Method, device, equipment and storage medium for processing data

Abstract

The present application discloses a method, device, equipment and storage medium for processing data, and relates to the fields of data processing and deep learning technology. The specific implementation scheme is: obtaining the user's electrocardiogram signal collected by at least two lead electrodes; extracting the features of the electrocardiogram signal of each lead respectively to obtain the feature map of each lead; fusing the feature maps of each lead to obtain the fused feature map. This implementation method can accurately extract the feature information of the electrocardiogram signal for subsequent processing.

Description

Technical Field

[0001] The present application relates to the field of computer technology, specifically to the field of data processing and deep learning technology, and in particular to methods, devices, equipment and storage media for processing data. Background Art

[0002] The (surface) electrocardiogram is the reflection of the heart's electrical activity on the body surface. Myocardial cells perform depolarization-repolarization actions in a certain spatial and temporal sequence, which brings about periodic changes in the distribution of surface potential. Electrodes placed at two different parts of the body record the periodic changes in the potential difference between the two parts. Each pair of such electrodes is called a lead.

[0003] Because the heart is a three-dimensional entity, the direction of its electrical conduction is three-dimensional, and the electrocardiogram recorded by a certain lead is only the projection of the heart's electrical activity on a certain plane in three-dimensional space. Sometimes, multiple leads must be used to observe from different sides to draw conclusions. Summary of the invention

[0004] Provided are a method, apparatus, device and storage medium for processing data.

[0005] According to a first aspect, a method for processing data is provided, comprising: acquiring an electrocardiogram signal of a user collected by at least two lead electrodes; performing feature extraction on the electrocardiogram signal of each lead to obtain a feature map of each lead; and fusing the feature maps of each lead to obtain a fused feature map.

[0006] According to a second aspect, a device for processing data is provided, comprising: a signal acquisition unit, configured to acquire an electrocardiogram signal of a user collected by at least two lead electrodes; a feature extraction unit, configured to perform feature extraction on the electrocardiogram signal of each lead respectively to obtain a feature map of each lead; and a feature fusion unit, configured to fuse the feature maps of each lead to obtain a fused feature map.

[0007] According to a third aspect, an electronic device for processing data is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the at least one processor is executed by the at least one processor so that the at least one processor can execute the method described in the first aspect.

[0008] According to a fourth aspect, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause a computer to execute the method as described in the first aspect.

[0009] According to a fifth aspect, a computer program product comprises a computer program, and when the computer program is executed by a processor, the method described in the first aspect is implemented.

[0010] According to the technology of the present application, a method for processing ECG signal data is provided, which can accurately extract characteristic information of the ECG signal for subsequent processing.

[0011] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present application.

[0013] Figure 1 is an exemplary system architecture diagram in which an embodiment of the present application may be applied;

[0014] Figure 2 is a flow chart of an embodiment of a method for processing data according to the present application;

[0015] Figure 3 is a schematic diagram of an application scenario of the method for processing data according to the present application;

[0016] Figure 4 is a flow chart of another embodiment of a method for processing data according to the present application;

[0017] Figure 5 yes Figure 4 A schematic diagram of the structure of a convolutional neural network used in the illustrated embodiment;

[0018] Figure 6 is a structural schematic diagram of an embodiment of a device for processing data according to the present application;

[0019] Figure 7 It is a block diagram of an electronic device used to implement the method for processing data in an embodiment of the present application. DETAILED DESCRIPTION

[0020] The following is a description of exemplary embodiments of the present application in conjunction with the accompanying drawings, including various details of the embodiments of the present application to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0021] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0022] Figure 1 An exemplary system architecture 100 is shown to which embodiments of the method for processing data or the apparatus for processing data of the present application can be applied.

[0023] like Figure 1 As shown, the system architecture 100 may include ECG monitors 101, 102, 103, a network 104, and a server 105. The network 104 is used to provide a medium for communication links between the ECG monitors 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or optical fiber cables.

[0024] The ECG monitors 101, 102, and 103 can be sent to the server 105 for interaction via the network 104 to receive or send messages, etc. Specifically, the user can use the ECG monitors 101, 102, and 103 to collect ECG signals of human subjects, and send the collected ECG signals to the server 105 via the network 104, and can also receive the blood pressure information of the user predicted by the server 105. The ECG monitors 101, 102, and 103 can be various types of ECG monitors, such as ECG monitoring bracelets, ECG monitoring ties, or existing ECG monitors.

[0025] The server 105 may be a server that provides various services, such as a background server that processes the ECG signals collected by the ECG monitors 101, 102, and 103. The background server may extract features of the ECG signals, obtain blood pressure information based on the features, and feed the blood pressure information back to the ECG monitors 101, 102, and 103.

[0026] It should be noted that the server 105 can be hardware or software. When the server 105 is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or it can be implemented as a single server. When the server 105 is software, it can be implemented as multiple software or software modules (for example, for providing distributed services), or it can be implemented as a single software or software module. No specific limitation is made here.

[0027] It should be noted that the method for processing data provided in the embodiment of the present application can be executed by the ECG monitors 101, 102, 103, or by the server 105. Accordingly, the device for processing data can be set in the ECG monitors 101, 102, 103, or in the server 105.

[0028] It should be understood that Figure 1 The number of ECG monitors, networks and servers in the embodiment is only for illustration. Any number of ECG monitors, networks and servers may be provided as required.

[0029] Continue to refer Figure 2 , shows a process 200 of an embodiment of a method for processing data according to the present application. The method for processing data in this embodiment includes the following steps:

[0030] Step 201: Acquire the user's electrocardiogram signal collected by at least two lead electrodes.

[0031] In this embodiment, the execution subject of the method for outputting information can obtain the user's electrocardiogram signal collected by at least two lead electrodes in various ways. For example, the execution subject can obtain the electrocardiogram signal collected by a 12-lead electrocardiogram monitor. Each lead electrode can transmit the collected electrocardiogram signal to the execution subject for processing by the execution subject.

[0032] Step 202, extracting features from the ECG signals of each lead to obtain a feature graph of each lead.

[0033] After acquiring the ECG signals of each lead, the execution subject can extract features from the ECG signals of each lead to obtain feature maps of each lead. Here, the execution subject can use various feature extraction algorithms to extract features from the ECG signals of each lead, such as using the convolution layer of a convolutional neural network to extract features, or using the SIFT feature extraction algorithm to extract features. When extracting features from the ECG signals of each lead, the same feature extraction algorithm can be used, or different feature extraction algorithms can be used according to the characteristics of the ECG signals of each lead. The size of the feature maps of each lead can be the same.

[0034] Step 203, the feature maps of each lead are fused to obtain a fused feature map.

[0035] The execution subject may fuse the feature graphs of each lead to obtain a fused feature graph, thereby realizing feature extraction of the ECG signal of each lead. When fusing, the execution subject may add the feature graphs of each lead, or perform weighted fusion by combining weights.

[0036] Continue to see Figure 3 , which shows a schematic diagram of an application scenario of the method for processing data according to the present application. Figure 3In the application scenario, the user collects the user's 12-lead ECG signal through a 12-lead ECG monitor. Then, the ECG signal is sent to the server through the network. The server extracts the features of the ECG signal of each lead and obtains the feature map of each lead. Then, the feature map of each lead is fused to obtain a fused feature map. The ECG signal is sequenced based on the fused feature map.

[0037] The method for processing data provided by the above-mentioned embodiments of the present application can perform feature extraction on the ECG signals collected by each lead respectively, so that the unique features on one or several leads will not be masked by other leads that do not have these features but are in the majority in number because ordinary convolution jointly extracts all lead features from the beginning.

[0038] Continue to see Figure 4 , which shows a process 400 of another embodiment of a method for processing data according to the present application. Figure 4 As shown, the method of this embodiment may include the following steps:

[0039] Step 401: Acquire the user's electrocardiogram signal collected by at least two lead electrodes.

[0040] Step 402, performing at least one of the following preprocessing on the ECG signal of each lead: digital-to-analog conversion, filtering abnormal spike values, and normalization.

[0041] After acquiring the ECG signals of each lead, the execution subject may perform at least one of the following preprocessing on the ECG signals of each lead: digital-to-analog conversion, filtering abnormal spikes, and normalization. Digital-to-analog conversion (DAC) converts digital signals into analog signals (also called physical signals) in millivolts (mV). The execution subject may use a simple threshold filtering method to filter abnormal spikes. For example, the threshold is set to ±15mV, and the value of the point exceeding the threshold is reset to the value of the previous point within the threshold. The execution subject may randomly normalize the value of each lead of the ECG signal so that its mean value and standard deviation are within a certain range (for example, the mean value is between -0.05mV and 0.1mV, and the standard deviation is between 0.08mV and 0.32mV), thereby achieving normalization.

[0042] Step 403: Use at least one filter in a pre-trained convolutional neural network to extract features of the ECG signal of each lead to obtain a feature map of each lead.

[0043] In this embodiment, the execution subject can use a pre-trained convolutional neural network to extract the features of the ECG signal of each lead, thereby obtaining a feature map of each lead. The above convolutional neural network may include multiple convolutional layers, each convolutional layer includes multiple neurons, and the multiple neurons form a filter. Each filter can perform a convolution operation on the ECG signal of each lead, thereby obtaining a feature map of each lead.

[0044] Specifically, the executing entity can use Figure 4 The following steps are performed to realize feature extraction of ECG signals of each lead: inputting the ECG signals of each lead into a convolutional neural network through multiple channels; obtaining at least one feature subgraph corresponding to the ECG signals of each lead through at least one filter; and fusing the feature subgraphs to obtain a feature graph of each lead.

[0045] In this embodiment, the execution subject can input the ECG signal of each lead into the convolutional neural network through multiple channels. Assuming that the ECG signal has n (for example, 12) leads, and the number of input channels of the lead convolution layer is m×n, then the number of groups of the convolution operation is k×n, where k is an integer divisible by m. Then, the ECG signals of each lead are subjected to group convolution operation through at least one filter in the convolutional neural network, which is called lead-wise convolution, so as to obtain at least one feature subgraph corresponding to the ECG signal of each lead. The information in each feature subgraph comes only from the ECG signal of a single lead, achieving the purpose of extracting features from different leads in the same convolutional neural network at the same time. Due to the use of lead-wise convolution, the parameter amount of each filter is only 1 / n of the parameter amount of the filter without lead-wise convolution. If the convolution operation within each group after grouping is further changed to separable convolution, the parameter amount of the entire neural network can be further reduced.

[0046] The above convolutional neural network can be improved by ResNet. Figure 5 In the convolutional layers Conv(3,stride), Conv(3), Conv(1) involved in the basic module of ResNet, and Conv(1,stride=2) involved in the shortcut, the parameter of groups is set to the multiple of the number of leads of the ECG signal input to the entire neural network, and the number of output channels is adjusted accordingly. Figure 5Conv(3, stride) in the example refers to a convolutional layer with a kernel size of 3 and a stride of stride. Conv(3) and Conv(1) refer to convolutional layers with kernel sizes of 3 and 1, respectively, and a stride of 1. relu refers to the rectified linear unit (ReLU), which is defined as ReLU(x) = max(0, x).

[0047] After obtaining each feature sub-graph, the execution body can fuse them to obtain the feature graph of each lead. Among the feature graphs that need to be spliced, the feature graphs from the same lead are placed in different groups, and the splicing is performed within the group, and then the splicing between groups is performed. In this way, in the reorganized feature graph, the feature graphs from different leads will not be mixed together, and the next step of sub-lead convolution can be directly implemented using the grouped convolution method.

[0048] Step 404, fusion of the feature maps of each lead to obtain a fusion feature map.

[0049] Step 405: perform sequence labeling on the ECG signal according to the fused feature graph.

[0050] After obtaining the fused feature map, the execution subject can perform R peak detection and waveform segmentation on the ECG signal based on the feature information included in the fused feature map to achieve sequence labeling of the ECG signal.

[0051] The method for processing data provided by the above embodiment of the present application uses a method of lead-by-lead convolution to achieve the feature extraction effect of multiple models with one model. By using the lead-by-lead method, the unique features of one or several leads will not be masked by other leads that do not have these features but are in the majority in number because ordinary convolution extracts all lead features jointly from the beginning. Compared with ordinary convolution, the number of model parameters can be greatly reduced.

[0052] Further references Figure 6 As an implementation of the methods shown in the above figures, the present application provides an embodiment of a device for processing data, and the device embodiment is similar to Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0053] like Figure 6 As shown, the device 600 for processing data in this embodiment includes: a signal acquisition unit 601, a feature extraction unit 602 and a feature fusion unit 603.

[0054] The signal acquisition unit 601 is configured to acquire the user's electrocardiogram signal collected by at least two lead electrodes.

[0055] The feature extraction unit 602 is configured to extract features from the ECG signal of each lead respectively to obtain a feature graph of each lead.

[0056] The feature fusion unit 603 is configured to fuse the feature maps of each lead to obtain a fused feature map.

[0057] In some optional implementations of this embodiment, the feature extraction unit 602 may be further configured to: utilize at least one filter in a pre-trained convolutional neural network to perform feature extraction on the ECG signal of each lead respectively, to obtain a feature map of each lead.

[0058] In some optional implementations of the present embodiment, the feature extraction unit 602 may be further configured to: input the ECG signal of each lead into a convolutional neural network through multiple channels; obtain at least one feature subgraph corresponding to the ECG signal of each lead through at least one filter; and fuse the feature subgraphs to obtain a feature graph of each lead.

[0059] In some optional implementations of this embodiment, the apparatus 600 may further include: Figure 6 The sequence labeling unit not shown in the figure is configured to: perform sequence labeling on the ECG signal according to the fusion feature map.

[0060] In some optional implementations of this embodiment, the apparatus 600 may further include: Figure 6 The preprocessing unit not shown in the figure is configured to perform at least one of the following preprocessing on the ECG signal of each lead: digital-to-analog conversion, filtering abnormal spike value, and normalization.

[0061] It should be understood that the units 601 to 603 described in the apparatus 600 for processing data are respectively the same as those described in the reference Figure 2 Therefore, the operations and features described above for the method for processing data are also applicable to the device 600 and the units included therein, and will not be described in detail here.

[0062] According to an embodiment of the present application, the present application also provides an electronic device, a readable storage medium and a computer program product.

[0063] Figure 7A block diagram of an electronic device 700 for executing a method for processing data according to an embodiment of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0064] like Figure 7 As shown, the device 700 includes a processor 701, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 702 or a computer program loaded from a memory 708 into a random access memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the device 700 can also be stored. The processor 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An I / O interface (input / output interface) 705 is also connected to the bus 704.

[0065] A number of components in the device 700 are connected to the I / O interface 705, including: an input unit 707, such as a keyboard, a mouse, etc.; an output unit 707, such as various types of displays, speakers, etc.; a memory 708, such as a disk, an optical disk, etc.; and a communication unit 709, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 709 allows the device 700 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0066] The processor 701 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 701 performs the various methods and processes described above, such as methods for processing data. For example, in some embodiments, the method for processing data may be implemented as a computer software program, which is tangibly contained in a machine-readable storage medium, such as a memory 708. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 700 via ROM 702 and / or communication unit 709. When the computer program is loaded into RAM 703 and executed by the processor 701, one or more steps of the method for processing data described above may be performed. Alternatively, in other embodiments, the processor 701 may be configured to execute a method for processing data in any other appropriate manner (e.g., by means of firmware).

[0067] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0068] The program code for implementing the method of the present application can be written in any combination of one or more programming languages. The above program code can be packaged into a computer program product. These program codes or computer program products can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor 701, enables the functions / operations specified in the flow chart and / or the block diagram to be implemented. The program code can be executed completely on the machine, partially on the machine, partially on the machine as an independent software package and partially on a remote machine or completely on a remote machine or server.

[0069] In the context of the present application, a machine-readable storage medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable storage medium may be a machine-readable signal storage medium or a machine-readable storage medium. A machine-readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0070] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0071] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0072] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server may also be a server in a distributed system, or a server combined with a blockchain.

[0073] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this application can be executed in parallel, sequentially or in different orders, as long as the expected results of the technical solution of this application can be achieved, and this document is not limited here.

[0074] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A method for processing data, comprising: Acquire an electrocardiogram signal of a user collected by at least two lead electrodes; Extract features of the ECG signals of each lead respectively to obtain feature graphs of each lead; Fusing the feature maps of each lead to obtain a fused feature map; The feature extraction of each lead's electrocardiogram signal is performed to obtain a feature map of each lead, including: The ECG signals of each lead are input into a pre-trained convolutional neural network through multiple channels; Performing a group convolution operation on the ECG signals of each lead through at least one filter in the convolutional neural network to obtain at least one characteristic subgraph corresponding to the ECG signals of each lead; The characteristic subgraphs are merged to obtain the characteristic graphs of each lead, including: placing at least one characteristic subgraph corresponding to the electrocardiogram of the same lead into different groups, splicing the characteristic subgraphs in each group to obtain the spliced ​​characteristic graph corresponding to the group; splicing the spliced ​​characteristic graphs corresponding to different groups to obtain the characteristic graphs of each lead; Wherein, the method further comprises: According to the fusion feature graph, the electrocardiogram signal is sequence labeled, including: performing R peak detection and waveform segmentation on the electrocardiogram signal.

2. The method according to claim 1, wherein: The method further comprises: Perform at least one of the following preprocessing on the ECG signal of each lead: analog-to-digital conversion, filtering abnormal spike values, and normalization.

3. A device for processing data, comprising: A signal acquisition unit, configured to acquire an electrocardiogram signal of a user collected by at least two lead electrodes; The feature extraction unit is configured to extract features from the ECG signals of each lead to obtain a feature map of each lead; a feature fusion unit configured to fuse the feature maps of each lead to obtain a fused feature map; Wherein, the feature extraction unit is further configured to: The ECG signals of each lead are input into a pre-trained convolutional neural network through multiple channels; Performing a group convolution operation on the ECG signals of each lead through at least one filter in the convolutional neural network to obtain at least one characteristic subgraph corresponding to the ECG signals of each lead; The characteristic subgraphs are merged to obtain the characteristic graphs of each lead, including: placing at least one characteristic subgraph corresponding to the electrocardiogram of the same lead into different groups, splicing the characteristic subgraphs in each group to obtain the spliced ​​characteristic graph corresponding to the group; splicing the spliced ​​characteristic graphs corresponding to different groups to obtain the characteristic graphs of each lead; The device further includes a sequence labeling unit configured to: According to the fusion feature graph, the electrocardiogram signal is sequence labeled, including: performing R peak detection and waveform segmentation on the electrocardiogram signal.

4. The device according to claim 3, wherein: The device also includes a pre-processing unit configured to: Perform at least one of the following preprocessing on the ECG signal of each lead: digital-to-analog conversion, filtering abnormal spike values, and normalization.

5. An electronic device for processing data, comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 2.

6. A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 2.

7. A computer program product, comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 2.

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