KR102192345B1 - Eeg signal variability analysis system for depression diagnosis and method thereof - Google Patents

Abstract

According to various embodiments of the present disclosure, an apparatus and method for analyzing EEG variability for diagnosis of depression include refined composite multi-scale permutation entropy (RCMPE) indicators based on EEG data and obtaining EEG data. May be configured to detect EEG variability for EEG data using the subdivided complex multiscale permutation entropy index.

Description

EEG variability analysis device and method for diagnosis of depression {EEG SIGNAL VARIABILITY ANALYSIS SYSTEM FOR DEPRESSION DIAGNOSIS AND METHOD THEREOF}

Various embodiments relate to an electronic device and a method of operating the same, and more particularly, to an apparatus and method for analyzing brain wave variability based on depression diagnosis.

Depression is a mental illness that affects more than 300 million people worldwide, causing the greatest burden on humanity. Providing adequate treatment for depression, patients can resume work on average 70 days before, and depressed patients receiving appropriate treatment have a remission rate of 76%. However, since there are no biomarkers and imaging methods that can be used to diagnose depression, it is difficult to diagnose depression. For this reason, by analyzing the brain waves based on the frequency-resolution, depression is diagnosed.

However, even if brain waves are analyzed based on frequency-resolution, it is difficult to accurately diagnose depression. Therefore, in diagnosing mental disorders such as depression, an EEG analysis method capable of improving accuracy is required.

According to various embodiments of the present disclosure, a method for analyzing EEG variability of an electronic device includes an operation of acquiring EEG data and detecting a refined composite multi-scale permutation entropy (RCMPE) index based on the EEG data. And detecting an EEG variability with respect to the EEG data by using the subdivided complex multiscale permutation entropy index.

An electronic device according to various embodiments of the present disclosure is a device for analyzing an EEG variability, and includes a memory and a processor connected to the memory, wherein the processor acquires EEG data, and is subdivided based on the EEG data. It may be configured to detect a complex multiscale permutation entropy index, and to detect an EEG variability for the EEG data by using the subdivided complex multiscale permutation entropy index.

According to various embodiments, whether to diagnose a mental disorder may be determined based on an EEG variability. In this case, as the electronic device uses the subdivided complex multiscale permutation entropy index, it is possible to accurately detect the EEG variability for EEG data. Here, the electronic device can accurately detect the EEG variability even for short EEG data. Through this, accuracy can be improved in diagnosing mental disorders.

1 is a diagram illustrating an electronic device according to various embodiments.
2 is a diagram illustrating a method of operating an electronic device according to various embodiments.
FIG. 3 is a diagram for describing an operation of acquiring EEG data of FIG. 2.
4 is a diagram illustrating an operation of detecting a subdivided complex multiscale permutation entropy index of FIG. 2.
5 and 6 are diagrams for explaining an operation of detecting a subdivided complex multiscale permutation entropy index of FIG. 2.
7 is a diagram for describing an operation of detecting an EEG variation degree of FIG. 2.

Hereinafter, various embodiments of the present document will be described with reference to the accompanying drawings.

Various embodiments of the present document and terms used therein are not intended to limit the technology described in this document to a specific embodiment, and should be understood to include various modifications, equivalents, and/or substitutes of the corresponding embodiment. In connection with the description of the drawings, similar reference numerals may be used for similar elements. Singular expressions may include plural expressions unless the context clearly indicates otherwise. In this document, expressions such as "A or B", "at least one of A and/or B", "A, B or C" or "at least one of A, B and/or C" are all of the items listed together. It can include possible combinations. Expressions such as "first", "second", "first" or "second" can modify the corresponding elements regardless of their order or importance, and are only used to distinguish one element from another. The components are not limited. When it is mentioned that a certain (eg, first) component is “(functionally or communicatively) connected” or “connected” to another (eg, second) component, the certain component is It may be directly connected to the component, or may be connected through another component (eg, a third component).

The term "module" used in this document includes a unit composed of hardware, software, or firmware, and may be used interchangeably with terms such as, for example, logic, logic blocks, parts, or circuits. A module may be an integrally configured component or a minimum unit or a part of one or more functions. For example, the module may be configured as an application-specific integrated circuit (ASIC).

1 is a diagram illustrating an electronic device 100 according to various embodiments.

Referring to FIG. 1, an electronic device 100 according to various embodiments may include at least one of a sensing unit 110, a memory 120, and a processor 130.

The sensing unit 110 may detect a biological signal of an object. Here, the object may include a person. For example, the sensing unit 110 may detect a biosignal by contacting a human body. In this case, the biosignal may include an electroencephalogram (EEG) signal. For example, the sensing unit 110 may detect a change in current according to brain activity of the object.

The memory 120 may store data used by at least one component of the electronic device 100. The memory 120 may store at least one of input data or output data for software such as a program and a command related thereto. The program may include at least one of an operating system, middleware, and application. For example, the memory 120 may include at least one of volatile memory and nonvolatile memory.

The processor 130 may control at least one component of the electronic device 100 based on a program, and may perform data processing and operation. In this case, the processor 130 may detect an EEG variability for EEG data based on a refined composite multi-scale permutation entropy (RCMPE) index. To this end, the processor 130 detects a subdivided complex multiscale permutation entropy (RCMPE) index based on the EEG data, and uses the subdivided complex multiscale permutation entropy (RCMPE) index to determine the EEG variability for EEG data. Can be detected.

The electronic device 100 according to various embodiments may include a memory 120 and a processor 130 connected to the memory 120 as a device for analyzing brain wave variability.

According to various embodiments, the processor 130 acquires EEG data, detects a refined composite multi-scale permutation entropy (RCMPE) index based on the EEG data, The subdivided complex multiscale permutation entropy (RCMPE) index may be used to detect an EEG variability for the EEG data.

According to various embodiments, the processor 130 arranges data points constituting the EEG data into multiscale time series, and for each of the multiscale time series, the subdivided complex multiscale entropy (RCMPE) It can be configured to detect an indicator.

According to various embodiments, the processor 130, for each of the multiscale time series, based on the data points and a length determined to group the data points, the subdivided complex multiscale entropy (RCMPE) It can be configured to detect an indicator.

According to various embodiments, the data points may be grouped into a plurality of data elements according to the predetermined length for each of the multiscale time series.

According to various embodiments, the predetermined length may be determined differently according to the multiscale time series.

According to various embodiments, the predetermined length may represent the number of data points grouped into each of the data elements.

According to various embodiments, the processor 130 may be configured to determine whether to diagnose a disease based on the EEG variability.

2 is a diagram illustrating a method of operating an electronic device 100 according to various embodiments. 3 is a diagram for explaining an operation of acquiring EEG data of FIG. 2. FIG. 4 is a diagram showing an operation of detecting a refined composite multi-scale permutation entropy (RCMPE) of FIG. 2, and FIGS. 5 and 6 are a detailed composite multi-scale permutation entropy indicator of FIG. 2 These are diagrams for explaining the (RCMPE) detection operation. 7 is a diagram for describing an operation of detecting an EEG variation degree of FIG. 2.

Referring to FIG. 2, the electronic device 100 may acquire EEG data in operation 210. The processor 130 may acquire EEG data, as shown in FIG. 3. According to an embodiment, the processor 130 may measure EEG data from an object through the sensing unit 110. At this time, the object may be in at least one of an open state or a state where the object is closed. According to another embodiment, the processor 130 may receive EEG data from an external device. For example, the processor 130 may acquire brainwave data using a neuroscan system. As an example, the neuroscan system operates based on a band pass filter of 0.5 Hz to 100 Hz, a sampling rate of 500 Hz, and a characteristic corresponding to an impedance of 10 kΩ or less, and uses a silver-silver chloride (Ag-AgCl) electrode. EEG data can be measured.

The electronic device 100 may detect a subdivided complex multiscale permutation entropy (RCMPE) index based on the EEG data in operation 220. In this case, the subdivided complex multiscale permutation entropy (RCMPE) index is an index for classifying the characteristics of the EEG data and may indicate the complexity of the EEG data. The processor 130 may analyze the EEG data to detect a subdivided complex multiscale permutation entropy (RCMPE) index. According to an embodiment, the processor 130 may include a subdivided complex multiscale permutation entropy (RCMPE) index corresponding to a state in which an object is open or a subdivided complex multiscale permutation entropy (RCMPE) corresponding to a state in which the object is closed. RCMPE) can detect at least one of the indicators.

Referring to FIG. 4, in operation 410, the electronic device 100 may configure multi-scale time series 510 and 520 based on EEG data. To this end, the processor 130 may perform preprocessing on the EEG data. For example, the processor 130 may perform filtering on the EEG data. And the processor 130, as shown in Figure 5, the data points constituting the EEG data (data points) (x ₁ , x ₂ , x ₃ , x ₄ , x ₅ , x ₆ , ...) are multi-scale time series It can be arranged in (510, 520). At this time, the data points (x ₁ , x ₂ , x ₃ , x ₄ , x ₅ , x ₆ , …) are, for each of the multi-scale time series 510 and 520, a plurality of data according to a predetermined length (τ). It can be grouped into elements (y ₁ , y ₂ , y ₃ , …). Here, each data element (y ₁ , y ₂ , y ₃ , …) is a data element (y1, y2, y3) corresponding to a predetermined length (τ) and a predetermined length (τ) as shown in [Equation 1] below. , …). Here, the predetermined length τ may be determined differently according to the multiscale time series 510 and 520, and data points to be grouped into each of the data elements (y ₁ , y ₂ , y ₃ , …) (x ₁ , x ₂ , x ₃ , x ₄ , x ₅ , x ₆ , …). For example, for any one of the multiscale time series 510 and 520, the predetermined length τ is determined to be 2, and for the other one of the multiscale time series 510 and 520, the predetermined length τ Can be determined as 3.

Here, i represents the identifier of the data points (x ₁ , x ₂ , x ₃ , x ₄ , x ₅ , x ₆ , …), and j is the identifier of the data elements (y ₁ , y ₂ , y ₃ , …) Can represent.

In operation 420, the electronic device 100 may detect a subdivided composite multiscale entropy (RCMPE) index for each of the multiscale time series 510 and 520. The processor 130 includes data points (x ₁ , x ₂ , x ₃ , x ₄ , x ₅ , x ₆ , ...) for each of the multi-scale time series 510 and 520 as shown in [Equation 2] below. And based on the predetermined length (τ), it is possible to detect a subdivided complex multiscale entropy (RCMPE) index. At this time, the processor 130 uses a plurality of ordinal permutation patterns of data points (x ₁ , x ₂ , x ₃ , x ₄ , x ₅ , x ₆ , …) of a predetermined length (τ). You can sample multiple times based on the field. In other words, the processor 130 may sample data elements (y ₁ , y ₂ , y ₃ , ...) several times based on sequential permutation patterns. In addition, the processor 130 may detect the probability p _j for each of the sequential permutation patterns. Through this, the processor 130 may detect a subdivided complex multiscale entropy (RCMPE) index based on the probability pj for each of the sequential permutation patterns. At this time, if the probability (pj) of the sequential permutation patterns is uniformly distributed within a predetermined range, the subdivided complex multiscale entropy (RCMPE) index is detected as a high value, and the probability of some of the sequential permutation patterns (p _j ) Is distributed outside a predetermined range, or shows a large difference from the probability (p _j ) of the rest of the sequential permutation patterns, the subdivided complex multiscale entropy (RCMPE) index may be detected as a low value. The electronic device 100 may return to FIG. 2.

Here, m is a variable that is arbitrarily determined, and represents the number of data constituting each permutation pattern, and may be determined as any one of 3 to 7, for example, and L may represent a time delay.

As shown in FIG. 6, when τ is 1 and m is set to 3, three consecutive data points (x1) randomly from data points (x1, x2, x3, x4, x5, x6, ...) , x2, x3, x4, x5, x6, ...) can be selected multiple times as data elements (y1, y2, y3, ...). At this time, in each data element (y1, y2, y3, …), the data points (x1, x2, x3, x4, x5, x6, …) are 3! It may belong to one of (3*2*1) sequential permutation patterns. Here, the permutation patterns are (1, 2, 3), (1, 3, 2), (2, 1, 3), (2, 3, 1), (3, 1, 2) and (3, 2, 1) may be included. In addition, the processor 130 randomly selects three consecutive data points (x1, x2, x3, x4, x5, x6, …) from data points (x1, x2, x3, x4, x5, x6, …). By sampling times, the probability pj for each of the sequential permutation patterns can be detected. Through this, the processor 130 may detect a subdivided complex multiscale entropy (RCMPE) index based on the probability pj for each of the sequential permutation patterns.

The electronic device 100 may detect a degree of EEG variability for EEG data using a complex multiscale permutation entropy (RCMPE) index subdivided in operation 230. According to an embodiment, the processor 130 uses a subdivided complex multiscale permutation entropy (RCMPE) index corresponding to a state in which the object is open as shown in (a) of FIG. 7, and calculates the EEG variability. Can be detected. According to another embodiment, the processor 130 uses a subdivided complex multiscale permutation entropy (RCMPE) index corresponding to a state in which the object is closed, as shown in (b) of FIG. Can be detected. According to another embodiment, the processor 130 includes a subdivided complex multiscale permutation entropy (RCMPE) index corresponding to a state in which an object is open and a subdivided complex multiscale permutation entropy corresponding to a state in which the object is closed. EEG variability can be detected using the average of the (RCMPE) index. Through this, the electronic device 100 may determine whether to diagnose a disease based on the EEG variability. Here, the disease may include a psychiatric disease such as depression or anxiety disorder and a central nervous system disease. To this end, the processor 130 may analyze the EEG variability.

According to various embodiments of the present disclosure, an operation of acquiring EEG data and a refined composite multi-scale permutation entropy (RCMPE) based on the EEG data may include obtaining EEG data. An operation of detecting an index, and an operation of detecting an EEG variability for the EEG data using the subdivided complex multiscale permutation entropy (RCMPE) index.

According to various embodiments, the subdivided complex multiscale entropy (RCMPE) index detection operation includes an operation of arranging data points constituting the brainwave data into multiscale time series, and for each of the multiscale time series, It may include the operation of detecting the subdivided complex multiscale entropy (RCMPE) index.

According to various embodiments, for each of the multi-scale time series, the operation of detecting the subdivided complex multi-scale entropy (RCMPE) index includes, for each of the multi-scale time series, the data points and the data points. It may include an operation of detecting the subdivided complex multiscale entropy (RCMPE) index based on a length determined to group the files.

According to various embodiments, the data points may be grouped into a plurality of data elements according to the predetermined length for each of the multiscale time series.

According to various embodiments, the predetermined length may be determined differently according to the multiscale time series.

According to various embodiments, the predetermined length may represent the number of data points grouped into each of the data elements.

According to various embodiments, the method may further include determining whether to diagnose a disease based on the brain wave variability.

According to various embodiments, the operation of acquiring the EEG data includes an operation of measuring the EEG data from the object in at least one of a state in which an object is open or a state in which the object is closed. I can.

According to various embodiments, whether to diagnose a mental disorder may be determined based on an EEG variability. In this case, as the electronic device 100 uses the subdivided complex multiscale permutation entropy (RCMPE) index, it is possible to accurately detect the EEG variability for EEG data. Here, the electronic device 100 may accurately detect the EEG variability even for short EEG data. Through this, accuracy can be improved in diagnosing mental disorders.

Although various embodiments of the present document have been described, various modifications may be made without departing from the scope of the various embodiments of the present document. Therefore, the scope of the various embodiments of the present document is limited to the described embodiments and should not be defined, but should be defined by the scope of the claims as well as the equivalents of the claims to be described later.

Claims (15)

In the electroencephalogram analysis method of an electronic device,
Obtaining EEG data;
Detecting a refined composite multi-scale permutation entropy (RCMPE) index based on the EEG data; And
Using the subdivided complex multi-scale permutation entropy index, including the operation of detecting an EEG variability for the EEG data,
The subdivided complex multiscale entropy index detection operation,
Arranging data points constituting the brainwave data into multiscale time series; And
For each of the multi-scale time series, including the operation of detecting the subdivided complex multi-scale entropy index,
For each of the multiscale time series, the operation of detecting the subdivided complex multiscale entropy index,
For each of the multi-scale time series, grouping the data points into a plurality of data elements based on a predetermined length;
Detecting probabilities for the sequential permutation patterns of each of the data elements by sampling each of the data elements based on a plurality of ordinal permutation patterns; And
And detecting the subdivided complex multiscale entropy indicator based on the detected probabilities.
delete delete delete The method of claim 1,
The predetermined length is determined differently according to the multiscale time series.
The method of claim 1,
The predetermined length represents the number of the data points grouped into each of the data elements.
The method of claim 1,
The method further comprising determining whether to diagnose a disease based on the brain wave variability.
The method of claim 1, wherein the subdivided complex multiscale entropy index,
Subdivided complex multiscale entropy index detected from EEG data measured from the object while the object is open and subdivided complex multiscale entropy detected from EEG data measured from the object while the object is closed The method that is the average of the indicators.
In an electronic device for analyzing brain wave variability,
Memory; And
And a processor connected to the memory,
The processor,
Acquire EEG data,
Based on the EEG data, a refined composite multi-scale permutation entropy (RCMPE) index is detected,
Using the subdivided complex multi-scale permutation entropy index, configured to detect the degree of EEG variability for the EEG data,
The processor,
Arranging data points constituting the brainwave data into multiscale time series,
For each of the multiscale time series, configured to detect the subdivided complex multiscale entropy index,
The processor,
For each of the multiscale time series, grouping the data points into a plurality of data elements based on a predetermined length,
By sampling each of the data elements based on a plurality of sequential permutation patterns, detecting probabilities for each of the sequential permutation patterns of the data elements,
An electronic device configured to detect the subdivided complex multiscale entropy index based on the detected probabilities.
delete delete delete The method of claim 9,
The predetermined length is determined differently according to the multi-scale time series.
The method of claim 9,
The predetermined length indicates the number of the data points grouped into each of the data elements.
The method of claim 9, wherein the processor,
An electronic device configured to determine whether to diagnose a disease based on the brain wave variability.

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