences. It is also possible to perform the classification ⁇ fication in the common processing unit or in a separate computing unit.
  • Another aspect of the invention relates to an ECG device for carrying out a method as described herein.
  • the ECG device preferably comprises a device as described herein.
  • an ECG device may be provided which allows the classification of heartbeats immediately upon acquisition of the signal. An additional device or external evaluation is not necessary.
  • the ECG device can be integrated into a patien ⁇ tenmonitor.
  • the computer program product comprises Softwareab ⁇ sections for determining a correlation threshold between ei ⁇ ner QRS sequence of a single beat and a QRS existing template to compare the QRS-QRS sequence with the template. Falling below the correlation threshold causes computer readable program means of a computer to process the current heartbeat further in a decision tree.
  • the general inventive system 2 shows an exemplary selection of features of a decision tree
  • FIG. 3 shows a visualization of the decision tree on FIG. 2
  • the signal of a heartbeat is typically generated via a con ventional ⁇ ECG device. Electrodes are placed on the surface of a patient and heart waves, which initiate the heartbeat in the heart, are measured and signals are generated. These signals can be processed further.
  • a circuit according to the invention for evaluating an ECG signal according to FIG. 1 consists of a QRS template matcher 1, a first classifier 2, a decision tree evaluator 3 and a second classifier 4. These components can be designed as one or more arithmetic units or as a common arithmetic unit be.
  • the QRS Template Matcher 1 ver ⁇ tries to assign a newly detected heartbeat to an existing QRS template.
  • the maximum cross-correlation coefficients between the new heartbeat and all existing QRS templates are calculated.
  • the new heart beat is included in this QRS template. Otherwise, the new heartbeat can not be assigned to best ⁇ Henden QRS template. In this case, a new QRS template can be created, provided the maximum number of possible QRS templates has not yet been reached.
  • the QRS Template Matcher 1 uses several cross correlators to determine the best matching QRS template.
  • the cross-correlation calculation is not done directly on the individual ECG leads, but at the absolute spatial velocity (ASV).
  • ASV absolute spatial velocity
  • the approximated vector cardiogram is calculated from the electrocardiogram in a first step, ie the components orthogonal as possible x, y, z.
  • the ASV arises then as the amount of 3-dimensional vectorcardiogram Gradi ⁇ ducks in, for example, according to the following formulas.
  • ASV - ⁇ x 2 + Ay 2 + Az 2
  • the cross-correlation between a sequence and a QRS-QRS template typically takes place in a window of 180ms, be ⁇ ginnend 50ms before the first QRS wave and ending 130 ms after the first QRS wave of a single beat and the respective Templa ⁇ tes.
  • the time windows are shifted to the left and 22ms to the right in small increments up to a maximum of 22 ms to find the correlation maximum.
  • the mean values of the windowed signals are each removed in a manner known per se, the cross-correlation is calculated and normalized with the root of the product of the power of the two signals involved.
  • the cross-correlation coefficient is between -1 and +1. Are only required values from the po ⁇ sitiven range between 0 and 1, that is between 0% and 100%. Negative values are set to zero.
  • c is the cross ⁇ correlation coefficient and a and b are the two signals to be compared with window length N.
  • the maximum found cross correlation coefficient is decisive for the selection of the best matching QRS template. A however, definitive assignment only takes place if the maximum cross-correlation coefficient exceeds the correlation threshold .
  • the correlation threshold is either fixed, eg 96%, or variable.
  • the permissible value range of the correlation threshold is preferably between 80% and 98%.
  • QRS template supplied assigns ⁇
  • the question QRS template is updated with the new single shock. This is preferably done with the method of incremental averaging, ie, applying a fixed small increment in the direction of the new single beat. This is a robust, non-linear process that is largely immune to single-art artefacts.
  • a new QRS template can be created, provided that the maximum number of possible QRS templates has not yet been reached.
  • the maximum number of QRS templates is limited to a number of 8 in order to keep the computational effort within reasonable limits. It is also conceivable a method where QRS templates can be deleted or overwritten after a certain unused time. A completely new learning phase can be initiated manually at any time or automatically, eg after signal loss. Then all existing QRS templates are deleted and at the end of Lernpha ⁇ se 10s a new reference template and any other QRS generated templates.
  • Template gets a classification if it has been classified in the previous time, e.g. the last two minutes, associated large majority heartbeats, e.g. could be assigned a strike class with a 2/3 majority.
  • the templates can thus be adapted to the patient.
  • the first classifier 2 classifies a new heartbeat, for example, in a specific embodiment described below as supraventricular (' ⁇ '), ventricular ('V') or questionable ('Q').
  • the decision 'N' is made if the new heartbeat can be assigned to the reference template or another QRS template of class 'N'.
  • the decision 'V drops when the new heartbeat is a QRS template of the class
  • a QRS template has the Class ' ⁇ ' if it were classified with more than 2/3 majority than 'N' either to the reference template han ⁇ punched or when associated with at least 3 single strokes in the last two minutes and this.
  • a QRS template has the class 'V when in the last two minutes at least three single strokes zugeord ⁇ net and this with more than 2/3 majority as' classified V.
  • a QRS template does not have a class if less than 3 single strikes have been assigned to a single class within the last two minutes, or if no clear majority for 'N' or 'V is apparent.
  • the decision tree evaluator 3 operates on a set of typically 20 features of the current heartbeat, the reference template and their combination. If the current heartbeat could be assigned to a QRS template, then instead of the measured variables of the current heartbeat, alternatively those of the assigned QRS template can also be used to classify the template. These are in many cases more robust and the classification results become even more stable and better.
  • Decision tree evaluator 3 employs a decision tree based on the typically selected 20 features. There are in addition to the basic features of all possible combinations of these characteristics as potential features with einbezo ⁇ gen. Features higher significance can be used in the decision tree ⁇ with a higher weighting.
  • the decision tree evaluator 3 includes a decision tree based on a set of 20 features according to the following table.
  • the features can essentially be divided into the three categories “discrete”, “continuous” and “derived.”
  • the discrete features are ternary or binary state variables. "The 12 continuous features represent continuous measures.”
  • the 3 derived features result from pairwise subtraction of two continuous characteristics.
  • the learning phase is used to accommodate different QRS sequences with essentially similar sequences are a template zugeord ⁇ net.
  • the learning phase can extend over the entire measurement period, with the assignments and decisions becoming more and more accurate as the amount of data and the signals measured increases.
  • no classes are defined and thus all QRS sequences of the individual ⁇ strikes are classified with the decision tree.
  • the decision tree is preferably such that it can divide the individual slugs into at least supraventricular beats 'N' and ventricular beats 'V'. A further division and in consequence a further end branching of the tree is thus possible.
  • curpwave ⁇ , ⁇ refpwave describe whether the current heart beat or the reference template, a P-wave ha ⁇ ben or not.
  • the learning phase is refpwave larra 1 initia ⁇ .
  • corr ⁇ 100 ⁇ corrcoef (current strike, reference template)
  • prevcorr 100 ⁇ corrcoef (previous strike, reference template)
  • nextcorr 100 ⁇ corrcoef (following beat, reference template)
  • curqrsdur ⁇ and, refqrsdur ⁇ correspond to the QRS duration of the current heartbeat or reference template.
  • refqrsdur is initialized to 100ms.
  • curqrsdur curqrsojf - curqrson (current strike)
  • refqrsdur refqrsoff - refqrson (reference template)
  • refqrsdur refqrsoff - refqrson (reference template)
  • curqrsact ⁇ and denote QRS activity from the current heartbeat or reference template, respectively, according to the formulas below.
  • the number N corresponds to 180ms.
  • the scaling factor S is typically set to 4. Is currency ⁇ rend the learning phase, refqrsact ⁇ 100%
  • curqrsmob ⁇ and, refqrsmob ⁇ refer to QRS mobility from the current heartbeat or from the reference template according to the following formulas.
  • the number N also corresponds to 180ms.
  • the scaling factor S is set to 4.
  • refqrsmob x is set to 100%
  • relrr ⁇ , nextrr ⁇ , relnnv ⁇ describe the temporal relationships. These are the current / next relative RR interval and the relative NN variability. In NN variability, only the distances between normal beats are considered. During the learning phase, relrr and nextrr are at 100% and relnnv at 6%
  • Figures 2 and 3 show a possible decision tree.
  • Starting at node Nl completes it for a specific QRS sequence.
  • the decision tree was automatically optimized by training on standard databases. In addition to the basic features, all products of two characteristics were also provided as possible combined features. In the
  • optimization of the decision tree is automatically chosen in an optimal manner which basic characteristics and which com ⁇ bined features in the various decision node in the tree will eventually be used.
  • the decision tree When using the decision tree, only those combined features have to be calculated that actually have to be compared with a decision threshold when going through the tree. It is overall a very efficient and effective CLASSIFICA ⁇ approximate method.
  • a professional software can be used, for example Matlab.
  • the classification results eg in the form of sensitivity and positive predictivity, are also checked on other annotated test databases.
  • the preferred embodiment operates on a decision tree with 150 decision nodes .
  • the method or device is programmed with a static thus determined decision tree so that during use the patien ⁇ th no further optimization of the decision tree takes place.
  • the second classifier 4 makes the final classification based on the results from the decision tree.
  • the classifi cation ⁇ typically occurs with the delay of a heartbeat, as some of the required features need the features or the distance to the next heartbeat.
  • the second classifier 4 classifies each heartbeat entwe ⁇ example, as supraventricular (' ⁇ ') or as a ventricular ('V') based on the result of the previous block.
  • the circuit or method of the present invention may subclass the beat classes, such as the supraventricular or ventricular beat class, in a downstream additional logic.
  • the supraventricular beatings can be subdivided into normal beats, supra ⁇ ventricular extrasystoles, and junctional beats. Such a classification is possible for example by means of another decision tree. Or in ventricular beatings, ventricular extrasystoles and fusion beats can be distinguished.
  • the preferred embodiment can be easily programmed on a commercial processor or implemented in an integrated circuit. All variables must be suitably quantized and the operations optimized to the existing architecture blocks. Depending on the target system, there are optimized procedures for this. But these are not opposite ⁇ standing of the present invention.

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Abstract

L'invention concerne un procédé et un dispositif pour la classification automatique de battements de cœur. Une répartition en deux ou plusieurs classes de battements de cœur est effectuée. Dans une première étape, la séquence QRS d'un battement unique est comparée avec le modèle QRS d'au moins une classe de battements de cœur. Lors du dépassement d'un seuil de corrélation, la séquence QRS est associée à cette première classe de battements de cœur. Lorsqu'on tombe en dessous du seuil de corrélation, la séquence QRS est classifiée au moyen d'un arbre de décision. L'arbre de décision est basé sur des caractéristiques du battement unique, ces caractéristiques sont incluses, isolément et/ou en combinaison, lors de l'utilisation de l'arbre de décision.
PCT/EP2014/068534 2014-09-01 2014-09-01 Procédé et dispositif pour la classification automatique de battements de cœur, produit-programme d'ordinateur et appareil ecg pour la mise en œuvre du procédé WO2016034203A1 (fr)

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Cited By (4)

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CN107766869A (zh) * 2016-08-22 2018-03-06 富士通株式会社 对象分类方法和对象分类设备
CN109303559A (zh) * 2018-11-01 2019-02-05 杭州质子科技有限公司 一种基于梯度提升决策树的动态心电图心拍分类方法
CN114224353A (zh) * 2022-02-21 2022-03-25 深圳泰和智能医疗科技有限公司 一种基于体温监测仪的心电检测分类方法
CN116350199A (zh) * 2023-05-31 2023-06-30 合肥心之声健康科技有限公司 一种动态心电图心搏模板生成方法及系统

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CN109303559B (zh) * 2018-11-01 2021-05-14 杭州质子科技有限公司 一种基于梯度提升决策树的动态心电图心拍分类方法
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