Seizure prediction program, storage medium, seizure prediction device, and method for prediction seizure

Abstract

To provide a novel technique for predicting a seizure with high usefulness.SOLUTION: The present invention relates to a program for causing a computer to function as an acquisition unit and a prediction unit, the acquisition unit acquiring log information including at least one of action information indicating actions of a subject, emotion information input on the basis of subjective feelings of the subject, and seizure information relating to records of seizures occurring in the subject, and the prediction unit predicting a seizure associated with brain activity or mental activity of the subject on the basis of the log information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a seizure prediction program, a storage medium, a seizure prediction device, and a seizure prediction method. [Background technology]

[0002] Epileptic seizures occur without any provocation and are difficult to detect or predict, which significantly reduces patients' quality of life (QOL). To address these issues, research has been conducted on epileptic seizures and similar seizures associated with brain or mental activity (hereinafter referred to simply as "seizures" in this specification).

[0003] For example, Patent Document 1 describes an epileptic seizure prediction technology that detects signs of an epileptic seizure from data generated from electrocardiogram signals. The technology described in Patent Document 1 detects signs of an epileptic seizure based on whether information based on heart rate exceeds a control limit that should be met when the patient is in an interictal period. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] International Publication No. 2020 / 066430 Summary of the Invention [Problem to be solved by the invention]

[0005] While there are known technologies for detecting epileptic seizures and detecting signs of seizures based on biological information such as heart rate, such technologies require the acquisition of biological information. This requires the use of expensive devices to measure the biological information and the placement of electrodes on the subject's body. Furthermore, signs of an epileptic seizure appear in biological information just before the seizure, which poses a problem in that even if signs are detected, there are limited countermeasures that can be taken.

[0006] In light of the above, an object of the present invention is to provide a novel technology for predicting seizures that is highly useful. [Means for solving the problem]

[0007] In order to solve the above problems, the present invention provides a seizure prediction program that causes a computer to function as an acquisition unit and a prediction unit, wherein the acquisition unit acquires log information including at least one of behavioral information indicating the behavior of a subject, emotional information input based on the subject's subjective opinion, and seizure information related to records of seizures that have occurred in the subject, and the prediction unit predicts seizures associated with the subject's brain activity or mental activity based on the log information.

[0008] With this configuration, seizures can be predicted based on log information without using biological information, making it possible to predict seizures based on information that can be obtained without placing the burden of constantly wearing a device on the subject or interfering with the subject's body.

[0009] In a preferred embodiment of the present invention, the acquisition unit acquires at least two of the behavioral information, the emotional information, and the seizure information as the log information, and the prediction unit predicts the seizure of the subject based on the multiple types of log information acquired by the acquisition unit.

[0010] This configuration allows for more accurate prediction of seizures by taking into account multiple combinations of the subject's behavior, emotions, and seizure records.

[0011] In a preferred embodiment of the present invention, the behavioral information includes information indicating the behavior and the timing of the behavior, the emotional information includes information indicating the emotion felt by the subject and the timing of the emotion, the seizure information includes information indicating the timing of a seizure that occurred in the subject, and the prediction unit predicts a seizure based on the timing indicated by the log information.

[0012] With this configuration, future seizures can be predicted from records of the subject's past behavior, emotions, or seizures.

[0013] In a preferred embodiment of the present invention, the prediction unit predicts that a seizure will not occur within a specified time or period after the time indicated by the log information.

[0014] This configuration makes it possible to predict that a seizure will not occur during a specified period, greatly improving the quality of life of epilepsy patients.

[0015] In a preferred embodiment of the present invention, the prediction unit predicts that a seizure will not occur on the specified day based on the behavioral information or emotional information from the day before the specified day.

[0016] With this configuration, the subject can know that a seizure is unlikely to occur on the specified date. By predicting a specified date as the specified period, it is expected that the subject will be able to plan their activities more easily. Specifically, on days when the possibility of a seizure occurring is low, the subject will be able to go out and do other activities with peace of mind, which is expected to have a significant stress reduction effect.

[0017] In a preferred embodiment of the present invention, the prediction unit makes the prediction by estimating whether or not a seizure will occur on the specified day using a seizure interval that indicates the elapsed period since the previous seizure, based on the timing of the seizure information recorded in the past immediately prior to the specified day.

[0018] In a preferred embodiment of the present invention, the prediction unit makes the prediction by estimating whether a seizure will occur at a specified time or within a specified period of time after the time indicated by the log information.

[0019] This configuration makes it easier for the subject to take measures according to the prediction results, such as taking medication or avoiding dangerous objects or actions during periods when seizures are likely to occur.

[0020] In a preferred form of the present invention, for the purpose of making the prediction, the prediction unit inputs the log information into a trained model generated by machine learning, and obtains as an output of the trained model an estimated result of whether or not the subject will have a seizure within a specified time or period after the time indicated by the log information, and the trained model is generated from training data including the subject's log information and information indicating whether or not the subject has had a seizure within a specified time or period after the time indicated by the log information.

[0021] With this configuration, it is possible to predict whether or not a seizure will occur within a specified time or period using a trained model that has been trained based on past performance.

[0022] In a preferred form of the present invention, the trained model is generated by machine learning using a dataset including training data on multiple subjects with the same seizure classification as the subject and training data on the subject.

[0023] This configuration makes it possible to realize more appropriate predictions that reflect the tendencies of the subject. Furthermore, while the amount of training data tends to be small when only the subject's data is used, combining the data of multiple subjects and the subject's data makes it possible to secure a large amount of training data while also reflecting the subject's characteristics.

[0024] In a preferred embodiment of the present invention, the prediction unit predicts a seizure of the subject based on the log information as well as a seizure classification of the subject.

[0025] Such a configuration is expected to achieve more accurate predictions.

[0026] In a preferred embodiment of the present invention, the acquisition unit acquires the behavioral information as the log information, and the behavioral information includes sleep information related to the sleep of the subject.

[0027] It is known that sleep is related to seizures, and this configuration is expected to result in more accurate predictions.

[0028] In a preferred embodiment of the present invention, the acquisition unit acquires the behavioral information as the log information, and the behavioral information includes usage information acquired from background data in a terminal used by the subject.

[0029] With this configuration, the subject can acquire behavioral information without having to input it himself, further reducing the burden on the subject.

[0030] In a preferred embodiment of the present invention, the acquisition unit acquires the behavioral information as the log information, and the behavioral information includes movement information related to a movement speed of the subject.

[0031] In a preferred form of the present invention, the acquisition unit further acquires evaluation information based on the results of an examination or ability test of the subject, and the prediction unit predicts a seizure of the subject based on the evaluation information in addition to the log information.

[0032] Such a configuration is expected to achieve more accurate predictions.

[0033] In order to solve the above problems, the present invention is a seizure prediction device comprising an acquisition unit and a prediction unit, wherein the acquisition unit acquires log information including at least one of behavioral information indicating the behavior of a subject, emotional information input based on the subject's subjective opinion, and seizure information relating to records of seizures that have occurred in the subject, and the prediction unit predicts seizures associated with the subject's brain activity or mental activity based on the log information.

[0034] In order to solve the above-mentioned problems, the present invention provides a seizure prediction method in which a computer including an acquisition unit and a prediction unit acquires, by the acquisition unit, log information including at least one of behavioral information indicating the behavior of a subject, emotional information input based on the subject's subjective opinion, and seizure information relating to a record of seizures that have occurred in the subject, and predicts, by the prediction unit, a seizure associated with the subject's brain activity or mental activity based on the log information.

[0035] In order to solve the above problem, the present invention provides a seizure prediction method that acquires log information including at least one of behavioral information indicating a subject's behavior and the timing of those behaviors, emotional information indicating the subject's subjective emotions and the timing of those emotions, and seizure information relating to a record of seizures that have occurred in the subject, and estimates whether a seizure associated with brain activity or mental activity will occur on a specified day based on the log information from the day before the specified day. [Effects of the Invention]

[0036] According to the present invention, a novel technique for easily predicting seizures can be provided. [Brief explanation of the drawings]

[0037] [Figure 1] FIG. 1 is a block diagram showing the configuration of a system according to an embodiment of the present invention. [Figure 2] FIG. 2 is a hardware configuration diagram of an information processing apparatus and a terminal device according to the embodiment. [Figure 3] 10 is a flowchart showing a seizure prediction procedure according to the present embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0038] The present invention relates to a technology for predicting seizures associated with brain activity or mental activity in a subject. In particular, a seizure in this invention refers to a sudden symptom that repeatedly occurs in the same patient associated with brain activity or mental activity. In this embodiment, epileptic seizures and seizures similar to epileptic seizures (exhibiting similar symptoms), whose symptoms appear in the state of consciousness or motor state, are predicted. Such seizures are classified into focal seizures, generalized seizures, and psychogenic non-epileptic seizures (PNES). Hereinafter, in this specification, such seizures associated with brain activity or mental activity will be simply referred to as "seizures."

[0039] In the present invention, the term "subject" refers to a subject for whom a seizure is predicted. The subject of the present invention is particularly a person who is likely to have a seizure. Specifically, the subject hereinafter refers to an epilepsy patient or a patient with psychogenic nonepileptic seizures who is a subject for seizure prediction. Note that the present invention is intended to present the results of seizure prediction, and is not intended for therapeutic or diagnostic purposes.

[0040] The present invention will now be described in more detail with reference to the accompanying drawings, in which preferred embodiments are shown, but which may be embodied in many different forms and are not limited to the embodiments set forth herein.

[0041] For example, in this embodiment, the configuration, operation, etc. of a seizure prediction device will be described, but similar effects can also be achieved by a system, method, method executed by a device, computer program that causes a computer device to execute a method, etc. that has similar functions. The program may be provided as a non-transitory computer-readable recording medium, or may be provided so as to be downloadable from an external server.

[0042] <1. System configuration> Fig. 1 is a block diagram showing the configuration of a seizure prediction system including a seizure prediction device of this embodiment. As shown in Fig. 1, the seizure prediction system 0 of this embodiment includes a seizure prediction device 1 and a user terminal 2. The seizure prediction device 1 and the user terminal 2 are configured to be able to communicate via a network NW. In this embodiment, the network NW is an IP (Internet Protocol) network, but there are no restrictions on the type of communication protocol, type of network, etc.

[0043] One or more information processing devices 10 (computer devices), such as a general-purpose server or personal computer, can be used as the seizure prediction device 1. Furthermore, a terminal device 9 (computer device), such as a personal computer, smartphone, or tablet terminal, can be used as the user terminal 2. In this embodiment, the seizure prediction device 1 is an information processing device 10 in which a computer program (seizure prediction program) that executes a seizure prediction method is installed.

[0044] Fig. 2(a) is a hardware configuration diagram of the information processing device 10. As shown in Fig. 2, the information processing device 10 has a control unit 101, a storage unit 102, and a communication unit 103, which are used to perform the functions of each unit and each process.

[0045] The control unit 101 has a processor such as a CPU that can execute an instruction set, and executes an OS and programs. The storage unit 102 has a volatile memory such as a RAM capable of storing an instruction set, and a non-volatile recording medium such as an HDD or SSD capable of recording an OS, an attack prediction program, a DBMS (database server), and the like. The communication unit 103 has an interface for physically connecting to a network, and controls communication with the network NW to input and output information.

[0046] Fig. 2(b) is a hardware configuration diagram of the terminal device 9. As shown in Fig. 2, the terminal device 9 has a control unit 901, a storage unit 902, a communication unit 903, an input unit 904, and an output unit 905, which are used to perform the functions of each unit and each process.

[0047] The control unit 901 has a processor such as a CPU that can execute an instruction set, and executes an OS, programs, and the like. The storage unit 902 includes a volatile memory such as a RAM capable of storing an instruction set, and a non-volatile recording medium such as an HDD or SSD capable of recording an OS, programs, and the like. The communication unit 903 has an interface for physically connecting to a network, and controls communication with the network NW to input and output information. The input unit 904 includes an operation input device capable of input processing, such as a touch panel or keyboard, and an audio input device capable of audio input, such as a microphone. The output unit 905 includes a display device capable of display processing, such as a display, and an audio output device, such as a speaker.

[0048] <2. Functional configuration> The seizure prediction device 1 has, as its functional configuration, an acquisition unit 11, a prediction unit 12, and a storage unit 13. This is a specific implementation of software-based information processing using hardware. The user terminal 2 also has input means for the user to input information, and output means including means for outputting to the user and connecting to the network NW. The user terminal 2 is used by the subject or by others who can observe the subject. In this embodiment, the user terminal 2 is mainly assumed to be a device such as a smartphone owned or carried by the subject.

[0049] The acquisition unit 11 acquires at least one of behavioral information indicating the behavior of the subject, emotional information input based on the subject's subjective opinion, and seizure information related to records of seizures that have occurred in the subject, via the user terminal 2. In this embodiment, the acquisition unit 11 acquires behavioral information, emotional information, and seizure information via the user terminal 2. Specifically, it is assumed that the subject inputs this information via the user terminal 2 by selection or free input, and the user terminal 2 transmits the information together with the subject ID via the network NW to the seizure prediction device 1. Hereinafter, records related to the subject that can be used to predict seizures, including seizure information, behavioral information, and emotional information, will be collectively referred to as log information.

[0050] The log information is linked to information about each subject by a subject ID that identifies the subject, and includes time information indicating the time corresponding to each type of record. The time corresponding to each type of record is, for example, date and time information indicating the date and time of the behavior, emotion, seizure, etc. indicated by each record. The acquisition unit 11 acquires log information within a specific time range a predetermined time before the time at which prediction is to be made, and passes it to the prediction unit 12, which will be described later.

[0051] The time period information may be information that does not include the time but specifies the day it belongs to. In this embodiment, a "day" starts from the time the "subject (target person)" wakes up, and a "day" here refers to the period from when the subject wakes up until when they wake up the next day.

[0052] In the present invention, "behavior" refers to observable movements or reactions exhibited by a subject. Therefore, biological information that cannot be observed without using measuring equipment, such as heart rate, brain waves, and breathing, is not included in the behavioral information of the present invention. The behavioral information in the log information includes, for example, sleep information about the sleep of the subject, movement information about the movement of the subject, usage information about the usage history of the user terminal 2 used by the subject, exercise information about the exercise performed by the subject, diet information about the meals performed by the subject, medication information about the medication history of the subject, free time and working hours or the ratio thereof, etc. In addition to the above, information indicating any behavior may be included in the behavioral information in the present invention.

[0053] An example of the behavior information in this embodiment will be described more specifically. Each type of behavioral information includes a unique behavior log ID, time information indicating the date and time of the behavior, and behavior content indicating the type of behavior that the behavioral information represents. In addition, the target ID is used to link the information of the target who performed the behavior.

[0054] The sleep information included in the behavioral information includes the bedtime (date and time) and wake-up time (date and time) as the aforementioned period information. Sleep duration can be calculated from the bedtime and wake-up time, but the sleep duration may also be input separately. Sleep may also be divided into types such as "real sleep" mainly at night and "nap" such as afternoon naps, and input separately. Here, the bedtime and wake-up time refer to the actual start and end times of sleep, but the planned start time (planned bedtime) and planned end time of sleep (planned wake-up time) may also be acquired. The difference between the planned start time of sleep and the actual start time of sleep, and the difference between the planned end time of sleep and the actual end time of sleep may also be used as sleep information.

[0055] In this embodiment, the boundary between sleep days is set to 5:00 a.m. Specifically, sleep that started between 5:00 a.m. on the previous day and 4:59:59 a.m. on the current day is treated as sleep on the "previous day." If a person wakes up during the night, the sleep information up to the point where the person feels they have woken up is counted as one piece of sleep information, without separating the information before and after the awakening. However, the time of the awakening during the night may be recorded as sleep information, and the time when the awakening occurred may be excluded from the sleep time.

[0056] The sleep information also includes information about sleep quality. Specifically, information indicating how one feels when waking up can be used as information about sleep quality. Here, the "feelings when waking up" refers to the subjective feelings about sleep felt by the subject. For example, it is assumed that the subject inputs how one feels when waking up by selecting from multiple options such as "well rested," "fairly rested," "average," "not very rested," and "not at all rested."

[0057] The information relating to sleep quality may also be information representing other indicators such as brain waves during sleep (for example, the time or percentage at which alpha waves, beta waves, theta waves, and delta waves are detected during sleep), waking up during sleep, body movements during sleep, etc. However, the information relating to sleep quality in this embodiment is preferably information that can be obtained without measuring brain waves, body movements, etc.

[0058] The movement information included in the behavioral information includes a movement speed. For example, a movement speed of the user terminal 2 can be identified using a GPS (Global Positioning System) receiver or a sensor provided in the user terminal 2, and this can be used as the movement information. For example, the movement information can include an average movement speed, a maximum movement speed, a minimum movement speed, etc. for a predetermined period, for example, for a day (from waking up to going to bed).

[0059] Among the behavioral information, background data acquired by the user terminal 2 can be used as usage information relating to the usage history of the user terminal 2 used by the subject. For example, the usage time and usage duration (total usage duration in a predetermined period) for each app (or for each app category), the message sending history and number of replies for a messaging app, etc. can be used as usage information. Here, app categories refer to the classification of each app according to its function or purpose, such as tools, SNS, games, music, etc.

[0060] Other examples of usage information include screen-off time, screen-on time, the number of times photos are saved, the number of times videos are saved, and the number of calendar events registered.

[0061] Furthermore, the exercise information included in the behavioral information includes information such as the type, intensity, and duration of exercise performed by the subject. Specifically, for example, the number of times exercised in a predetermined period, the total duration, the number of steps, the distance walked and run, the stride length, the number of floors climbed, the time spent supporting both feet, stability, speed, active energy (energy expenditure during exercise), energy expenditure at rest, the number of falls, etc. In addition to the above, any information that can be input by the user or that can be obtained by a wearable device may be used.

[0062] Furthermore, the dietary information included in the behavioral information includes information on the content and amount of meals eaten by the subject. Among these, for example, the number of meals eaten and calorie intake can also be used as exercise information or dietary information. Furthermore, for example, whether or not a meal was eaten may be received for each of breakfast, lunch, snacks, dinner, and late-night snacks.

[0063] The medication information included in the behavioral information includes information such as the timing at which the subject took medication (time of day, whether morning, noon, or night, or whether before or after a meal, etc.), the type and amount of medication, etc. Furthermore, for example, the number of times a medication was taken per day may also be used as medication information. The medication information is input by the subject or an observer via the user terminal 2, and is acquired by the acquisition unit 11.

[0064] The above-described behavioral information is merely an example, and various types of behavioral information may include any other information. In this embodiment, all of the above information is used to predict a seizure, but only some of the information may be used for prediction.

[0065] In addition, in the present invention, "emotion" refers to a subjective feeling of a subject. Therefore, anything that can be inferred from biological information such as brain activity information or heart rate is not included in the "emotion" of the present invention.

[0066] Emotion information in the log information is information indicating the type and intensity of an emotion expressed in any way, such as the four types of joy, anger, sadness, and pleasure, the eight basic emotions of joy, anticipation, anger, disgust, sadness, surprise, fear, and trust (Plutchik's Wheel of Emotions), or indicators of pleasure, displeasure, and arousal (Russell's Circumplex of Emotions). For example, the type and intensity of an emotion selected from multiple pre-set options may be used as emotion information. Alternatively, only the type of emotion may be used as emotion information, without using the intensity.

[0067] Emotion information may be input only once a day, or may be input at any time (multiple times) when the subject feels an emotion. In this embodiment, once a day, the acquisition unit 11 acquires emotion information that is subjectively selected by the user from nine categories of "happy," "fun," "calm," "tranquil," "bored," "anxious," "tired," "angry," and "sad" regarding the overall emotion of that day.

[0068] The seizure information is one type of log information in the present invention, and is information relating to past seizures that have occurred in the subject. The seizure information includes the date on which the seizure occurred, the time the seizure started and ended, the type of seizure, etc. For example, the type of seizure may be whether or not the subject is conscious and / or whether or not there is a convulsion. The seizure information is input by the subject or an observer via the user terminal 2, and is acquired by the acquisition unit 11.

[0069] In addition to the log information, the acquisition unit 11 of this embodiment further acquires environmental information related to the environment surrounding the subject, and biological information of the subject acquired by a wearable device communicatively connected to the user terminal 2. Like the log information, the environmental information and biological information are linked to the subject information of each subject by a subject ID that identifies the subject, and both include information indicating the time corresponding to various records.

[0070] Possible environmental information includes illuminance, temperature, atmospheric pressure, weather, humidity, etc. The environmental information may be acquired by a sensor included in the user terminal 2, or may be acquired from an external database that provides weather data based on location information of the user terminal 2 obtained by GPS or the like.

[0071] Furthermore, location information indicating the area of ​​stay and activity range information indicating the range of activity may be used as environmental information. Both location information and activity range information can be acquired based on the position information of the user terminal 2 obtained by GPS or the like. Location information is assumed to be information classified by area, such as downtown, outskirts, residential areas, etc., and to indicate which area classification the location indicated by the location information falls into. Furthermore, activity range information may be, for example, the difference between the activity range on weekdays and on holidays.

[0072] The biological information can be any information that can be acquired by a sensor equipped in the wearable device. Examples of biological information include heart rate, activity level, blood pressure, etc. As described above, the present invention predicts seizures primarily based on log information, and biological information can be used as a supplementary measure.

[0073] The acquiring unit 11 also acquires subject information that is pre-stored in the storage unit 13. The subject information includes information such as a subject ID that uniquely identifies the subject, the subject's name, seizure classification, attributes, diagnostic results for the subject, and psychological evaluation results.

[0074] Seizure classification refers to the classification of seizures exhibited by a subject. Seizures associated with brain or mental activity are classified according to the brain activity at the time of the seizure. For example, epileptic seizures cause excessive electrical excitation in the brain, and are classified into "focal seizures (also called partial seizures)" and "generalized seizures" depending on the location and spread of this excitation.

[0075] Furthermore, seizures that do not show abnormal epileptic waves on the EEG and that do not technically constitute "epileptic" seizures but that suddenly cause psychosomatic states similar to those of epileptic seizures are called psychogenic non-epileptic seizures (PNES). In this invention, "seizures" include psychogenic non-epileptic seizures, and "psychogenic non-epileptic seizures" are treated as one type of seizure classification.

[0076] Seizures associated with brain activity or mental activity are classified by patient, and in this embodiment, one of the three categories of "focal seizures," "generalized seizures," and "psychogenic non-epileptic seizures" that occurs in a subject is registered as the seizure classification in the subject information. Note that seizure classification is not limited to the above three categories, and classifications that further subdivide focal seizures and generalized seizures may be used, or an "unclassifiable" seizure category may be added.

[0077] Attributes include the attributes of the subject, such as age or generation, sex, family structure (whether married or not, or whether family members live together, etc.), occupation, years of employment, working hours per week, drinking and smoking habits, recent (e.g., one month) health condition, whether or not they are receiving care, housework and exercise habits, etc. The diagnostic results are the diagnosis given to the subject and test data obtained through tests at the hospital. The psychological evaluation results are the results of interviews and psychological evaluation tests taken by the subject, and are an example of evaluation information in the present invention. For example, evaluation results based on the subject's responses regarding depressive symptoms, QOL, stigma, etc., and ability evaluation results based on the results of ability tests regarding verbal IQ, memory, etc., are included in the evaluation information.

[0078] The prediction unit 12 predicts a seizure in the subject based on the log information acquired by the acquisition unit 11. The prediction unit 12 of this embodiment combines the various types of information acquired by the acquisition unit 11 described above and uses them to predict a seizure. The combination of information to be used can be determined arbitrarily, but it is particularly preferable to use a combination of at least two of behavioral information, emotional information, and seizure information to predict a seizure, such as a combination of behavioral information and emotional information, a combination of behavioral information and seizure information, or a combination of emotional information and seizure information.

[0079] As described above, the log information, environmental information, and biological information each contain date and time information indicating a date and time as information indicating the time period. The prediction unit 12 predicts a seizure at a predetermined time based on the date and time information. More specifically, the prediction unit 12 predicts a seizure that will occur later than the time indicated by the information used for the prediction (log information).

[0080] Specifically, prediction unit 12 makes a prediction using information from a predetermined time before the specified time or specified period for which prediction is to be made. For example, it is assumed that the specified period is a specific day, and log information, environmental information, and biological information of the subject on the day before the specified day that is the target of prediction are used to predict whether or not a seizure will occur in the subject on the specified day (specified period). The period of information used for the prediction and the range of time periods for which the prediction is to be made may be changed as desired. For example, information from the week immediately preceding a specified date may be used to predict whether a seizure will occur on the specified date. Information from one week may also be used to predict whether a seizure will occur in the following week.

[0081] In this regard, subjects who experience repeated attacks often have to live with constant anxiety about attacks, which limits their daily lives. Given this background, providing information such as "attack-free periods" is particularly important in improving the subject's quality of life. Therefore, the prediction unit 12 of this embodiment outputs a prediction result that "a seizure will not occur" (is unlikely to occur) particularly when the possibility of a seizure occurring during a predetermined period, for example, on a specified date, is low.

[0082] In this embodiment, the prediction unit 12 predicts seizures using a trained model generated using training data via machine learning technology. That is, the prediction unit 12 inputs information acquired by the acquisition unit 11 into the trained model, receives an estimated result of whether or not a seizure has occurred at a specified time as the output of the trained model, and outputs a prediction result based on the estimated result. In this embodiment, the memory unit 13 stores the trained model in advance, and the prediction unit 12 inputs various information into the trained model stored in the memory unit 13 and receives the presence or absence of a seizure as the output. The procedure for generating the trained model will be described later.

[0083] The storage unit 13 stores the above-mentioned subject information, log information, environmental information, biological information, etc. The subject information is pre-registered for each subject before receiving information such as log information. The storage unit 13 then receives log information, environmental information, biological information, etc. from the registered subject along with the subject ID as needed, and stores the information in association with the subject information. As a result, various pieces of information are registered in association with the subject and the date and time, and can be used to predict seizures.

[0084] In this embodiment, the storage unit 13 receives and registers various types of information as needed, and the acquisition unit 11 acquires information on the timing required for seizure prediction from the information and passes it on to the prediction unit 12. However, the procedure for acquiring information can be changed as desired. For example, instead of storing information in the storage unit 13, various types of information may be stored in the user terminal 2, and the user terminal 2 may transmit the information required for a specified timing required for prediction to the seizure prediction device 1 each time. In addition, in this embodiment, the storage unit 13 also stores a trained model, which will be described later.

[0085] The above configuration is merely an example, and various functions may be realized by, for example, placing some of the means of each device in another device, and having multiple devices work together. Also, the functions of the seizure prediction device 1 and the user terminal 2 may be provided in a single device, and the input reception of various information such as log information and the prediction of seizures may be performed in a single device.

[0086] <3. Learning process> Next, we will explain how to generate a trained model that takes subject information (especially attributes, diagnostic results, and psychological evaluation results), log information, environmental information, and biometric information as input and outputs an estimation result as to whether a seizure will occur.

[0087] In this embodiment, machine learning is performed on a different model for each seizure classification, and a trained model is generated for each seizure classification. That is, the seizure prediction device 1 generates a trained model that has learned only training data related to focal seizures, a trained model that has learned only training data related to generalized seizures, and a trained model that has learned only training data related to psychogenic non-epileptic seizures, and stores them in the memory unit 13.

[0088] Regarding the type of model, any known classification model can be used. For example, models such as logistic regression, k-nearest neighbor (KNN), decision tree, support vector machine (SVM), and artificial neural network (ANN) can be used. Note that all of these models are widely used in classification problems, and those skilled in the art can understand their mechanisms, so a description thereof will be omitted.

[0089] In this embodiment, a seizure is predicted using a prediction model that outputs a prediction result of whether or not a seizure will occur. Specifically, the prediction model of this embodiment uses, as explanatory variables, subject information as well as log information, environmental information, and biological information within a certain range prior to the specified period (or specified time) for which prediction is to be made, and estimates whether or not a seizure will occur within the specified period, and outputs the result. The prediction unit 12 then receives the estimation result and outputs the prediction result based on it.

[0090] In the learning process, a set of information used as input (explanatory variables) for the model and seizure information that serves as training data for the output is acquired as learning data. Machine learning techniques are widely used, and those skilled in the art can understand the learning procedure, so a detailed explanation will be omitted.

[0091] In this embodiment, a model is generated that outputs a prediction result of whether or not a seizure will occur on a specified date using subject information, a seizure interval determined based on seizure information over a specified period, and behavioral information, emotional information, environmental information, and biological information from the day before the specified date as explanatory variables. Therefore, the training data includes a set of subject information (particularly attributes, diagnostic results, and psychological evaluation results), seizure information for a specific subject on a certain day, seizure information for the most recent specified period (or the period since the previous seizure), behavioral information, emotional information, environmental information, and biological information from the day before. Here, as described above, a "day" in this embodiment refers to the time from when the "subject (target)" wakes up, and a "day" here refers to the period from when the subject wakes up to when they wake up the next day.

[0092] Here, data from multiple different subjects is used as training data. Although a trained model dedicated to a single subject may be generated, in this embodiment, in order to ensure a sufficient amount of training data, sets of subject information, log information, environmental information, and biological information, and seizure information for multiple subjects assigned the same seizure classification are trained.

[0093] It is known that seizure tendency varies from person to person, and the trained model generated by the above procedure may be adjusted for each subject. For example, a trained model may be generated by performing machine learning using a dataset that includes not only the training data described above but also training data obtained from the subject as a test subject. Furthermore, for example, a trained model may be generated by prioritizing training data from time periods when the subject is prone to seizures.

[0094] Alternatively, a trained model may be first generated using training data on multiple subjects other than the subject, and then the model may be further trained using additional training data in which the subject is a subject, thereby adjusting the model to suit the subject.

[0095] For the learning data on the subject, it is assumed that the subject's seizure information, behavioral information, and / or emotional information over a certain period of time prior to the specified date is used as the explanatory variables, and the presence or absence of a seizure on the specified date is used as the objective variable. As described above, the explanatory variables can be not only seizure information, behavioral information, or emotional information, but also any combination of seizure information, behavioral information, emotional information, environmental information, biological information, and subject information.

[0096] The period of time for which the information used as the explanatory variables is to be a certain period or longer is preferable to reflect the subject's tendency toward seizures. In particular, it is preferable that the information (seizure information, behavioral information, or emotional information) used for adjustment to suit the subject be a period longer than the period of the information (seizure information, behavioral information, or emotional information) used in the aforementioned learning involving multiple subjects.

[0097] More specifically, for example, in the aforementioned learning involving multiple subjects, learning is performed using information from the previous day as the explanatory variable and the presence or absence of a seizure on a specified date as the objective variable, while in the adjustment tailored to the subject, additional learning can be performed using information from one month prior to the specified date as the explanatory variable and the presence or absence of a seizure on the specified date as the objective variable. Furthermore, in the adjustment tailored to the subject, all available past data can be used without any particular time period limitation. This allows sufficient information about the subject's seizures to be learned, enabling highly accurate predictions that better reflect the subject's seizure tendencies.

[0098] <4. Prediction processing> Next, the seizure prediction process by the prediction unit 12 will be described with reference to Fig. 3. In this embodiment, seizures are predicted using the trained models that correspond to the three seizure classifications of focal seizures, generalized seizures, and psychogenic non-epileptic seizures and that have been further adjusted for each subject using the method described above. Note that prediction can be performed by any method based on the correlation between seizure information, behavioral information, or emotional information and the presence or absence of a seizure, and the prediction method is not limited to this.

[0099] 3 is a flowchart showing the processing steps for predicting a seizure by the prediction unit 12. First, in step S1, the acquisition unit 11 acquires information to be used for predicting a seizure. The acquired information includes subject information, log information about the subject, environmental information, and biological information, which are the same types of information used to generate the trained model. The timing of the log information, environmental information, and biological information may also be acquired within the ranges used as explanatory variables in generating the trained model.

[0100] In this embodiment, the user terminal 2 transmits log information, environmental information, and biological information for each subject as needed and stores them in the storage unit 13. The acquisition unit 11 acquires information for the required period from the storage unit 13 according to the period to be predicted. In this embodiment, the acquisition unit 11 acquires the log information, environmental information, and biological information, as well as subject information, for the same period as the period used for the adjustment to fit the subject. However, there is no limitation on the acquisition period for the log information, environmental information, and biological information, and the acquisition period may be shorter than the period used for adjusting the model to fit the subject. Note that when adjustment for each subject is not performed, for example, to estimate the presence or absence of a seizure on a specified day using information from the previous day, the acquisition unit 11 acquires the log information, environmental information, and biological information for the day before the specified day, as well as the subject information.

[0101] Next, in step S2, the prediction unit 12 receives the information acquired in step S1 from the acquisition unit 11 and inputs it into the trained model. Here, as described above, a trained model is generated for each subject and stored in the storage unit 13. Therefore, in step S2, the prediction unit 12 references the subject ID and inputs subject information (particularly attributes, diagnosis results, and psychological evaluation results), log information, environmental information, and biological information into the corresponding trained model. Note that if adjustment for each subject is not performed, the prediction unit 12 references the seizure classification in the subject's subject information and inputs the subject information (particularly attributes, diagnosis results, and psychological evaluation results), log information, environmental information, and biological information into the trained model corresponding to that seizure classification.

[0102] Then, in step S3, the prediction unit 12 receives the probability that a seizure will occur in the subject on the specified date and the probability that a seizure will not occur in the subject on the specified date as the output of the trained model. The prediction unit 12 outputs the higher probability as the prediction result. The user terminal 2 receives the output from the prediction unit 12 and outputs the result to the subject by displaying it on a screen, speaking, or the like. The output format may be determined arbitrarily, but for example, the user terminal 2 may display the prediction result as a symbol, such as a "sunny" symbol if the prediction result is that a seizure will occur, or a "rainy" symbol if the prediction result is that a seizure will not occur.

[0103] As described above, the seizure prediction device according to this embodiment can predict a seizure based on easily available information, such as behavioral information or emotional information. The seizure prediction method according to the present invention is a method in which a computer executes the above-described procedures, or a method in which any subject executes the above-described procedures. The seizure prediction method according to the present invention is intended solely to provide a subject with information to help guide their behavior by predicting a seizure, and is not intended for diagnosis or treatment.

[0104] Furthermore, unlike conventional technologies that detect seizures and their symptoms based on biological information, the seizure prediction device according to this embodiment can predict the presence or absence of a seizure not just before a seizure occurs, but for example, within the day, etc. Below, we will explain examples in which a seizure was actually predicted using the seizure prediction device, seizure prediction program, and seizure prediction method of the present invention. [Example]

[0105] (1) Generating a trained model First, using the log information, environmental information, and biological information of multiple subjects, a trained model was generated for each seizure classification using the method described in <3. Learning Processing>. The subjects were 12 epilepsy patients aged 18 years or older who required hospitalization for detailed examination to diagnose epilepsy and determine treatment plans. The data collection period was 667 days, of which seizures associated with brain activity or mental activity were observed on 82 days.

[0106] The explanatory variables used were 368 items, including behavioral information such as sleep duration, wake-up time, average daily movement speed, and maximum daily movement speed, emotional information input by the subject at any time, most recent seizure information indicating the date and time of the previous seizure, biological information measured by a wristwatch-type wearable device, and attribute information indicating the subject's attributes. Note that the elapsed time since the previous seizure may be used instead of or in addition to the most recent seizure information. Specific examples of explanatory variables are as described in the above-mentioned embodiment. In this example, a trained model is generated by performing training for each seizure classification, and no adjustment is made to suit each subject.

[0107] The analysis algorithm used was LightGBM (Light Gradient Boosting Machine). LightGBM is a supervised learning method that classifies explanatory variables according to the target variable. (Reference URL: https: / / lightgbm.readthedocs.io / en / latest / Python-Intro.html)

[0108] (2) Prediction results Using the model created using the above procedure, seizure predictions were performed using test data. The accuracy rate, precision rate, recall rate, specificity rate, and F-measure were calculated from the prediction results, and the accuracy rate was 87.22%, precision rate 54.09%, recall rate 78.57%, specificity 88.51%, and F-measure 62.74%.

[0109] These results showed that it is possible to predict with high accuracy whether or not a seizure occurred on a specified date.

[0110] (3) Evaluation of learning results The contribution of each explanatory variable was evaluated using the SHAP (SHapley Additive exPlanations) method. SHAP uses a trained model and explanatory variables to calculate the SHAP value, which indicates the degree of contribution of the explanatory variables to the estimation results. (Reference URL: https: / / shap.readthedocs.io / en / latest / )

[0111] When the SHAP value was calculated for the trained model according to this embodiment, the following explanatory variables were considered important in the estimation. Interseizure interval (time since last seizure) ·Attribute information ·Sleep information Travel information Psychological evaluation results ·Emotional information

[0112] These results suggest that it is possible to predict whether or not a seizure will occur based on behavioral information such as sleep and movement information, emotional information, and seizure information. [Explanation of symbols]

[0113] 0: Seizure prediction system 1: Seizure prediction device 2: User terminal 11: Acquisition part 12: Prediction section 13: Storage section 10: Information processing device 101: Control unit 102: Storage section 103: Communications Department 9: Terminal device 901: Control unit 902: Storage section 903: Communications Department 904: Input section 905: Output section

Claims

1. A seizure prediction program that causes a computer to function as an acquisition unit and a prediction unit, the acquisition unit acquires log information including at least one of behavioral information indicating the behavior of the subject, emotional information input based on the subject's subjective opinion, and seizure information related to a record of a seizure that has occurred in the subject; The prediction unit is a seizure prediction program that predicts a seizure associated with the subject's brain activity or mental activity based on the log information.

2. the acquisition unit acquires at least two of the behavioral information, the emotion information, and the seizure information as the log information; The seizure prediction program according to claim 1 , wherein the prediction unit predicts the seizure of the subject based on multiple types of log information acquired by the acquisition unit.

3. the behavioral information includes information indicating the behavior and the time of the behavior; the emotion information includes information indicating an emotion felt by the subject and a time when the emotion was felt; the seizure information includes information indicating a time when a seizure occurred in the subject; The seizure prediction program according to claim 1 , wherein the prediction unit predicts a seizure based on a time indicated by the log information.

4. The seizure prediction program according to claim 3 , wherein the prediction unit estimates that a seizure will not occur within a specified time or period after the time indicated by the log information.

5. The seizure prediction program according to claim 4 , wherein the prediction unit predicts that a seizure will not occur on the specified day based on the behavioral information or emotional information on the day before the specified day.

6. The seizure prediction program of claim 3, wherein the prediction unit makes the prediction by estimating whether a seizure will occur on the specified date using a seizure interval indicating the period elapsed since the previous seizure, based on the timing of the seizure information recorded in the past immediately prior to the specified date.

7. The seizure prediction program according to claim 3 , wherein the prediction unit makes the prediction by estimating whether a seizure will occur within a specified time or period after the time indicated by the log information.

8. For the purpose of the prediction, the prediction unit inputs the log information into a trained model generated by machine learning, and obtains, as an output of the trained model, an estimation result of whether or not the subject will experience a seizure within a specified time or a specified period after the time indicated by the log information; The seizure prediction program of claim 7, wherein the trained model is generated from training data including the subject's log information and information indicating whether the subject experienced a seizure within a specified time or period after the time indicated by the log information.

9. The seizure prediction program of claim 8, wherein the trained model is generated by machine learning using a dataset including the training data for a plurality of subjects with the same seizure classification as the subject and the training data for the subject.

10. The seizure prediction program according to claim 1 , wherein the prediction unit predicts a seizure of the subject based on the log information and further on a seizure classification of the subject.

11. the acquisition unit acquires the behavior information as the log information, The seizure prediction program according to claim 1 , wherein the behavioral information includes sleep information regarding the subject's sleep.

12. the acquisition unit acquires the behavior information as the log information, The seizure prediction program according to claim 1 , wherein the behavioral information includes usage information obtained from background data on a device used by the subject.

13. the acquisition unit acquires the behavior information as the log information, The seizure prediction program according to claim 1 , wherein the behavioral information includes movement information regarding the subject's movement speed.

14. The acquisition unit further acquires evaluation information based on the results of an examination or ability test of the subject, The seizure prediction program according to claim 1 , wherein the prediction unit predicts a seizure in the subject based on the evaluation information in addition to the log information.

15. A storage medium storing the seizure prediction program according to any one of claims 1 to 14.

16. An acquisition unit and a prediction unit, the acquisition unit acquires log information including at least one of behavioral information indicating the behavior of the subject, emotional information input based on the subject's subjective opinion, and seizure information related to a record of a seizure that has occurred in the subject; The seizure prediction device, wherein the prediction unit predicts a seizure associated with the subject's brain activity or mental activity based on the log information.

17. A computer including an acquisition unit and a prediction unit, The acquisition unit acquires log information including at least one of behavioral information indicating the behavior of the subject, emotional information input based on the subject's subjective opinion, and seizure information related to a record of a seizure that has occurred in the subject; A seizure prediction method in which the prediction unit predicts a seizure associated with the subject's brain activity or mental activity based on the log information.

18. Acquire log information including at least one of behavioral information indicating the subject's behavior and the timing of the behavior, emotional information indicating the subject's subjective emotions and the timing of the emotions, and seizure information related to a record of seizures that have occurred in the subject; A seizure prediction method that estimates whether a seizure associated with brain activity or mental activity will occur on a specified day based on the log information for the day before the specified day.

Images