EP3946003A1 - Procédé pour la classification d'un enregistrement de polysomnographie dans des stades de sommeil définis - Google Patents

Procédé pour la classification d'un enregistrement de polysomnographie dans des stades de sommeil définis

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Publication number
EP3946003A1
EP3946003A1 EP20711544.5A EP20711544A EP3946003A1 EP 3946003 A1 EP3946003 A1 EP 3946003A1 EP 20711544 A EP20711544 A EP 20711544A EP 3946003 A1 EP3946003 A1 EP 3946003A1
Authority
EP
European Patent Office
Prior art keywords
data
type
training
sleep
electroencephalography
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP20711544.5A
Other languages
German (de)
English (en)
Inventor
Muthuraman MUTHURAMAN
Haralampos GOUVERIS
Philipp Tjarko BOEKSTEGERS
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Johannes Gutenberg Universitaet Mainz
Universitaetsmedizin der Johannes Gutenberg-Universitaet Mainz
Original Assignee
Johannes Gutenberg Universitaet Mainz
Universitaetsmedizin der Johannes Gutenberg-Universitaet Mainz
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Application filed by Johannes Gutenberg Universitaet Mainz, Universitaetsmedizin der Johannes Gutenberg-Universitaet Mainz filed Critical Johannes Gutenberg Universitaet Mainz
Publication of EP3946003A1 publication Critical patent/EP3946003A1/fr
Pending legal-status Critical Current

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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
    • A61B5/48—Other medical applications
    • A61B5/4806—Sleep evaluation
    • A61B5/4812—Detecting sleep stages or cycles
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
    • A61B5/02405—Determining heart rate variability
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
    • A61B5/0245—Detecting, measuring or recording pulse rate or heart rate by using sensing means generating electric signals, i.e. ECG signals
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
    • A61B5/087—Measuring breath flow
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording 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, mobility of a limb
    • A61B5/1113—Local tracking of patients, e.g. in a hospital or private home
    • A61B5/1114—Tracking parts of the body
    • 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
    • A61B5/369—Electroencephalography [EEG]
    • A61B5/372—Analysis of electroencephalograms
    • A61B5/374—Detecting the frequency distribution of signals, e.g. detecting delta, theta, alpha, beta or gamma waves
    • 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
    • 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
    • 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
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

Definitions

  • the present invention relates to a method for classifying sleep stages on the basis of a polysomnography recording.
  • the present invention relates to a
  • insomnia There are a large number of people who suffer from insomnia. The sleep disorders are sometimes very different and can therefore have a variety of different causes.
  • polysomnography records can provide clues as to the causes of the sleep disturbance.
  • a polysomnography involves a variety of
  • Body function data of a patient recorded during sleep.
  • the heart activity and the breathing intensity and frequency during sleep causes of
  • EEG electroencephalography
  • EKG electrocardiography
  • the polysomnography is carried out in a specially equipped sleep laboratory.
  • the sleep is divided into five different stages, namely the stage N1, the stage N2 and the stage N3 (as part of the non-REM sleep), the REM stage and the waking stage, which corresponds to the epochs or the period during of sleep when the person is awake.
  • the physical activity or the physical function data differ in these stages.
  • the brain waves which are recorded by means of electroencephalography (EEG)
  • EEG electroencephalography
  • both the frequency and the intensity of the brain waves differ.
  • Heart activity, especially heart rate also changes from one sleep stage to another.
  • the stages of sleep run in a more or less regular pattern. In patients with insomnia, this pattern may differ from that of a healthy person.
  • various bodily functions depending on the absolute or percentage sleep stage division during sleep, differ from those of a healthy person.
  • a polysomnography scan usually takes seven to eight hours, as this is how long a person usually sleeps. Since some sleep disorders can only last a few seconds, the data are displayed at very short intervals, i.e. almost continuously, recorded.
  • epochs Specialist around one to two hours, during which sleep is divided into 30-second units, so-called epochs, with each epoch being assigned to a sleep stage. Furthermore, the quality of the classification depends on the experience of the specialist.
  • the object is achieved by a method for classifying a
  • the sleep of a person is divided into different sleep stages, the sleep stages being identifiable on the basis of at least one data type of the first type.
  • a large number of items of information on body functions are then recorded over a predetermined period of time in the form of data, the data comprising at least one data record of the data type of the first type.
  • the recorded data is divided into time-dependent data blocks. This can be done manually, i.e. by a person, or automatically by a computer or the like.
  • a limited number of training data blocks are then manually selected from the data blocks and assigned sleep stages, the training data blocks being selected such that the data contained in the training block can each be clearly assigned to a defined sleep stage. This selection is preferably made by a trained person or a specialist.
  • Each data record of the first type of each training data block is evaluated by means of a data processing method.
  • Training objects are created from the evaluated data of each training data block, each training object comprising the data sets of the first type of training data block evaluated by means of the data processing method and the assignment of the training data block to a sleep stage.
  • the training objects are then transmitted to a support vector machine for creating a classification in the support vector machine. Thereafter, at least some of the data blocks, preferably all data blocks that are not as
  • Training data blocks have been selected, transmitted to the support vector machine, and these data blocks are automatically divided into the known sleep stages based on the data of the data type of the first type of each data block.
  • Training data blocks by means of a data processing method are understood in the context of the invention to include processes such as the processing and / or analysis of data records. With the aid of the method described, it is possible to automatically carry out most of the classification of a polysomnography recording in sleep stages.
  • the classification can thus be carried out much more cost-effectively than before.
  • the data set of the first type has data for the following body functions: brain waves, cardiac activity, air flow of breathing, breathing noises, in particular snoring noises, eye movement patterns, electrical muscle activity in the chin area and on the Lower leg (tibialis anterior muscle).
  • At least one of the following measuring methods or measuring devices is used to determine the data record of the first type: electroencephalography, electrocardiography, microphone, air flow meter.
  • the invention is based on the knowledge that the brain waves generated by the
  • Electroencephalography are measured, allow conclusions to be drawn particularly well about the sleep stage present in a data block.
  • Electroencephalograms are comparatively easy to determine and, due to their symmetrical arrangement on a person's head, also enable the measurement results to be compared with one another. Therefore, according to a preferred embodiment of the method described, the data record of the first type of data is provided
  • electroencephalography in particular C3 / C4 data of an electroencephalography.
  • Sleep stages can be achieved: cross-frequency coupling method, entropy method,
  • Electroencephalogram recorded data result from a superposition of several oscillating signals.
  • the electroencephalogram thus records various
  • the step of evaluating the data record of the first type of each training data block by means of a data processing method thus includes a cross-frequency coupling method that includes a phase-to-amplitude method. With this phase-to-amplitude method, the data of an electroencephalogram can be classified particularly accurately in sleep stages.
  • the recorded data are subdivided into a predefined time interval, the time interval in particular being in the range from 15 seconds to 5 minutes and, in particular with regard to electroencephalographic signals, preferably 30 seconds (so-called. 30-second epoch).
  • the support vector machine has an algorithm which uses a non-linear basic kernel function.
  • the data on the body functions can be recorded in a sleep laboratory, with the data on the body functions preferably being recorded on the second night in the sleep laboratory.
  • the data on the body functions can be recorded in the home environment.
  • a very high match rate or a very high hit rate in the classification of sleep stages according to the method described could be achieved if the data record of the data type of the first type consists of the data from an electroencephalography, in particular from C3 / C4 data from an electroencephalography, and if the Evaluation of the Data set of the first type of each training data block using a
  • Cross-frequency coupling method is carried out with a phase-to-amplitude method.
  • Classification of sleep stages can be achieved.
  • the data record of the data type of the first type consists of at least one of the following data types: data from an electroencephalography, in particular from C3 / C4 data from an electroencephalography, respiratory flow, snoring noises and the evaluation of the data record of the first type of each training data block using an entropy process takes place.
  • the data record of the data type of the first type consists of the data from an electrocardiography and the data processing method comprises a method for establishing the heart rate variability.
  • FIG. 1 shows a schematic representation of the sequence of a method for classifying a polysomnography recording into defined sleep stages on the basis of
  • Fig. 2 shows an overview of the usable data of the first type and corresponding
  • FIG. 3 shows a schematic representation of the sequence of a method for classifying a polysomnography recording into defined sleep stages on the basis of
  • FIG. 4 shows a schematic illustration of the sequence of a method for classifying a polysomnography recording into defined sleep stages on the basis of
  • Electroencephalography (EEG) data associated with an entropy procedure 5 shows a schematic representation of the sequence of a method for classifying a
  • FIG. 1 shows a schematic representation of the sequence of a method for classifying a polysomnography recording into defined sleep stages on the basis of electroencephalography (EEG) data in connection with a coupling frequency method.
  • EEG electroencephalography
  • the first step is to divide a person's sleep into different sleep stages.
  • sleep is divided into the five known stages, namely stage N1, stage N2, stage N3, REM stage and waking stage.
  • Each of these known stages can be identified using at least one data type of the first type.
  • a large amount of information about body functions about the duration of sleep of a person is recorded in the form of a known polysomnography recording in a sleep laboratory.
  • a polysomnography scan usually takes seven to eight hours.
  • the recorded data is divided into time-dependent data blocks with a duration of 30 seconds. This can be done manually, i.e. by a person, or automatically by a computer or the like.
  • a trained person or a specialist selects a limited number of training data blocks from the data blocks and assigns these selected training data blocks each to a sleep stage, the person or the specialist selecting the training data blocks in such a way that the data contained in the training block are each clearly assigned to a defined sleep stage can be.
  • the person or professional selects the same number of training data blocks for each sleep stage. It has been shown that the selection of four training data blocks per sleep stage is sufficient. It understands However, that in the context of the described method also more or less
  • Training data blocks can be selected.
  • the polysomnography recording and thus the data blocks contain, among other things, the brain waves recorded by means of electroencephalography.
  • the brain waves were recorded in different parts of the brain.
  • the data that were acquired at positions C3 and C4 on the head of a patient by means of electroencephalography are used (see FIG. 1 in FIG. 1).
  • Positions C3, C4 are the positions that are usually referred to as C3, C4 in electroencephalography.
  • the data of each training data block obtained at the C3 / C4 positions of an electroencephalography are evaluated by means of a data processing method.
  • each sleep stage is characterized by the presence or the intensity or amplitude of different known frequency groups.
  • the data that the electroencephalogram shows at one position in the brain are thus a superposition of various signals that the brain sends out in the form of brain waves.
  • a simple frequency analysis of the recorded data for example in the form of an (almost) Fourier transformation, does not provide any frequency sequences that can be clearly assigned to a sleep stage due to the superimposed signals.
  • the data obtained at the C3 / C4 positions of the electroencephalography are processed by means of a cross-frequency coupling method (see Fig. 2 in Fig. 1). It has surprisingly been found here that a cross-frequency coupling method with a phase-to-amplitude method is particularly suitable for converting the data of a
  • phase-to-amplitude method the dependency between the amplitude of a higher-frequency signal and the phase of a low-frequency signal is shown.
  • the characteristic course of the frequency groups processed by means of the phase-to-amplitude method can be clearly assigned to a sleep stage.
  • the data of a data block obtained with the aid of the cross-frequency coupling method, in particular with the aid of the phase-to-amplitude method, are compared with that of a
  • a person skilled in the art correlates a certain sleep stage and thus forms a training object.
  • the training objects obtained from the selected data blocks are transmitted to a support vector machine to create a classification in the support vector machine (see Fig. 3 in Fig. 1).
  • An algorithm contained in the Support Vector Machine marks each data element as a point in an n-dimensional space, where n represents the number of features.
  • the algorithm must calculate the best mean value from these between different separating straight lines in order to determine the best common separating plane for all points, i.e. in this case a line with the maximum possible distance to all data points.
  • the classification is carried out by determining the so-called optimal hyperplane.
  • the algorithm looks for the hyperplane on which the data points are located with the smallest distance to the optimal hyperplane, the so-called support vectors. This distance is called margin.
  • the optimal separating hyperplane now maximizes the margin in order to obtain clearly separated classification groups.
  • the Support Vector Machine thus divides the training data blocks into the specified sleep stages.
  • the remaining part of the data blocks that were not selected as training data blocks are then transmitted to the support vector machine and these data blocks are automatically divided into the known sleep stages based on the C3 / C4 data
  • a particularly precise classification of the data blocks not selected as training data blocks is achieved in that a non-linear basic kernel function is used in the algorithm of the support vector machine.
  • FIG. 2 shows an overview of the data of the first type which can be used in the context of the present method and which are suitable for performing a classification, and corresponding data processing methods for evaluating the data of the first type.
  • the data on the respiratory flow, the snoring and the data from an electrocardiography are also suitable as data of the first type for carrying out the method described if suitable data processing methods are used.
  • the data of the first type can also be evaluated by means of a power spectral analysis or an entropy method.
  • the frequency-related power of a signal is specified in a frequency band.
  • the power spectral analysis is suitable for example for data from an electroencephalography (see Fig. 3).
  • the multi-tap process is particularly suitable for this.
  • Frequency domain such a time-frequency representation.
  • the entropy method is a non-linear dynamic analysis.
  • the main principle of the entropy method is the quantification of information about a signal as well as the probability of the occurrence of certain patterns within a finite number of patterns and within a time series of the signal. The more information that is conveyed within a signal, the higher the entropy of the signal.
  • the sample entropy method which is a modification of the approximate entropy method, is particularly suitable for sleep stages.
  • the approximate entropy method depends on the data record length. To avoid the results being dependent on the data record length, a Entropy method used, in which self-matching sequences are not counted and which works regardless of the data record length.
  • This entropy method is the aforementioned sample entropy method, which is a modification of the approximate entropy method.
  • the sample entropy method also has the advantage that it can be carried out more quickly.
  • the sample entropy method can, as shown in FIG. 4, particularly advantageously in
  • a data processing method that determines the heart rate variability from the recorded data is also suitable (see FIG. 5).
  • the distance between two heartbeats is usually defined as the time between the start of two contractions of the heart chambers. This beginning of the ventricular contraction is shown in the electrocardiogram as an R-wave, the distance between two R-waves being called the RR interval.
  • the RR intervals are not of the same length, but are subject to fluctuations. The quantification of these fluctuations is known as heart rate variability (HRV).
  • HRV heart rate variability
  • the data blocks not selected as training data blocks can be classified into sleep stages using the support vector machine based on the heart rate variability.
  • Hit rate achieved Except for the procedure in which the snoring noises were used as data of the first type, the hit rates were generally over 50%, sometimes well over 50%.
  • the heart rate variability is also suitable for the classification of sleep stages using the method described. Here, too, the hit rates are over 50%.

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Abstract

L'invention concerne un procédé pour la classification d'un enregistrement de polysomnographie dans des stades de sommeil définis, le procédé comprenant essentiellement les étapes suivantes : · division du sommeil d'une personne en différents stades de sommeil identifiables, · détection d'une pluralité d'informations concernant les fonctions corporelles pendant une période de temps prédéfinie sous forme de données, les données comprenant au moins un ensemble de données d'un type de données d'un premier genre, au moyen desquelles les stades de sommeil peuvent être identifiés; ·subdivision des données détectées en blocs de données dépendant du temps; sélection manuelle d'un nombre limité de blocs de données d'apprentissage à partir des blocs de données et attribution à un stade de sommeil, · évaluation de l'ensemble de données de premier genre de chaque bloc de données d'apprentissage au moyen d'un procédé de préparation de données; · génération d'objets d'apprentissage, chaque objet d'apprentissage comprenant les ensembles de données de premier genre évalués d'un bloc de données d'apprentissage et l'attribution du bloc de données d'apprentissage à un stade de sommeil; · transmission des objets d'apprentissage à une machine à vecteurs de support pour la génération d'une classification; · transmission d'au moins une partie des blocs de données, qui n'ont pas été sélectionnés comme blocs de données d'apprentissage, à la machine à vecteurs de support.
EP20711544.5A 2019-03-26 2020-03-10 Procédé pour la classification d'un enregistrement de polysomnographie dans des stades de sommeil définis Pending EP3946003A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102019107666.8A DE102019107666A1 (de) 2019-03-26 2019-03-26 Verfahren zur Klassifizierung einer Polysomnographie-Aufnahme in definierte Schlafstadien
PCT/EP2020/056326 WO2020193116A1 (fr) 2019-03-26 2020-03-10 Procédé pour la classification d'un enregistrement de polysomnographie dans des stades de sommeil définis

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US (1) US20220183619A1 (fr)
EP (1) EP3946003A1 (fr)
JP (1) JP2022526516A (fr)
CA (1) CA3134205A1 (fr)
DE (1) DE102019107666A1 (fr)
WO (1) WO2020193116A1 (fr)

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DE102020125743A1 (de) * 2020-10-01 2022-04-07 Universitätsmedizin der Johannes Gutenberg-Universität Mainz, Körperschaft des öffentlichen Rechts Verfahren zur Klassifizierung einer Polysomnographie-Aufnahme in definierte Schlafstadien

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JP4731031B2 (ja) * 2001-03-30 2011-07-20 株式会社デンソー 睡眠解析装置及びプログラム並びに記録媒体
WO2005002313A2 (fr) * 2003-07-01 2005-01-13 Cardiomag Imaging, Inc. (Cmi) Utilisation de l'apprentissage machine pour la classification de magnetocardiogrammes
EP2858558A1 (fr) * 2012-06-12 2015-04-15 Technical University of Denmark Système d'aide et procédé de détection d'un trouble neurodégénératif
US9655559B2 (en) * 2014-01-03 2017-05-23 Vital Connect, Inc. Automated sleep staging using wearable sensors
RU2704787C1 (ru) * 2016-06-27 2019-10-30 Конинклейке Филипс Н.В. Система и способ определения для определения стадии сна субъекта
KR102063925B1 (ko) * 2016-09-23 2020-01-08 기초과학연구원 뇌 자극 장치
EP3654839A4 (fr) * 2017-07-17 2021-09-01 SRI International Optimisation d'activité d'ondes lentes basée sur des oscillations de système nerveux périphérique dominantes
CN107495962B (zh) * 2017-09-18 2020-05-05 北京大学 一种单导联脑电的睡眠自动分期方法

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WO2020193116A1 (fr) 2020-10-01
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US20220183619A1 (en) 2022-06-16
JP2022526516A (ja) 2022-05-25

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