JP2019162237A - Epilepsy determination device, epilepsy determination system, epilepsy determination method, and epilepsy determination program - Google Patents

Abstract

To provide an epilepsy determination device, an epilepsy determination system, an epilepsy determination method, and an epilepsy determination program capable of determining an epileptic seizure more accurately.SOLUTION: An epilepsy determination device 10 includes an acquisition unit 11 for acquiring brain wave data of a subject; a generation unit 12 for cutting out the brain wave data by a predetermined time width and generating a plurality of images showing brain waves; and determination unit 13 for inputting the plurality of images in a learned model 13a, and determining whether or not the brain wave of an epileptic seizure is represented in any of the plurality of images by the learned model.SELECTED DRAWING: Figure 1

Description

  The present invention relates to an epilepsy determination device, an epilepsy determination system, an epilepsy determination method, and an epilepsy determination program.

  Conventionally, an electroencephalogram (EEG) is measured by placing an electrode on a patient's head, and an electroencephalogram pattern peculiar to an epileptic seizure is observed. Observation of such an electroencephalogram pattern may be performed by an epilepsy specialist.

  Non-Patent Document 1 described below describes a technique for classifying whether or not an epileptic seizure has occurred by using a neural network, using an electroencephalogram as an input, extracting feature values by wavelet transform.

E. Juarez-Guerra, V. Alarcon-Aquino, P. Gomez-Gil, "Epilepsy seizure detection in eeg signals using wavelet transforms and neural networks", 2015, Lecture Notes in Electrical Engineering, volume 312, p. 261-269

  However, there are various types of epileptic seizures, and it is known that EEG patterns during seizures are diverse. For this reason, it has been difficult for conventional techniques to determine epileptic seizures with sufficiently high accuracy.

  Therefore, the present invention provides an epilepsy determination device, an epilepsy determination system, an epilepsy determination method, and an epilepsy determination program that can determine an epileptic seizure with higher accuracy.

  An epilepsy determination device according to an aspect of the present invention includes an acquisition unit that acquires brain wave data of a subject, a generation unit that generates brain images by cutting the brain wave data at a predetermined time width, and a plurality of images A determination unit configured to input an image into the learned model and determine whether the brain wave of the epileptic seizure appears in any of the plurality of images by the learned model.

  According to this aspect, the brain wave data is cut out with a predetermined time width, a plurality of images representing the brain waves are generated, and the learned model determines whether any of the plurality of images shows an epileptic brain wave. Thus, the determination can be performed under the same conditions as when the specialist diagnoses the epileptic seizure, and the epileptic seizure can be determined with higher accuracy.

  In the above aspect, a plurality of test images representing brain waves are input to a learned model, and an evaluation unit that evaluates the determination accuracy of the learned model, and a predetermined time width is set based on the evaluation by the evaluation unit A setting unit, a plurality of learning images representing brain waves cut out by a predetermined time width set by the setting unit, and information regarding whether or not the plurality of learning images represent brain waves of epileptic seizures, And a learning unit that learns a learning model as learning data and generates a new learned model.

  According to this aspect, it is possible to generate a new learned model so as to improve the determination accuracy by adjusting the time width for extracting the electroencephalogram data, and to determine epileptic seizures with higher accuracy.

  In the above aspect, the evaluation unit may evaluate the determination accuracy of the learned model based on a plurality of indices.

  According to this aspect, it is possible to evaluate the determination accuracy of the learned model from various angles, and it is possible to more appropriately adjust the time width for extracting the electroencephalogram data.

  In the above aspect, the plurality of indices are an index indicating whether or not an epileptic seizure appears in an image showing an epileptic seizure, and an epilepsy seizure does not appear in an image that does not show an epileptic seizure An index indicating whether it can be judged correctly, an image indicating that an epileptic seizure does not appear for an image showing an epileptic seizure, and an epileptic seizure appearing for an image not showing an epileptic seizure And an index indicating whether the determination is erroneously performed.

  According to this aspect, it is possible to evaluate the determination accuracy of the learned model from the perspective of true positive, true negative, false negative, and false positive in a multifaceted manner, and more appropriately set the time width for extracting the electroencephalogram data. Can be adjusted.

  In the above aspect, the plurality of images may include waveforms indicated by a plurality of colors corresponding to the plurality of electrodes for which electroencephalogram data is measured.

  According to this aspect, a plurality of electrodes for which electroencephalogram data is measured can be distinguished, and determination can be performed under the same conditions as a specialist diagnoses an epileptic seizure, and an epileptic seizure can be determined with higher accuracy. .

  In the above aspect, the learned model may be a learned convolutional neural network.

  According to this aspect, a learned model having a determination accuracy equivalent to or higher than that of a person can be used, and an epileptic seizure can be determined with higher accuracy.

  In the above aspect, the generation unit may generate a plurality of images by performing at least one of a low-pass filter, a high-pass filter, and a notch filter on the electroencephalogram data, and then cutting out the electroencephalogram data with a predetermined time width. Good.

  According to this aspect, it is possible to generate a plurality of images after removing noise that may be included in the electroencephalogram data, and to determine an epileptic seizure with higher accuracy.

  An epilepsy determination system according to another aspect of the present invention is an epilepsy determination system including a measurement apparatus that measures brain wave data of a subject and an epilepsy determination apparatus that determines epilepsy based on the electroencephalogram data. The determination device cuts out the electroencephalogram data with a predetermined time width, generates a plurality of images representing the electroencephalogram, and inputs the plurality of images into the learned model. Depending on the learned model, one of the plurality of images A determination unit that determines whether an electroencephalogram of an epileptic seizure appears, and a notification unit that notifies a predetermined terminal when the determination unit determines that an electroencephalogram of the epileptic seizure appears in any of a plurality of images And having.

  According to this aspect, the brain wave data is cut out with a predetermined time width, a plurality of images representing the brain waves are generated, and the learned model determines whether any of the plurality of images shows an epileptic brain wave. Thus, the determination can be performed under the same conditions as when the specialist diagnoses the epileptic seizure, and the epileptic seizure can be determined with higher accuracy. In addition, when it is determined that an electroencephalogram of an epileptic seizure appears, the burden of the electroencephalogram confirmation work can be reduced by notifying a predetermined terminal.

  An epilepsy determination method according to another aspect of the present invention includes acquiring a subject's brain wave data, cutting out the brain wave data with a predetermined time width, generating a plurality of images representing brain waves, and a plurality of images To the learned model, and to determine whether the brain wave of the epileptic seizure appears in any of the plurality of images by the learned model.

  According to this aspect, the brain wave data is cut out with a predetermined time width, a plurality of images representing the brain waves are generated, and the learned model determines whether any of the plurality of images shows an epileptic brain wave. Thus, the determination can be performed under the same conditions as when the specialist diagnoses the epileptic seizure, and the epileptic seizure can be determined with higher accuracy.

  An epilepsy determination program according to another aspect of the present invention includes a computer provided in an epilepsy determination apparatus, an acquisition unit that acquires brain wave data of a subject, a plurality of brain wave data cut out with a predetermined time width, and a plurality of brain waves It functions as a generation unit that generates an image and a determination unit that inputs a plurality of images to a learned model and determines whether the brain wave of an epileptic seizure appears in any of the plurality of images by the learned model.

  According to this aspect, the brain wave data is cut out with a predetermined time width, a plurality of images representing the brain waves are generated, and the learned model determines whether any of the plurality of images shows an epileptic brain wave. Thus, the determination can be performed under the same conditions as when the specialist diagnoses the epileptic seizure, and the epileptic seizure can be determined with higher accuracy.

  According to the present invention, an epilepsy determination device, an epilepsy determination system, an epilepsy determination method, and an epilepsy determination program that can determine an epileptic seizure with higher accuracy can be provided.

It is a figure which shows the functional block of the epilepsy determination system which concerns on embodiment of this invention. It is a figure which shows the physical structure of the epilepsy determination apparatus which concerns on this embodiment. It is an example of the image input into the learned model of the epilepsy determination apparatus which concerns on this embodiment. It is a 1st graph which shows the determination accuracy of the epilepsy determination apparatus which concerns on this embodiment. It is a 2nd graph which shows the determination precision of the epilepsy determination apparatus which concerns on this embodiment. It is a flowchart of the epilepsy determination process performed by the epilepsy determination system which concerns on this embodiment. It is a flowchart of the learning process performed by the epilepsy determination system which concerns on this embodiment.

  Embodiments of the present invention will be described with reference to the accompanying drawings. In addition, in each figure, what attached | subjected the same code | symbol has the same or similar structure.

  FIG. 1 is a diagram showing functional blocks of an epilepsy determination system 1 according to an embodiment of the present invention. The epilepsy determination system 1 includes an epilepsy determination device 10 and a measurement device 20. The measuring device 20 is a device that measures the subject's brain waves, and measures the electrical activity of the subject's brain with a plurality of electrodes attached to the subject's head. The measuring device 20 may have, for example, 21 electrodes arranged according to the International 10-20 method, but the number and arrangement of electrodes are arbitrary.

  The epilepsy determination device 10 includes an acquisition unit 11, a generation unit 12, a determination unit 13, an evaluation unit 14, a setting unit 15, a learning unit 16, a notification unit 17, and a storage unit 18. The acquisition unit 11 acquires brain wave data of the subject. The acquisition unit 11 may acquire the subject's brain wave data measured by the measurement device 20, but may acquire the subject's brain wave data stored in advance in the storage unit 18 or another storage device.

  The generation unit 12 cuts out the electroencephalogram data with a predetermined time width and generates a plurality of images representing the electroencephalogram. Here, the predetermined time width can be arbitrarily set, but may be, for example, 0.5 seconds, 1 second, 2 seconds, 5 seconds, and 10 seconds. For example, when the predetermined time width is 10 seconds and the recording time of the electroencephalogram data is 1 hour (3600 seconds), the generation unit 12 cuts out the electroencephalogram data in the range of 0 to 10 seconds to express the electroencephalogram. An image is generated, brain wave data in the range of 1 to 11 seconds is cut out to generate a second image representing the brain wave, and this process is continued. Finally, the brain wave data of 3590 to 3600 seconds is cut out to represent the brain wave 3591. An image may be generated. The generation unit 12 may shift the cut-out range at equal intervals so that the ranges of the electroencephalogram data cut out by the plurality of images overlap, but the range of the electroencephalogram data cut out by the plurality of images may not overlap. Good.

  The generation unit 12 may generate at least one of a low-pass filter, a high-pass filter, and a notch filter on the electroencephalogram data, and then extract the electroencephalogram data with a predetermined time width to generate a plurality of images. Here, the low-pass filter may be a filter that passes data with a frequency of 60 Hz or less, for example, the high-pass filter may be a filter that passes data with a frequency of 0.5 Hz or more, and the notch filter is 50 Hz. It may be a filter that blocks data of a certain frequency. Thereby, it is possible to generate a plurality of images after removing noise that may be included in the electroencephalogram data, and to determine an epileptic seizure with higher accuracy.

  The determination unit 13 inputs a plurality of images to the learned model 13a, and determines whether the brain wave of an epileptic seizure appears in any of the plurality of images by the learned model 13a. In the epilepsy determination apparatus 10 according to the present embodiment, the learned model 13a is a learned convolutional neural network (CNN). By using a learned convolutional neural network as the learned model 13a, it is possible to achieve a determination accuracy equal to or higher than that of a human and to determine epileptic seizures with higher accuracy.

  The evaluation unit 14 inputs a plurality of test images representing brain waves to the learned model 13a, and evaluates the determination accuracy of the learned model 13a. Here, the evaluation unit 14 may evaluate the determination accuracy of the learned model 13a based on a plurality of indices. As a result, the determination accuracy of the learned model 13a can be evaluated in a multifaceted manner, and the time width for extracting the electroencephalogram data can be adjusted more appropriately.

  More specifically, there are two or more indicators that indicate whether an epileptic seizure appears correctly for an image showing an epileptic seizure, and an epileptic seizure for an image that does not show an epileptic seizure. If there is an index that indicates that the epileptic seizure does not appear, an index that indicates that the epileptic seizure does not appear, and an image that does not show the epileptic seizure appear. And an index indicating whether it is erroneously determined that it is present. In this way, by evaluating the determination accuracy of the learned model 13a in a multifaceted manner in the case of true positive, true negative, false negative and false positive, the determination accuracy can be evaluated in a multifaceted manner, The time width for extracting the electroencephalogram data can be adjusted more appropriately.

  The setting unit 15 sets a predetermined time width based on the evaluation by the evaluation unit 14. The setting unit 15 may set a predetermined time width so that the evaluation by the evaluation unit 14 is improved.

  The learning unit 16 includes a plurality of learning images representing brain waves, which are cut out with a predetermined time width set by the setting unit 15, and information regarding whether or not the plurality of learning images represent brain waves of epileptic seizures. , And learning data as learning data, a new learned model is generated. Here, the plurality of learning images representing the electroencephalograms may be images that have been measured by the measurement device 20 in the past and associated with information regarding whether or not they represent an epileptic seizure. When the learning model is a neural network such as a convolutional neural network, the learning unit 16 may perform learning processing of the weighting coefficient of the neural network by the error back propagation method. In this way, the setting unit 15 adjusts the time width for extracting the electroencephalogram data, and the learning unit 16 can generate a new learned model so as to improve the determination accuracy. By installing in the unit 13, the epilepsy seizure can be determined with higher accuracy by the epilepsy determination device 10.

  The notification unit 17 notifies a predetermined terminal when the determination unit 13 determines that an electroencephalogram of an epileptic seizure appears in any of a plurality of images. Here, the predetermined terminal may be any information processing terminal. For example, the predetermined terminal may be a terminal used by a specialist, a terminal used by a person who cares for the subject, or a terminal used by the subject himself / herself. You may do it. When it is determined that an electroencephalogram of an epileptic seizure appears, notification to a predetermined terminal can reduce the burden of confirming the electroencephalogram.

  The storage unit 18 may store learning data used by the learning unit 16. But the learning part 16 and the memory | storage part 18 may not be provided in the epilepsy determination apparatus 10, and the learning process of a learning model may be performed by the other apparatus accessible via a communication network. In that case, the epilepsy determination apparatus 10 may transmit the learning process condition to another apparatus that performs the learning process, and may receive information for configuring the learned model. The information for configuring the learned model is information for identifying the learning model. In the case of a neural network, the number of layers, the type of layer, the connection of nodes between layers, and the weight coefficient of node connection (including threshold values) ) And the type of activation function.

  FIG. 2 is a diagram illustrating a physical configuration of the epilepsy determination apparatus 10 according to the present embodiment. The epilepsy determination device 10 includes a central processing unit (CPU) 10a corresponding to a calculation unit, a random access memory (RAM) 10b corresponding to a storage unit, a read only memory (ROM) 10c corresponding to a storage unit, a communication unit 10d, an input unit 10e, and a display unit 10f. Each of these components is connected to each other via a bus so that data can be transmitted and received. In addition, although the case where the epilepsy determination apparatus 10 is comprised with one computer is demonstrated in this example, the epilepsy determination apparatus 10 may be implement | achieved combining a some computer. Moreover, the structure shown in FIG. 2 is an example, and the epilepsy determination apparatus 10 may have a structure other than these, and it is not necessary to have a part of these structures.

  The CPU 10a is a control unit that performs control related to execution of a program stored in the RAM 10b or the ROM 10c, and calculates and processes data. The CPU 10a is a calculation unit that executes a program for determining epilepsy (epilepsy determination program) based on an image representing an electroencephalogram. The CPU 10a receives various data from the input unit 10e and the communication unit 10d, and displays a calculation result of the data on the display unit 10f or stores it in the RAM 10b or the ROM 10c.

  The RAM 10b can rewrite data in the storage unit, and may be composed of, for example, a semiconductor storage element. The RAM 10b may store an epilepsy determination program executed by the CPU 10a. These are examples, and the RAM 10b may store data other than these, or some of them may not be stored.

  The ROM 10c is capable of reading data out of the storage unit, and may be composed of, for example, a semiconductor storage element. The ROM 10c may store, for example, a search program and data that is not rewritten.

  The communication unit 10d is an interface that connects the epilepsy determination device 10 to another device. The communication unit 10d may be connected to a communication network such as the Internet or a LAN (Local Area Network).

  The input unit 10e receives data input from the user, and may include, for example, a keyboard and a touch panel.

  The display unit 10f visually displays the calculation result by the CPU 10a, and may be configured by an LCD (Liquid Crystal Display), for example. The display unit 10f may display, for example, an image representing an electroencephalogram or a determination result.

  The epilepsy determination program may be provided by being stored in a computer-readable storage medium such as the RAM 10b or the ROM 10c, or may be provided via a communication network connected by the communication unit 10d. In the epilepsy determination device 10, various operations described with reference to FIG. 1 are realized by the CPU 10 a executing the epilepsy determination program. In addition, these physical structures are illustrations, Comprising: It does not necessarily need to be an independent structure. For example, the epilepsy determination device 10 may include an LSI (Large-Scale Integration) in which a CPU 10a, a RAM 10b, and a ROM 10c are integrated.

  FIG. 3 is an example of an image P input to the learned model of the epilepsy determination apparatus 10 according to the present embodiment. The image P is an example of an image cut out from the electroencephalogram data by the generation unit 12. In this example, the electroencephalogram data for 10 seconds is cut out.

  The image P shows an electroencephalogram measured by 21 electrodes of the measuring device 20 and an electrocardiogram waveform. The electroencephalogram measured by the 21 electrodes is the waveform from the top of the graph to the 21st, and the electrocardiogram waveform is the waveform shown at the bottom of the graph.

  The image P includes waveforms indicated by a plurality of colors corresponding to a plurality of electrodes for which electroencephalogram data has been measured. In this example, waveforms drawn adjacent to each other in the vertical direction are shown in different colors, or the color of the waveform is changed so as to correspond to the arrangement of the electrodes, but the selection of the color is arbitrary. By color-coding in this way, it is possible to distinguish between multiple electrodes for which electroencephalogram data has been measured, and to make determinations under the same conditions as specialists diagnose epileptic seizures, and to determine epileptic seizures with higher accuracy Can do.

  FIG. 4 is a first graph showing the determination accuracy of the epilepsy determination apparatus 10 according to the present embodiment. The first graph shows the result of testing whether or not an epileptic seizure can be correctly determined by the epilepsy determination apparatus 10 according to the present embodiment by measuring brain wave data for several tens of hours for each of 24 subjects. In the first graph, the horizontal axis indicates the time width (Time Window) set by the setting unit 15, and the vertical axis indicates the determination accuracy of the learned model in which the learning process is performed with the time width set. The determination accuracy is a true positive value evaluated by the evaluation unit 14. That is, the determination accuracy shown in the first graph is a determination accuracy evaluated based on an index indicating whether or not an image showing an epileptic seizure can be correctly determined if an epileptic seizure appears.

  According to the first graph, when the time width is 0.5 seconds, the median represented by the box plot is about 0, the upper quartile is slightly larger than 0.1, and the maximum value is 0.2. Degree. When the time width is 1 second, the minimum value represented by the box plot is about 0, the lower quartile is about 0.5, the median is slightly larger than 0.7, The quartile is about 0.9, and the maximum value is about 1.0. When the time width is 2 seconds, the minimum value represented by the box plot is about 0, the lower quartile is about 0.4, the median is about 0.7, The quartile is about 0.9, and the maximum value is about 1.0. Also, when the time width is 5 seconds, the minimum value represented by the box plot is about 0, the lower quartile is about 0.5, the median is slightly smaller than 0.7, and the upper side The quartile is slightly smaller than 0.9 and the maximum value is slightly smaller than 1.0. When the time width is 10 seconds, the minimum value represented by the box plot is about 0, the lower quartile is about 0.2, the median is about 0.6, The quartile is slightly smaller than 0.8 and the maximum value is about 1.0.

  As described above, the time width for extracting the electroencephalogram data affects whether or not the learning process of the learning model can be performed appropriately, and may be a factor that fluctuates the determination accuracy. In the epilepsy determination apparatus 10 according to the present embodiment, it is possible to generate a new learned model so as to improve the determination accuracy by adjusting the time width for extracting the electroencephalogram data, and to determine the epileptic seizure with higher accuracy. be able to.

  FIG. 5 is a second graph showing the determination accuracy of the epilepsy determination apparatus 10 according to the present embodiment. The second graph shows the results of testing the brain wave data for several tens of hours for 24 subjects and testing whether the normal state can be correctly determined by the epilepsy determination device 10 according to the present embodiment. In the second graph, the horizontal axis indicates the time width (Time Window) set by the setting unit 15, and the vertical axis indicates the determination accuracy of the learned model in which the learning process is performed with the time width set. The determination accuracy is a true negative value evaluated by the evaluation unit 14. That is, the determination accuracy shown in the second graph is a determination accuracy evaluated based on an index indicating whether it is possible to correctly determine that an epileptic seizure does not appear for an image in which the epileptic seizure does not appear.

  According to the second graph, when the time width is 0.5 second, 1 second, 2 seconds, 5 seconds and 10 seconds, the minimum value represented by the box plot is about 0.97 to 0.99, The side quartile is about 0.98 to 0.99, the median is about 0.99, the upper quartile is about 0.995, and the maximum value is about 1.0.

  Comparing the first graph of FIG. 4 and the second graph of FIG. 5, it can be confirmed that the relationship between the time width for extracting the electroencephalogram data and the determination accuracy differs depending on the index used for the evaluation of the determination accuracy. More specifically, the first graph has a difference that the median value of determination accuracy is about 0 when the time width is 0.5 seconds, whereas the second graph is close to 1.0. In the first graph, the median determination accuracy decreases as the time width is increased from 1 second to 10 seconds, whereas in the second graph, the time width is increased from 1 second to 10 seconds. There is a difference that the median of the determination accuracy increases as the length increases. The epilepsy determination apparatus 10 according to the present embodiment evaluates the determination accuracy of a learned model based on a plurality of indexes, and the electroencephalogram data is improved so as to improve the determination accuracy evaluated based on the plurality of indexes in a well-balanced manner. You may set the time range to cut out. For example, you may set the time width which cuts out electroencephalogram data so that the sum total of the determination accuracy evaluated based on the some parameter | index may be maximized.

  FIG. 6 is a flowchart of epilepsy determination processing executed by the epilepsy determination system 1 according to the present embodiment. First, the brain wave data of the subject is measured by the measuring device 20 (S10). The subsequent processing may be executed after the electroencephalogram data is accumulated, or may be executed sequentially while measuring the electroencephalogram data.

  The generation unit 12 of the epilepsy determination device 10 performs preprocessing by filtering the acquired electroencephalogram data (S11). After that, the generation unit 12 cuts out the electroencephalogram data with a predetermined time width and generates a plurality of images (S12).

  The determination unit 13 of the epilepsy determination apparatus 10 inputs a plurality of images to a learned model such as CNN, and determines whether an electroencephalogram of an epileptic seizure appears (S13). If it is determined that there is an epileptic seizure (S14: YES), the notification unit 17 of the epilepsy determination device 10 notifies that an epileptic seizure has been detected at a predetermined terminal (S15). On the other hand, when it is not determined that there is an epileptic seizure (S14: NO), the epilepsy determination process ends.

  According to the epilepsy determination device 10 according to the present embodiment, brain wave data is cut out with a predetermined time width, a plurality of images representing brain waves are generated, and brain waves of epileptic seizures appear in any of the plurality of images. By making the learned model determine, the determination can be made under the same conditions as the specialist diagnoses the epileptic seizure, and the epileptic seizure can be determined with higher accuracy.

  Further, according to the epilepsy determination system 1 according to the present embodiment, brain wave data is cut out with a predetermined time width to generate a plurality of images representing the brain waves, and the brain wave of an epileptic seizure appears in one of the plurality of images. By making the learned model determine whether or not the epileptic seizure is detected, it is possible to perform the determination under the same conditions as when the specialist diagnoses the epileptic seizure, and the epileptic seizure can be determined with higher accuracy. In addition, when it is determined that an electroencephalogram of an epileptic seizure appears, the burden of the electroencephalogram confirmation work can be reduced by notifying a predetermined terminal.

  FIG. 7 is a flowchart of the learning process executed by the epilepsy determination system 1 according to this embodiment. First, the evaluation unit 14 of the epilepsy determination apparatus 10 inputs a test image to the learned model and evaluates the determination accuracy (S20). The setting unit 15 sets a time width for cutting out the electroencephalogram data based on the evaluation by the evaluation unit 14 (S21).

  The learning unit 16 cuts out the electroencephalogram data with the set time width, executes learning model learning processing, and generates a new learned model (S22). Then, the epilepsy determination device 10 mounts a new learned model in the determination unit 13 (S23). Thus, the learning process ends.

  As described above, the time span for extracting the electroencephalogram data can be adjusted to generate a new learned model so as to improve the determination accuracy, and the epileptic seizure can be determined with higher accuracy. Further, as the learning data accumulates, it is possible to generate a learned model with higher determination accuracy and update the learned model to be implemented in the determination unit 13, so that the operation of the epilepsy determination system 1 is continued. Will be able to determine epileptic seizures with higher accuracy.

  The embodiments described above are for facilitating the understanding of the present invention, and are not intended to limit the present invention. Each element included in the embodiment and its arrangement, material, condition, shape, size, and the like are not limited to those illustrated, and can be changed as appropriate. In addition, the structures shown in different embodiments can be partially replaced or combined.

  DESCRIPTION OF SYMBOLS 1 ... Epilepsy determination system, 10 ... Epilepsy determination apparatus, 10a ... CPU, 10b ... RAM, 10c ... ROM, 10d ... Communication part, 10e ... Input part, 10f ... Display part, 11 ... Acquisition part, 12 ... Generation part, 13 ... determining unit, 13a ... learned model, 14 ... evaluating unit, 15 ... setting unit, 16 ... learning unit, 17 ... notifying unit, 18 ... storage unit, 20 ... measuring device

Claims (10)

An acquisition unit for acquiring the subject's brain wave data;
Generating a plurality of images representing brain waves by cutting out the electroencephalogram data with a predetermined time width;
A determination unit that inputs the plurality of images into a learned model, and determines whether the brain wave of an epileptic seizure appears in any of the plurality of images by the learned model;
An epilepsy determination device comprising: An evaluation unit that inputs a plurality of test images representing an electroencephalogram into the learned model, and evaluates the determination accuracy of the learned model;
A setting unit configured to set the predetermined time width based on the evaluation by the evaluation unit;
Learning a plurality of learning images representing brain waves clipped by the predetermined time width set by the setting unit, and information regarding whether or not the plurality of learning images represent brain waves of epileptic seizures A learning unit that learns a learning model as data for generation and generates a new learned model;
The epilepsy determination apparatus according to claim 1, further comprising: The evaluation unit evaluates the determination accuracy of the learned model based on a plurality of indices.
The epilepsy determination apparatus according to claim 2. The plurality of indicators are:
For an image showing an epileptic seizure, an index indicating whether it can be correctly determined that an epileptic seizure appears,
For an image that does not show an epileptic seizure, an index indicating whether it can be correctly determined that no epileptic seizure appears,
An index indicating whether an image showing an epileptic seizure is erroneously determined not to show an epileptic seizure,
An image indicating whether an image that does not show an epileptic seizure is erroneously determined to show an epileptic seizure,
The epilepsy determination apparatus according to claim 3. The plurality of images include waveforms shown in a plurality of colors corresponding to a plurality of electrodes from which the electroencephalogram data was measured,
The epilepsy determination device according to any one of claims 1 to 4. The learned model is a learned convolutional neural network,
The epilepsy determination apparatus according to any one of claims 1 to 5. The generation unit performs at least one of a low-pass filter, a high-pass filter, and a notch filter on the brain wave data, and then cuts out the brain wave data with the predetermined time width to generate the plurality of images.
The epilepsy determination apparatus according to any one of claims 1 to 6. An epilepsy determination system comprising a measurement device that measures brain wave data of a subject, and an epilepsy determination device that determines epilepsy based on the electroencephalogram data,
The epilepsy determination device
Generating a plurality of images representing brain waves by cutting out the electroencephalogram data with a predetermined time width;
A determination unit that inputs the plurality of images into a learned model, and determines whether the brain wave of an epileptic seizure appears in any of the plurality of images by the learned model;
A notification unit for notifying a predetermined terminal when it is determined by the determination unit that an electroencephalogram of an epileptic seizure appears in any of the plurality of images;
Epilepsy determination system. Acquiring brain wave data of the subject,
Cutting out the electroencephalogram data with a predetermined time width to generate a plurality of images representing electroencephalograms;
Inputting the plurality of images into a learned model, and causing the learned model to determine whether an electroencephalogram of an epileptic seizure appears in any of the plurality of images;
Epilepsy determination method including A computer equipped with an epilepsy determination device
An acquisition unit for acquiring brain wave data of the subject,
A generation unit that cuts out the electroencephalogram data with a predetermined time width and generates a plurality of images representing electroencephalograms, and inputs the plurality of images into a learned model, and the trained model selects any one of the plurality of images. A determination unit that determines whether an EEG of an epileptic seizure appears
Epilepsy determination program to function as