Apparatus, method, and computer program for detecting cortical-cortical evoked potentials using transcranial magnetic stimulation

Abstract

The present disclosure relates to an apparatus, a method, and a computer program for detecting cortical-cortical evoked potentials (CCEP) using transcranial magnetic stimulation (TMS). In particular, the apparatus comprises: a communication module for communicating with an external device; a memory storing at least one process for performing CCEP detection using the TMS; and a processor for performing CCEP detection using the TMS according to the process. The processor can be configured to: calculate noise intervals by collecting electroencephalogram (EEG) signals induced by TMS; cluster EEG channels excluding the calculated noise intervals; perform clustering EEG analysis on the basis of representative EEG signals of each cluster; perform individual EEG analysis on the basis of individual EEG signals within each of the clusters; and then detect CCEP signals on the basis of the clustering EEG analysis results and the individual EEG analysis results.

Description

Device, method, and computer program for detecting cortical-cortical evoked potentials using transcranial magnetic stimulation

[0001] The present disclosure relates to an apparatus, method, and computer program for detecting cortical-cortical evoked potentials using transcranial magnetic stimulation.

[0002] Transcranial Magnetic Stimulation (TMS) is a non-invasive treatment method for the nervous system that involves stimulating the brain by passing a magnetic field generated from an electromagnetic coil through the surface of the head and into the skull to activate or inhibit nerve cells in specific areas of the brain. In other words, TMS utilizes changes in the magnetic field to apply electrical stimulation to the subject. This technique has the advantage of stimulating only a portion of the brain, allowing for the convenient treatment of neurological disorders without anesthesia, drugs, or invasive procedures.

[0003] Generally, transcranial magnetic stimulation has been performed by applying electrical stimulation to clinically or empirically known stimulation points, or by having the user determine the stimulation location while moving the device. Consequently, there were problems in that it was difficult to account for differences in the types of coils used or individual body structures, and it was difficult to directly verify the effects of the procedure.

[0004] Meanwhile, Corticocortical Evoked Potentials (CCEP) are a method that measures the electrical response elicited in one brain region when one region is stimulated, serving as an important tool for studying the brain's functional connectivity. Previously, these methods were primarily performed using invasive techniques, but this approach had limitations regarding patient safety and the scope of research subjects.

[0005] Although non-invasive cortical-cortical evoked potential research using transcranial magnetic stimulation has the potential to overcome these limitations, there was a technical problem in which the quality of electroencephalogram (EEG) signals was significantly degraded due to strong electromagnetic noise generated during stimulation by transcranial magnetic stimulation. More specifically, the EEG signals stimulated by transcranial magnetic stimulation had an extremely low signal-to-noise ratio (SNR) of about 0.00001, which severely distorted the EEG signals and limited analysis using existing frequency domain analysis methods.

[0006] Therefore, there is a need to develop a technology that enables the acquisition of high-quality cortical-cortical evoked potential signals using transcranial magnetic stimulation.

[0007] The present disclosure provides an apparatus, method, and computer program for detecting cortical-cortical evoked potentials using transcranial magnetic stimulation, which improves the quality of brainwave signals caused by strong electromagnetic noise generated by the stimulation of transcranial magnetic stimulation when performing non-invasive cortical-cortical evoked potential studies using transcranial magnetic stimulation, and enables the automatic detection and analysis of only high-quality cortical-cortical evoked potential signals confirmed through criteria based on visual analysis.

[0008] The problems that this disclosure aims to solve are not limited to those mentioned above, and other unmentioned problems will be clearly understood by a person skilled in the art from the description below.

[0009] A cortical-cortical evoked potential detection device using transcranial magnetic stimulation according to one aspect of the present disclosure for achieving the aforementioned technical problem comprises: a communication module that communicates with an external device; a memory in which at least one process for performing a cortical-cortical evoked potential detection operation using transcranial magnetic stimulation is stored; and a processor that performs a cortical-cortical evoked potential detection operation using transcranial magnetic stimulation according to the process, wherein the processor may be configured to collect brainwave signals induced by transcranial magnetic stimulation to calculate a noise interval, cluster brainwave channels excluding the calculated noise interval, perform clustered brainwave analysis based on a representative brainwave signal of each cluster, perform individual brainwave analysis based on individual brainwave signals within each cluster, and then detect a cortical-cortical evoked potential signal based on the clustered brainwave analysis result and the individual brainwave analysis result.

[0010] Meanwhile, a method for detecting cortical-cortical evoked potentials using transcranial magnetic stimulation according to one aspect of the present disclosure may include: a step of collecting brainwave signals induced by transcranial magnetic stimulation and calculating a noise interval; a step of clustering brainwave channels excluding the calculated noise interval; a step of performing clustered brainwave analysis based on a representative brainwave signal of each cluster; a step of performing individual brainwave analysis based on individual brainwave signals within each cluster; and a step of detecting cortical-cortical evoked potential signals based on the results of the clustered brainwave analysis and the individual brainwave analysis.

[0011] In addition, a computer program stored on a computer-readable recording medium for executing a method for implementing the present disclosure may be further provided.

[0012] In addition, a computer-readable recording medium for recording a computer program for executing a method for implementing the present disclosure may be further provided.

[0013] According to the means for solving the aforementioned problem of the present disclosure, when performing a non-invasive cortical-cortical evoked potential study using transcranial magnetic stimulation, the quality of brainwave signals caused by strong electromagnetic noise generated by the stimulation of transcranial magnetic stimulation is improved, thereby enabling the automatic detection and analysis of only high-quality cortical-cortical evoked potential signals.

[0014] The effects of the present disclosure are not limited to those mentioned above, and other unmentioned effects will be clearly understood by a person skilled in the art from the description below.

[0015] FIG. 1 is a diagram showing the network structure of a cortical-cortical evoked potential detection system using transcranial magnetic stimulation according to one embodiment of the present disclosure.

[0016] FIG. 2 is a schematic diagram showing the configuration of a service server for detecting cortical-cortical evoked potentials using transcranial magnetic stimulation according to one embodiment of the present disclosure.

[0017] FIG. 3 is a diagram illustrating a method for detecting cortical-cortical evoked potentials using transcranial magnetic stimulation according to one embodiment of the present disclosure.

[0018] FIG. 4 is a drawing showing an example of performing transcranial magnetic stimulation according to one embodiment of the present disclosure.

[0019] FIG. 5 is a diagram illustrating the operation of determining a noise interval based on brainwave signals induced by transcranial magnetic stimulation collected according to one embodiment of the present disclosure.

[0020] FIG. 6 is a graph showing cluster brainwave characteristics, individual brainwave characteristics, and coefficients calculated using them, each according to an embodiment of the present disclosure.

[0021] FIG. 7 is a diagram showing a specific operation for performing clustering brainwave analysis in a method for detecting cortical-cortical evoked potentials using transcranial magnetic stimulation according to one embodiment of the present disclosure.

[0022] FIG. 8 is a diagram showing a specific operation for detecting a cortical-cortical evoked potential signal in a method for detecting cortical-cortical evoked potentials using transcranial magnetic stimulation according to one embodiment of the present disclosure.

[0023] The advantages and features of the present disclosure and the methods for achieving them will become clear by referring to the embodiments described below in detail together with the accompanying drawings. However, the present disclosure is not limited to the embodiments disclosed below but may be implemented in various different forms. These embodiments are provided merely to make the present disclosure complete and to fully inform those skilled in the art of the scope of the present disclosure, and the present disclosure is defined only by the scope of the claims.

[0024] The terms used in this specification are for describing embodiments and are not intended to limit the disclosure. In this specification, the singular form includes the plural form unless specifically stated otherwise in the text. The terms “comprises” and / or “comprising” as used in this specification do not exclude the presence or addition of one or more other components in addition to the components mentioned. Throughout the specification, the same reference numerals refer to the same components, and “and / or” includes each of the mentioned components and all combinations of one or more. Although terms such as “first,” “second,” etc., are used to describe various components, these components are not limited by these terms. These terms are used merely to distinguish one component from another. Accordingly, the first component mentioned below may be the second component within the technical scope of this disclosure.

[0025] Unless otherwise defined, all terms used herein (including technical and scientific terms) may be used in a meaning commonly understood by those skilled in the art to which this disclosure pertains. Additionally, terms defined in commonly used dictionaries are not to be interpreted ideally or excessively unless explicitly and specifically defined otherwise.

[0026] Throughout this disclosure, the same reference numerals denote the same components. This disclosure does not describe all elements of the embodiments, and general content in the art to which this disclosure pertains or content that overlaps between embodiments is omitted. As used in the specification, the terms “part” or “module” refer to hardware components such as software, FPGAs, or ASICs, and the “part” or “module” performs certain roles. However, the term “part” or “module” is not limited to software or hardware. The “part” or “module” may be configured to reside in an addressable storage medium or may be configured to run one or more processors. Accordingly, by example, the “part” or “module” includes components such as software components, object-oriented software components, class components, and task components, as well as processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functions provided within the components and "parts" or "modules" may be combined into a smaller number of components and "parts" or "modules," or further separated into additional components and "parts" or "modules."

[0027] Throughout the specification, when a part is described as being "connected" to another part, this includes not only cases where they are directly connected but also cases where they are indirectly connected, and indirect connections include connections made via a wireless communication network.

[0028] Furthermore, when it is stated that a part "includes" a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components.

[0029] Throughout the specification, when it is stated that a component is located "on" another component, this includes not only cases where a component is in contact with another component, but also cases where another component exists between the two components.

[0030] Terms such as "first," "second," etc., are used to distinguish one component from another, and the components are not limited by the aforementioned terms.

[0031] Singular expressions include plural expressions unless there is an obvious exception in the context.

[0032] In each step, identification codes are used for convenience of explanation and do not describe the order of the steps; the steps may be performed differently from the specified order unless a specific order is clearly indicated in the context.

[0033] The terms used in the following description are defined as follows.

[0034] Although described herein with limitation as a 'service server,' this refers to a device for detecting cortical-cortical evoked potentials using transcranial magnetic stimulation and providing detection results and / or analysis results based thereon, and may include all various devices capable of performing computational processing. Furthermore, this service server may be connected to or linked with a separate server, computer, and / or portable terminal to change settings or collect and analyze information and provide it, and its type and form are not limited.

[0035] Here, the computer may include, for example, a notebook, desktop, laptop, tablet PC, slate PC, etc. equipped with a web browser.

[0036] The above server is a server that processes information by communicating with external devices, and may include an application server, a computing server, a database server, a file server, a game server, a mail server, a proxy server, and a web server.

[0037] The above portable terminal may include, for example, all types of handheld-based wireless communication devices such as PCS (Personal Communication System), GSM (Global System for Mobile communications), PDC (Personal Digital Cellular), PHS (Personal Handyphone System), PDA (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), WiBro (Wireless Broadband Internet) terminals, smartphones, etc., as well as wearable devices such as watches, rings, bracelets, anklets, necklaces, glasses, contact lenses, or head-mounted devices (HMDs).

[0038] The operating principles and embodiments of the present disclosure will be described below with reference to the attached drawings.

[0039] FIG. 1 is a diagram showing the network structure of a cortical-cortical evoked potential detection system using transcranial magnetic stimulation according to one embodiment of the present disclosure.

[0040] Referring to FIG. 1, a cortical-cortical evoked potential detection system using transcranial magnetic stimulation according to one embodiment of the present disclosure (hereinafter referred to as the 'detection system') (1000) may be configured to include at least one of a service server (100), a procedure device (200) for performing transcranial magnetic stimulation, and a user terminal (300).

[0041] A service server (100) is a device for detecting cortical-cortical evoked potentials using transcranial magnetic stimulation, and for this purpose, it may provide a separate web page and / or platform (application), and each of the treatment device (200) and user terminal (300) may provide or receive various information / data for detecting cortical-cortical evoked potentials using transcranial magnetic stimulation based on the web page or platform.

[0042] Additionally, the service server (100) transmits and receives various data and / or information with a surgical device (200), a user terminal (300), or a separate external device (not shown) based on at least one communication method, performs a detection operation for cortical-cortical evoked potentials, and provides the detection result.

[0043] This service server (100) is connected to a procedure device (200) and / or a user terminal (300) to perform detection of cortical-cortical evoked potentials, and can collect or receive all information / data related to the detection operation of cortical-cortical evoked potentials, such as various notifications, requests, and information. Meanwhile, the detection system (1000) may be linked to an electronic medical record system to collect and utilize various information regarding each subject (patient). This electronic medical record system is a system configured to allow each medical staff or each medical institution to record and manage various information regarding each subject, and when information regarding a specific patient is input from a specific medical staff or a specific medical institution, all of it can be processed and managed in a database. Here, the various information regarding each patient may include basic information such as personal details and physical information, as well as medical data (medical information) including at least one of medical history, medical results, and medical images. However, this is merely one example, and other additional information regarding other patients may be included or at least some may be excluded, and the stored data / information is not limited to the data / information listed above.

[0044] Specifically, the service server (100) collects brainwave signals induced by transcranial magnetic stimulation from the surgical device (200) and automatically detects cortical-cortical evoked potential characteristics through brainwave signal patterns based on this. Additionally, the service server (100) can perform analysis based on the detected cortical-cortical evoked potential characteristics.

[0045] Thus, by providing the detection result and / or analysis result to the user terminal (300), the user can easily visually check the detection result and / or analysis result. At this time, although the detection result and the analysis result were described separately, for the convenience of explanation, they will be collectively referred to as the detection result below.

[0046] In this case, the corticocortical evoked potential may be non-invasive or invasive, but is not limited thereto. Additionally, the corticocortical evoked potential detection algorithm using transcranial magnetic stimulation according to the present disclosure can be validated by utilizing invasive corticocortical evoked potentials.

[0047] Meanwhile, the service server (100) may collect and use at least one piece of data regarding the target for detection or analysis, and at least some of the at least one piece of data may be collected through a crawler or an API (Application Programming Interface). The data to be collected can be identified through data tagging. A crawler is a type of data collector that automatically searches and indexes various information on the web. A crawler is also referred to as software, a spider, a bot, or an intelligent agent. A crawler continuously searches for new web pages according to a pre-programmed method of a computer program and repeats the task of searching for new information based on the search results. An API refers to a collection of screen configurations, various functions, etc., that are necessary for application developers to easily develop programs that run on an operating system. By utilizing an API, medical data regarding a patient that is frequently generated or updated through at least one external device / server can be collected in real time.

[0048] Meanwhile, the surgical device (200) is a device for performing transcranial magnetic stimulation, and by generating a magnetic field while an electromagnetic coil is in contact with the surface of the subject's head, the magnetic field passes through the skull via the surface of the head to activate or inhibit nerve cells in a specific part of the brain. This transcranial magnetic stimulation is a non-invasive brain stimulation method using an electric current through changes in the magnetic field.

[0049] Additionally, the user terminal (300) may include at least one terminal possessed by a user who is pre-registered or connected to the service server (100) to receive detection results provided by the service server (100). Here, the term "user" is a collective concept referring to the patient, who is the subject of the procedure, as well as guardians, medical staff, and medical institution personnel, and can refer to any person or institution related to the patient without limitation. However, the service server (100) may grant prior authority to the patient and guardian to enable information sharing and access regarding the patient. At this time, there may be differences in authority regarding information sharing and access for each user, and such authority can be set or changed by the patient, guardian, medical staff, medical institution personnel, or the administrator of the service server (100). For example, the patient may be granted authority to send and receive all data related to the patient from the service server (100), while the guardian may be granted authority to receive only some data regarding the patient from the service server (100). Here, each patient and at least one guardian may be distinguished based on the terminal, but may also be distinguished based on inputting, recognizing, and / or authenticating a unique ID, biometric information (fingerprint, voice, facial recognition, etc.) through the platform.

[0050] In order to receive detection results provided by the service server (100), each user may access a web page provided by the service server (100) through a user terminal (300) they possess, or install and use a platform (application). Based on this web page or platform, the user may receive detection results regarding the cortical-cortical evoked potential of the target object from the service server (100) through the user terminal (300). At this time, the detection results may be displayed through a display module provided on the user terminal (300) so that the user can visually check them, or they may be displayed on a separately connected display module so that the user can easily check them visually.

[0051] Meanwhile, the user terminal (300) may be a computer, UMPC (Ultra Mobile PC), workstation, netbook, PDA (Personal Digital Assistants), portable computer, web tablet, wireless phone, mobile phone, smartphone, pad, smart watch, wearable terminal, e-book, PMP (portable multimedia player), portable game console, navigation device, black box or digital camera, or other mobile communication terminal, capable of installing and running a number of applications (i.e., applications) desired by the user. That is, the user terminal (200) may be provided in various forms, and its form, number, and type are not limited.

[0052] As described above, the measurement system (1000) according to the present disclosure can be implemented through the transmission and reception of data / information between a network-based service server (100), a procedure device (200), and / or a user terminal (300).

[0053] FIG. 2 is a schematic diagram showing the configuration of a service server for detecting cortical-cortical evoked potentials using transcranial magnetic stimulation according to one embodiment of the present disclosure.

[0054] Referring to FIG. 2, a service server (100) according to one embodiment of the present disclosure may be configured to include at least one of a communication module (110), a memory (120), and a processor (130).

[0055] The communication module (110) can communicate with at least one of various terminals (devices), external storage (e.g., database (140)), external servers, and cloud servers.

[0056] Meanwhile, an external server or cloud server may be configured to perform at least a part of the role of the processor (130). That is, the performance of data processing or data operations, etc., can be performed on an external server or cloud server, and the present disclosure does not impose any special restrictions on such a method.

[0057] Meanwhile, the communication module (110) can support various communication methods depending on the communication standards of the target being communicated (e.g., electronic device, external server, device, etc.).

[0058] For example, the communication module (110) may be configured to communicate with a communication target using at least one of the following technologies: WLAN (Wireless LAN), Wi-Fi (Wireless-Fidelity), Wi-Fi (Wireless Fidelity) Direct, DLNA (Digital Living Network Alliance), WiBro (Wireless Broadband), WiMAX (World Interoperability for Microwave Access), HSDPA (High Speed ​​Downlink Packet Access), HSUPA (High Speed ​​Uplink Packet Access), LTE (Long Term Evolution), LTE-A (Long Term Evolution-Advanced), 5G (5th Generation Mobile Telecommunication), Bluetooth (Bluetooth™), RFID (Radio Frequency Identification), Infrared Data Association (IrDA), UWB (Ultra-Wideband), ZigBee, NFC (Near Field Communication), Wi-Fi Direct, and Wireless USB (Wireless Universal Serial Bus).

[0059] Meanwhile, the memory (120) may be configured to store various information related to the present disclosure. In the present disclosure, the memory (120) may be provided in the device itself according to the present disclosure. Alternatively, at least a portion of the memory (120) may refer to at least one of a database (DB, 140) and a cloud storage (or cloud server). That is, the memory (120) is sufficient as a space where information necessary for the device and method according to the present disclosure is stored, and it can be understood that there are no restrictions on the physical space. Accordingly, below, the memory (120), database (140), external storage, and cloud storage (or cloud server) will not be distinguished separately and will all be referred to as memory (120).

[0060] This memory (120) can store a number of applications (or applications) running on the service server (100), data for the operation of the service server (100), and instructions. At least some of these applications may be downloaded from an external server via wireless communication. Meanwhile, the applications may be stored in at least one memory provided in the memory (120), installed on the service server (100), and driven to perform operations (or functions) by at least one processor stored in the memory (120) through the processor (130).

[0061] Meanwhile, at least one memory may include a storage medium of at least one type among flash memory type, hard disk type, multimedia card micro type, card type memory (e.g., SD or XD memory, etc.), RAM (Random Access Memory), SRAM (Static Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory), magnetic memory, magnetic disk, and optical disk. In addition, the memory may store information temporarily, permanently, or semi-permanently, and may be provided as an embedded or removable type.

[0062] Next, the processor (130) may be configured to control the overall operation of the device related to the present disclosure. The processor (130) may process brainwave signals, data, information, etc. that are input or output through the components described above, or provide or process appropriate information or functions to a user (including an administrator, worker, inspector, etc.).

[0063] The processor (130) may include at least one CPU (Central Processing Unit) and may perform the functions according to the present disclosure.

[0064] Specifically, the processor (130) may be configured to collect brainwave signals induced by transcranial magnetic stimulation to calculate noise intervals, cluster brainwave channels based on the calculated noise intervals, perform clustered brainwave analysis based on representative brainwave signals of each cluster, perform individual brainwave analysis based on individual brainwave signals within each cluster, and then detect cortical-cortical evoked potential signals based on the clustered brainwave analysis results and the individual brainwave analysis results.

[0065] At this time, the processor (130) may perform epoching on the collected brainwave signal before calculating the noise interval.

[0066] Here, segmentation refers to the process of dividing brainwave signals by setting intervals of a certain time before and after the stimulus onset (e.g., 500ms or 1000ms before and after the stimulus). Through this segmentation process, only brainwave signals related to the stimulus can be selected for analysis. Each interval serves as a basic unit for analyzing the brain response to transcranial stimulation, and noise intervals are calculated based on these segmented signals.

[0067] In this case, the processor (130) can calculate a noise interval for each segment according to the segmentation. To do this, a segment in which the amplitude of the signal from the time of stimulation is n times (e.g., 5 times) or more of the total signal standard deviation is considered as noise, and that segment is determined as a noise interval. The noise interval can be calculated by calculating a moving standard deviation (moving std) within the segmented signal and calculating a segment that exceeds a threshold.

[0068] Meanwhile, the processor (130) may be configured to collect at least one data related to the subject from at least one of the surgical device (200), user terminal (300), database and pre-linked electronic medical record system.

[0069] Additionally, the processor (130) may be configured to calculate the noise interval as a section in which the amplitude of the brainwave signal from the stimulation point for transcranial magnetic stimulation is n times (e.g., 5 times) or more of the standard deviation of the entire brainwave signal.

[0070] In this case, the noise region can be determined based on two pieces of information.

[0071] Meanwhile, the processor (130) may be configured to generate a similarity matrix by combining the Euclidean distance between electrodes and the correlation coefficient between brainwave signals when clustering brainwave channels, and to apply a hierarchical clustering algorithm based on the matrix. That is, the processor (130) may be configured to perform hierarchical clustering considering the distance between each brainwave electrode and the similarity of the brainwave signals.

[0072] Additionally, when performing clustering brainwave analysis, the processor (130) may be configured to calculate the representative brainwave signal of each cluster through a median operation, detect a negative peak and a positive peak in the representative brainwave signal, and then filter triangles corresponding to cortical-cortical evoked potential characteristics based on the positive peak and the negative peak. In other words, the processor (130) may be configured to construct triangles corresponding to cortical-cortical evoked potential characteristics based on the positive peak and the negative peak, and to filter only triangles that meet the criteria corresponding to those characteristics. Here, the criteria may include at least one of amplitude, duration, skewness, and latency.

[0073] Meanwhile, the processor (130) may be configured to determine the point most likely to be located at the peak of the cortical-cortical evoked potential by multiplying the cluster coefficient according to the clustering brainwave analysis result and the coefficient of the individual brainwave according to the individual brainwave analysis result when detecting a cortical-cortical evoked potential signal, and to calculate the cortical-cortical evoked potential characteristic by selecting the triangle closest to the determined point. For example, the processor (130) may be configured to calculate at least one of Onset latency, Peak latency, Offset latency, Amplitude, and Duration as a cortical-cortical evoked potential characteristic based on the selected triangle.

[0074] In addition, the specific operation of the processor (130) will be explained below based on each drawing.

[0075] In addition, at least one component may be added or removed in response to the performance of the components illustrated in FIG. 2. Furthermore, it will be readily understood by those skilled in the art that the relative positions of the components may be changed in response to the performance or structure of the device.

[0076] FIG. 3 is a diagram illustrating a method for detecting cortical-cortical evoked potentials using transcranial magnetic stimulation according to one embodiment of the present disclosure. Hereinafter, while describing FIG. 3, reference will be made to FIG. 4 to 8 for a more detailed explanation.

[0077] Referring to FIG. 3, the service server (100) collects brainwave signals induced by transcranial magnetic stimulation and calculates a noise interval (S110).

[0078] At this time, the brainwave signal is collected from the procedure device (200), and as shown in FIG. 4, the procedure module (2000) equipped in the procedure device (200) is placed adjacent to one side of the head of the subject (A) and can be collected by generating a magnetic field (14) through an electromagnetic coil (11). That is, the brainwave signal may be obtained by stimulating a specific part (12) of the brain using an electric field induced in the brain of the subject (A) by the magnetic field (14) generated from the electromagnetic coil (11). To this end, the subject (A) may wear a separate cap (EEG cap) formed to enable the measurement of brainwave signals on the head, or at least one electrode may be attached to the head of the subject (A).

[0079] Meanwhile, depending on the shape of the electromagnetic coil (2000), the strength and shape of the magnetic field generated around the electromagnetic coil (2000) may differ, and depending on the shape of the head and brain of the object (A), the area (13) or appearance in which the electrical signal is propagated, as well as the waveform of the brainwave signal, may also differ.

[0080] Additionally, the service server (100) determines the noise interval based on this brainwave signal, and for this purpose, a moving std and a threshold may be used. First, the moving std is a concept similar to a moving average, and the standard deviation (σ) on the moving window i The power of the brainwave signal can be estimated by calculating ). At this time, the standard deviation can be calculated by the following mathematical formula 1.

[0081] [Mathematical Formula 1]

[0082]

[0083]

[0084]

[0085] Here, W iis a set of indices within a window centered at i. Here, the max and min functions consider the window to the extent that it partially contains data at the beginning and end of the time series.

[0086] In addition, the threshold can be calculated by the following mathematical formula 2.

[0087] [Mathematical Formula 2]

[0088]

[0089] Here, represents the standard deviation of the eeg time series, and is a threshold constant.

[0090] Meanwhile, although FIG. 4 illustrates that electrical signals are propagated only to the area where transcranial magnetic stimulation is applied, other areas of the brain (not shown) connected to the area (13) may also be affected in addition to that area (13). That is, cortical-cortical evoked potentials can be generated in both the area (13) and other areas (not shown). For example, when the occipital lobe is stimulated with transcranial magnetic stimulation, cortical-cortical evoked potentials can be generated not only in the occipital lobe area but also in the frontal lobe along the tract.

[0091] Afterward, the service server (100) calculates the point where the brainwave signal becomes zero due to stimulation, and determines the section where the moving std exceeds the threshold based on that point as the noise interval. In FIG. 5, the section where the moving std exceeds the threshold corresponds to the shaded area (50). If there is no section where the moving std exceeds the threshold, the service server (100) considers it not to be affected by noise.

[0092] Next, the service server (100) performs clustering on the brainwave channels excluding the noise intervals calculated by step S110 (S120). At this time, the service server (100) generates a similarity matrix by combining the Euclidean distance between electrodes and the correlation coefficient between brainwave signals, and applies a hierarchical clustering algorithm based on this matrix. For example, the hierarchical clustering algorithm may be Ward's method.

[0093] Next, the service server (100) performs clustering brainwave analysis based on the clustering results of step S120, but based on the representative brainwave signal of each cluster (S130), and performs individual brainwave analysis based on the individual brainwave signal within each cluster (S140).

[0094] First, FIG. 7 specifically illustrates step S130 of performing clustering brainwave analysis, wherein the service server (100) calculates the representative brainwave signal of each cluster through a median calculation (S131).

[0095] Next, the service server (100) detects a negative peak and a positive peak in a representative brainwave signal (S132), and then filters triangles corresponding to cortical-cortical evoked potential characteristics based on the detected positive peak and negative peak (S133).

[0096] Meanwhile, in step S140, which performs individual brainwave analysis, some parts may be composed of the same procedures as steps S131 to S133 described above.

[0097] Next, the service server (100) detects cortical-cortical evoked potential signals based on the clustering brainwave analysis results according to step S130 and the individual brainwave analysis results according to step S140 (S150).

[0098] FIG. 8 specifically illustrates step S150 for detecting cortical-cortical evoked potential signals, wherein the service server (100) multiplies the cluster coefficient based on the clustering brainwave analysis result of step S130 and the coefficient of the individual brainwave based on the individual brainwave analysis result of step S140 (S151), and determines the point most likely to be located at the peak of the cortical-cortical evoked potential (S152).

[0099] Afterwards, the service server (100) selects the triangle closest to the point determined by step S152 and calculates the cortical-cortical evoked potential characteristics (S153).

[0100] FIG. 6 is an example of steps S151 to S153 described above, wherein (a) shows cluster brainwave characteristics, (b) shows individual brainwave characteristics, and (c) shows a graph for each of the coefficients calculated using these.

[0101] In particular, the service server (100) can calculate cortical-cortical evoked potential characteristics by calculating at least one of Onset latency, Peak latency, Offset latency, Amplitude and Duration using the vertex and base of the selected triangle as in (c).

[0102] The aforementioned program may include code encoded in a computer language such as C, C++, JAVA, or machine language, which can be read by the computer's processor (CPU) through the computer's device interface, in order for the computer to read the program and execute the methods implemented in the program. Such code may include functional code related to functions that define the necessary functions for executing the methods, and may include control code related to execution procedures necessary for the computer's processor to execute the functions according to a predetermined procedure. Additionally, such code may further include memory reference code regarding where (address) additional information or media necessary for the computer's processor to execute the functions should be referenced in the computer's internal or external memory. In addition, if the processor of the computer needs to communicate with any other computer or server located remotely in order to execute the above functions, the code may further include communication-related code regarding how to communicate with any other computer or server located remotely using the communication module of the computer, and what information or media to transmit or receive during communication.

[0103] The above-mentioned storage medium refers to a medium that stores data semi-permanently and is readable by a device, rather than a medium that stores data for a short period of time, such as a register, cache, or memory. Specifically, examples of the above-mentioned storage medium include, but are not limited to, ROM, RAM, CD-ROM, magnetic tape, floppy disk, and optical data storage device. That is, the above-mentioned program may be stored on various recording media on various servers that the computer can access, or on various recording media on the user's computer. Additionally, the above-mentioned medium may be distributed across networked computer systems, and computer-readable code may be stored in a distributed manner.

[0104] The steps of the method or algorithm described in connection with the embodiments of the present disclosure may be implemented directly in hardware, implemented as a software module executed by hardware, or implemented by a combination thereof. The software module may reside in RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), Flash Memory, a hard disk, a removable disk, a CD-ROM, or any form of computer-readable recording medium well known in the art to which the present disclosure belongs.

[0105] Although embodiments of the present disclosure have been described above with reference to the attached drawings, those skilled in the art will understand that the present disclosure may be implemented in other specific forms without altering its technical concept or essential features. Therefore, the embodiments described above should be understood as illustrative in all respects and not restrictive.

Claims

1. A communication module that communicates with an external device; A memory storing at least one process for performing a cortical-cortical evoked potential detection operation using transcranial magnetic stimulation; and It includes a processor that performs a cortical-cortical evoked potential detection operation using transcranial magnetic stimulation according to the above process, The above processor is, A system configured to collect brainwave signals induced by transcranial magnetic stimulation to calculate noise intervals, cluster brainwave channels excluding the calculated noise intervals, perform clustered brainwave analysis based on representative brainwave signals of each cluster, perform individual brainwave analysis based on individual brainwave signals within each cluster, and then detect cortical-cortical evoked potential signals based on the results of the clustered brainwave analysis and the individual brainwave analysis. Corticocortical evoked potential detection device using transcranial magnetic stimulation.

2. In Paragraph 1, The above processor is, When calculating the above noise interval, configured to calculate the section from the stimulation point for the above transcranial magnetic stimulation where the amplitude of the above brainwave signal is at least n times the standard deviation of the entire brainwave signal as the noise interval. Corticocortical evoked potential detection device using transcranial magnetic stimulation.

3. In Paragraph 1, The above processor is, When clustering the above brainwave channels, a similarity matrix is ​​generated by combining the Euclidean distance between electrodes and the correlation coefficient between brainwave signals, and a hierarchical clustering algorithm is applied based on the matrix. Corticocortical evoked potential detection device using transcranial magnetic stimulation.

4. In Paragraph 1, The above processor is, When performing the above clustering EEG analysis, A method configured to calculate a representative brainwave signal of each cluster through a median operation, detect a negative peak and a positive peak in the representative brainwave signal, and filter triangles corresponding to cortical-cortical evoked potential characteristics based on the positive peak and the negative peak. Corticocortical evoked potential detection device using transcranial magnetic stimulation.

5. In Paragraph 4, The above processor is, When filtering triangles that correspond to the above cortical-cortical evoked potential characteristics, configured to filter only triangles that meet preset criteria, Corticocortical evoked potential detection device using transcranial magnetic stimulation.

6. In Paragraph 5, The above criteria are, including at least one of amplitude, duration, skewness, and latency, Corticocortical evoked potential detection device using transcranial magnetic stimulation.

7. In Paragraph 1, The above processor is, When the above cortical-cortical evoked potential signal is detected, A configuration for determining the point most likely to be located at the peak of the cortical-cortical evoked potential by multiplying the cluster coefficient based on the clustering brainwave analysis result and the coefficient of the individual brainwave based on the individual brainwave analysis result, and for calculating the cortical-cortical evoked potential characteristics by selecting the triangle closest to the determined point. Corticocortical evoked potential detection device using transcranial magnetic stimulation.

8. In Paragraph 7, The above processor is, When calculating the above cortical-cortical evoked potential characteristics, Calculating at least one of Onset latency, Peak latency, Offset latency, Amplitude, and Duration as the cortical-cortical evoked potential characteristic based on the selected triangle above, Corticocortical evoked potential detection device using transcranial magnetic stimulation.

9. A method for detecting cortical-cortical evoked potentials using transcranial magnetic stimulation performed by a device, A step of collecting brainwave signals induced by transcranial magnetic stimulation and calculating a noise interval; A step of clustering brainwave channels excluding the noise intervals calculated above; A step of performing clustering brainwave analysis based on representative brainwave signals of each cluster; A step of performing individual brainwave analysis based on individual brainwave signals within each of the above clusters; and A step comprising detecting cortical-cortical evoked potential signals based on clustering brainwave analysis results and individual brainwave analysis results, Method for detecting cortical-cortical evoked potentials using transcranial magnetic stimulation.

10. In Paragraph 9, The step of calculating the above noise interval is, Calculating the noise interval as the interval in which the amplitude of the brainwave signal is at least n times the standard deviation of the entire brainwave signal from the stimulation point for the above transcranial magnetic stimulation, Method for detecting cortical-cortical evoked potentials using transcranial magnetic stimulation.

11. In Paragraph 9, The step of clustering the above brainwave channels is, Generating a similarity matrix by combining the Euclidean distance between electrodes and the correlation coefficient between brainwave signals, and applying a hierarchical clustering algorithm based on said matrix. Method for detecting cortical-cortical evoked potentials using transcranial magnetic stimulation.

12. In Paragraph 9, The step of performing the above clustering brainwave analysis is, A step of calculating the representative brainwave signal of each of the above clusters through a median calculation; A step of detecting a negative peak and a positive peak in the above representative brainwave signal; and A step comprising filtering triangles corresponding to cortical-cortical evoked potential characteristics based on the above positive peak and the above negative peak, Method for detecting cortical-cortical evoked potentials using transcranial magnetic stimulation.

13. In Paragraph 12, The step of filtering the triangles above filters only the triangles that meet preset criteria, and The above criteria include at least one of amplitude, duration, skewness, and latency, Method for detecting cortical-cortical evoked potentials using transcranial magnetic stimulation.

14. In Paragraph 9, The step of detecting the above cortical-cortical evoked potential signal is, A step of determining the point most likely to be located at the peak of the cortical-cortical evoked potential by multiplying the cluster coefficient based on the clustering brainwave analysis result and the coefficient of the individual brainwave based on the individual brainwave analysis result; and A step comprising selecting the triangle closest to the above-determined point to calculate cortical-cortical evoked potential characteristics, Method for detecting cortical-cortical evoked potentials using transcranial magnetic stimulation.

15. In Paragraph 14, The step of calculating the above cortical-cortical evoked potential characteristics is, Calculating at least one of Onset latency, Peak latency, Offset latency, Amplitude, and Duration as the cortical-cortical evoked potential characteristic based on the selected triangle above, Method for detecting cortical-cortical evoked potentials using transcranial magnetic stimulation.

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