Information processing method, program, information processing device, and brain disease determination auxiliary device

Abstract

The present invention proposes a new method for presenting information relating to the presence of a brain disease on the basis of the input of brain waves.　Provided is an information processing method for an information processing device, the information processing method comprising: calculating, on the basis of a time-frequency analysis, time-frequency information in a set time range from brain wave signals acquired from a plurality of electrodes; and calculating a value relating to the presence of a brain disease on the basis of the time-frequency information.

Description

Information processing method, program, information processing device, and brain disease diagnosis assisting device

[0001] The present invention relates to an information processing method and the like.

[0002] Measuring and evaluating electroencephalograms (EEG) is an essential element in assessing the normality of brain function. A typical EEG test requires the attachment of 19 channels of electrodes to the scalp based on the International 10-20 system. Attaching the electrodes requires maintaining low contact impedance between the electrodes and the scalp, requiring skill and experience. Furthermore, EEG tests for epilepsy and other conditions require the continuous attachment of electrodes for extended periods of time to obtain EEG data during seizures, which can place significant physical and psychological strain on the subject. Furthermore, interpreting measured EEG data typically requires several years of specialized training. Therefore, even if EEG data can be measured, the number of EEG interpreters who can utilize the data for diagnosing diseases is limited, and measurement results may not be fully utilized. For example, Non-Patent Document 1 discloses an artificial intelligence model for automated interpretation of clinical EEG data, which is capable of distinguishing between abnormal and normal EEG recordings based on the International 10-20 system and classifying abnormal EEG recordings into categories relevant to clinical decision-making in epilepsy diagnosis. Furthermore, Non-Patent Document 2 discloses a seizure detection device that automatically detects absence seizures based on a one-channel electroencephalogram measured using a wearable headband device.

[0003] Jesper Tveit et al. “Automated Interpretation of Clinical Electroencephalograms Using Artificial Intelligence”, JAMA Neurol. 2023;80(8):805-812. Giorgi Japaridze1 et al. “Automated detection of absence sequences using a wearable electroencephalographic device: a phase 3 validation "study and feasibility of automated behavioral testing", Epilepsia. 2023;64:S40-S46.

[0004] The artificial intelligence model described in Non-Patent Document 1 may be able to distinguish between abnormal EEG recordings in epilepsy, an example of a brain disease, and normal EEG recordings. However, the artificial intelligence model described in Non-Patent Document 1 requires EEGs acquired from 19 channels of electrodes. The seizure detection device described in Patent Document 2 may be able to detect absence seizures, which are a pathological condition of epilepsy. However, absence seizures are not the only pathological condition of epilepsy, and the model is not sufficient for use in clinical practice, for example, to determine whether a subject is suffering from epilepsy, an example of a brain disease.

[0005] The present invention has been made in consideration of the above-mentioned problems, and its purpose is to propose a new method that makes it possible to present information regarding the incidence of brain diseases based on input of electroencephalograms.

[0006] According to a first aspect of the present invention, an information processing method for an information processing device includes calculating time-frequency information for a set time range from EEG signals acquired from a plurality of electrodes based on time-frequency analysis, and calculating a value related to the prevalence of a brain disease based on the time-frequency information. According to a second aspect of the present invention, a program for causing a computer to execute the following causes the computer to calculate time-frequency information for a set time range from EEG signals acquired from a plurality of electrodes based on time-frequency analysis, and calculate a value related to the prevalence of a brain disease based on the time-frequency information. According to a third aspect of the present invention, an information processing device includes a processing unit that calculates time-frequency information for a set time range from EEG signals acquired from a plurality of electrodes based on time-frequency analysis, and calculates a value related to the prevalence of a brain disease based on the time-frequency information. According to a fourth aspect of the present invention, a brain disease assessment assistance device includes an information processing device and a display unit that displays information based on the value related to the prevalence of a brain disease.

[0007] According to the present invention, it is possible to present information about the onset of brain disease based on input of electroencephalograms. In addition, by using a small number of electrodes, it is possible to present information about the onset of brain disease based on input of electroencephalograms acquired through a small number of channels.

[0008] 1 is a block diagram showing an example of the functional configuration of an information processing device. A diagram showing an example of the placement of an electrode group for acquiring electroencephalograms. A flowchart showing an example of the flow of information processing. A diagram showing a specific example of the processing result in density spectrum array conversion processing. A diagram showing a specific example of the processing result in DSA time window extraction processing. A diagram showing an example of an extracted spectrum used for learning of the disease presence / absence determination section. A table showing an example of benchmark evaluation. A graph showing an example of an ROC curve. A table showing an example of benchmark evaluation. A flowchart showing another example of the flow of information processing. A block diagram showing an example of the functional configuration of a brain disease diagnosis assistance device.

[0009] An example of an embodiment of the present invention will be described below with reference to the drawings. In the description of the drawings, the same elements are designated by the same reference numerals, and duplicate descriptions may be omitted. Furthermore, the components described in this embodiment are merely examples, and are not intended to limit the scope of the present invention.

[0010] [Embodiment] Hereinafter, an example of an embodiment for realizing the information processing technology of the present invention will be described.

[0011] 1 is a block diagram showing an example of the functional configuration of an information processing device 1 according to one aspect of the present embodiment. The information processing device 1 may be regarded as an electroencephalogram (EEG) diagnosis support device (EEG diagnosis support device), a brain disease assessment support device (brain disease assessment support device), or the like. The information processing device 1 includes, for example, an EEG signal acquisition unit 110, a density modulated spectral array (DSA) conversion unit 120, a time window extraction unit 130, and a brain disease assessment unit 140. These may be functional units (functional blocks) included in, for example, a processing unit (processing device) or a control unit (control device) of the information processing device 1 (not shown), and are configured with processing circuits such as a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), and a field programmable gate array (FPGA).

[0012] In the following, epilepsy will be taken as an example of a brain disease. However, the brain disease that can be determined by this device is not limited to epilepsy. For example, the brain disease that can be determined may be dementia or the like.

[0013] The electroencephalogram signal acquiring section 110 receives, as input, an electroencephalogram signal acquired by an electroencephalogram acquiring electrode group 5 external to the information processing device 1, for example.

[0014] The EEG acquisition electrode group 5 includes two or more electrodes 5x placed on the subject's head, earlobes, etc. For example, if the number of electrodes in the EEG acquisition electrode group 5 is "N" (where "N" is a natural number equal to or greater than two), the EEG acquisition electrode group 5 includes N electrodes 5A, 5B, 5C, ..., 5N. FIG. 2 shows an example of a method for placing the EEG acquisition electrode group 5. In this figure, electrode placement positions based on the International 10-20 System are indicated by white circles. For example, if the number of electrodes in the EEG acquisition electrode group 5 is "3," electrode 5A may be placed at "Cz," electrode 5B at "A1," and electrode 5C at "A2."

[0015] In this example, an example of acquiring one-channel electroencephalograms using "A1" and "A2" as reference electrodes is illustrated, but the reference electrodes may be placed on the mastoid processes "M1" and "M2", for example. In other words, the number of reference electrodes may be any number as long as it is one or more. The reference electrodes may also be called standard electrodes.

[0016] Furthermore, the number and positions of the head electrodes are not limited to the above example. For example, electrodes may be placed at "C3" and "C4." That is, the number of head electrodes may be any number as long as it is one or more. The head electrodes may also be called search electrodes.

[0017] The reference electrode may be placed on the subject's head (e.g., "Cz"), and the head electrodes may be placed on the earlobes (e.g., "A1" and "A2").

[0018] Hereinafter, the time-series data of potential (EEG signal) acquired by electrode 5A will be referred to as the "channel 1 signal," the EEG signal acquired by electrode 5B as the "channel 2 signal," ..., and the EEG signal acquired by electrode 5N as the "channel N signal." Note that "channel" may be abbreviated as "Ch." Furthermore, the electrode that acquires the "channel x signal" may be referred to as "electrode Ch.x." Furthermore, the potential of the "channel x signal" at time "t" may be referred to as "Ch.x(t)."

[0019] The EEG signal acquisition unit 110 has the function of, for example, receiving an EEG signal as input from the EEG acquisition electrode group 5, buffering the EEG signal for a predetermined period (for example, "180 minutes"), and outputting the EEG signal within the predetermined period to the density spectrum array conversion unit 120.

[0020] The density spectrum array conversion unit 120 has a function of, for example, receiving as input an EEG signal acquired by the EEG signal acquisition unit 110, performing time-frequency analysis on the EEG signal based on the potential difference between the reference electrode and the head-mounted electrode, converting it into a power spectrum (DSA), and outputting the result to the time window extraction unit 130. The DSA may be considered a signal (tensor data) that expresses the amplitude (power) of the EEG signal into multiple channels (e.g., (R, G, B)) through time-frequency analysis. The DSA may also be considered a type of color image data.

[0021] The time window extraction unit 130 has a function of generating an extracted spectrum by extracting the EEG signal from the DSA-processed EEG signal over a time range (time window) of a predetermined width from a predetermined reference point (e.g., the time point of seizure detection), and outputting the extracted spectrum to the brain disease assessment unit 140. The extracted spectrum may be considered a multi-channel signal (tensor data) that expresses the EEG signal in the predetermined time window in the frequency domain. The extracted spectrum may also be a type of color image data.

[0022] The brain disease assessment unit 140 is configured, for example, with a convolutional neural network (CNN) model such as ResNet or a neural network model such as Vision Transformer (ViT). For example, upon receiving a cut-out spectrum as input, the brain disease assessment unit 140 has a function of outputting a probability (e.g., "0" to "1") that serves as a guide for determining the presence or absence of a brain disease in accordance with a pre-trained neural network model. The brain disease assessment unit 140 may also be configured to determine the presence or absence of a disease (e.g., "0" or "1") based on the probability of the presence or absence of a brain disease based on the cut-out spectrum, and output the result.

[0023] Here, the "output" of the judgment result may include not only the display of the judgment result on the device itself (display output), but also, for example, the output of the judgment result to another functional unit on the device itself (internal output), or the output (external output) or transmission (external transmission) of the judgment result to a device other than the device itself (external device).

[0024] [Information Processing Procedure] Fig. 3 is a flowchart showing an example of an information processing procedure in this embodiment. The processing in the flowchart in Fig. 3 is realized, for example, by the processing unit of the information processing device 1 reading out the code of a program stored in a storage unit (not shown) into a RAM (Random Access Memory) (not shown) and executing it.

[0025] Each symbol S in the flowchart in Fig. 3 represents a step. Note that the flowchart described below merely shows an example of the information processing procedure in this embodiment, and other steps may be added or some steps may be deleted. Also, some of the steps in the flowchart may be interchanged and executed.

[0026] First, the EEG signal acquiring unit 110 executes an EEG signal acquiring process (S110). In the EEG signal acquiring process, for example, the EEG signal acquiring unit 110 acquires EEG signals from the EEG acquisition electrode group 5 and accumulates and stores the EEG signals in a buffer (not shown).

[0027] Then, for example, when the EEG signal acquiring section 110 acquires EEG signals for a predetermined period (for example, "180 minutes"), for example, the density spectrum array converting section 120 executes density spectrum array converting processing (S120).

[0028] In the density spectrum array conversion process, the density spectrum array conversion unit 120 calculates a unipolar EEG signal for one channel based on, for example, the potential at the reference electrode and the potential at the head-mounted electrode. For example, when the electrode positions are as shown in FIG. 2, the unipolar EEG signal Vd(t) at time "t" may be calculated using the following formula: Vd(t) = abs(Ch.1(t) - (Ch.2(t) + Ch.3(t)) ÷ 2), where "abs(x)" represents the absolute value of "x."

[0029] The unipolar EEG signal may be considered as time series data of the differential voltage between the average potential of the head electrode and the average potential of the reference electrode. The unipolar EEG signal may be considered as an EEG signal based on unipolar induction. The unipolar EEG signal may be considered as a signal for observing activity at the head electrode location relative to the reference electrode location.

[0030] The density spectrum array transform unit 120 then applies a fast Fourier transform to the monopolar EEG signal at predetermined time intervals (e.g., 5.12 seconds) to calculate the power in each frequency domain at each time interval. The unit then calculates a density spectrum array (DSA) expressed as a color spectrum in which the maximum power domain is (R, G, B) = (255, 0, 0) and the minimum power domain is (R, G, B) = (0, 0, 255). The sampling time interval may be set to overlap at a predetermined interval (e.g., 2.56 seconds).

[0031] FIG. 4 shows an example of a unipolar EEG signal calculated from an EEG signal acquired by the EEG signal acquisition unit 110 and a DSA calculated from the unipolar EEG signal. In this figure, the color spectrum of the DSA (primary colors: "red" indicates maximum power, "blue" indicates minimum power, and "yellow" indicates intermediate power) is converted to a grayscale, where "(R,G,B)=(255,0,0)" is black (0), "(R,G,B)=(0,255,255)" is white (0), and "(R,G,B)=(0,0,255)" is gray (128). In other words, in each region of the DSA, the closer the color is to black (across gray and white), the greater the amplitude (power) of the unipolar EEG signal in that time period and frequency band. The frequency band for the DSA is 0 Hz to 20 Hz.

[0032] 3 , for example, after the density spectrum array conversion process is performed, the time window extracting unit 130 executes a seizure onset detection process (S130). In the seizure onset detection process, the time window extracting unit 130 analyzes a movement sequence of the subject captured in synchronization with the electroencephalogram signal time sequence by an imaging unit (not shown) during electroencephalogram acquisition, and detects a seizure onset (clinical onset), which is, for example, the start of body tremors.

[0033] Body tremors are one of the characteristics of epileptic seizures, but analysis of EEG signals is essential to distinguish them from syncope and psychogenic non-epileptic seizures, which are conditions that also cause body tremors.

[0034] The time window extracting unit 130 may detect the seizure onset based on, for example, an input from the subject via an input unit (e.g., a push button, not shown). The time window extracting unit 130 may also detect the seizure onset based on, for example, spikes or sharp waves detected using an EEG signal or DSA. The time window extracting unit 130 may also detect the seizure onset based on, for example, a time series analysis of the subject's weight shift using a pressure-sensitive sensor (not shown).

[0035] The time window extractor 130 may also receive time information indicating the start point of a seizure via an input unit (not shown), for example.

[0036] In addition, when the brain disease to be diagnosed is dementia, for example, the time point at which cognitive dysfunction (e.g., memory impairment, disorientation, or executive dysfunction) is observed may be analyzed from the subject's conversation sequence acquired by a sound input unit (not shown) and treated as the onset of a predetermined symptom. Furthermore, in epilepsy, for example, abnormal waveforms may occur in areas showing hyperexcitability during periods between seizures (so-called interictal periods). Therefore, for example, the time point at which abnormal waveforms are observed in electroencephalograms during sleep may be analyzed and treated as the onset of a predetermined symptom.

[0037] For example, when the seizure onset detection process is executed, the time window extracting unit 130 executes a DSA time window extracting process (S140). In the DSA time window extracting process, the time window extracting unit 130 extracts a DSA in a predetermined time window (e.g., ±1 minute or ±3 minutes) based on the seizure onset, and generates an extracted spectrum with a normalized magnitude.

[0038] FIG. 5 shows examples of generated cut-out spectra for various time windows. The DSA and cut-out spectra in this figure are shown in grayscale, similar to FIG. 4 . For example, when (A) the time window is ±1 minute and the reference point is (A2) the seizure onset (referred to as "middle 1 / 3" in the evaluation experiment), when (A1) the reference point is -2 minutes before the seizure onset (referred to as "early 1 / 3" in the evaluation experiment), and when (A3) the reference point is +2 minutes before the seizure onset (referred to as "late 1 / 3" in the evaluation experiment), the cut-out spectra obtained from the same DSA are significantly different. Furthermore, when (B) the time window is ±3 minutes and the reference point is the seizure onset (referred to as "full" in the evaluation experiment), the cut-out spectrum appears similar to (A) due to the wide time window. It appears that the amount of information is greater than when the time window is ±1 minute. Note that the extracted spectrum may be extracted using a time window and reference point other than those described above.

[0039] Returning to FIG. 3 , for example, after the DSA time window extraction process is performed, the brain disease assessment unit 140 executes a disease presence / absence determination process (S150). In the disease presence / absence determination process, the brain disease assessment unit 140 receives, for example, the generated extracted spectrum as input and then executes an inference process according to pre-trained weights. The brain disease assessment unit 140 then calculates, for example, a probability of disease presence (e.g., a value between "0" corresponding to no disease and "1" corresponding to the presence of disease). The disease presence / absence probability may also be referred to as the "probability of disease" or "probability of disease."

[0040] The brain disease assessment unit 140 may, for example, perform threshold processing on the calculated probability of disease presence or absence, thereby outputting the presence or absence of disease as a binary value such as "0" or "1." For example, the brain disease assessment unit 140 may output "1" if the probability of disease presence or absence is equal to or greater than a preset threshold, and output "0" if not. In other words, the user may be able to change the criteria for determining the presence or absence of disease by setting a threshold.

[0041] The brain disease assessment unit 140 then outputs, for example, the calculated probability of disease presence or absence or a binarized disease presence / absence assessment result (S160). Note that the disease presence / absence assessment result may include the unipolar EEG signal or DSA used for the assessment, information on the seizure onset and time window, the generated extracted spectrum, etc.

[0042] For example, when the disease presence / absence determination result output process is executed, the information processing device 1 determines whether or not to terminate the process (S170). For example, if it is determined to terminate the process based on a user operation (an example of a user input) on an operation unit (an example of an input unit) not shown (S170: YES), the information processing device 1 terminates the process. If it is determined not to terminate the process (S170: NO), the information processing device 1 returns the process to, for example, the EEG signal acquisition process.

[0043] For example, if a seizure starting point is not detected during the time that the EEG signal is acquired during the seizure starting point detection process, the information processing device 1 may, for example, return the process to the EEG signal acquisition process.

[0044] Furthermore, for example, if multiple seizure onset points are detected in the seizure onset detection process, the time window extraction unit 130 may, for example, set reference points based on the respective seizure onset points and generate multiple extracted spectra. The brain disease assessment unit 140 may then output a disease presence / absence probability or a disease presence / absence determination result for each of the generated extracted spectra based on the multiple extracted spectra. The brain disease assessment unit 140 may, for example, output the average value of the disease presence / absence probabilities calculated for the multiple extracted spectra as the final disease presence / absence probability. The brain disease assessment unit 140 may, for example, output the voting result of the disease presence / absence determination calculated for the multiple extracted spectra as the final disease presence / absence determination.

[0045] Figure 3 shows the inference processing in the brain disease assessment unit 140, but the learning processing in the brain disease assessment unit 140 can be realized, for example, by labeling the extracted spectra generated in accordance with S110 to S140 and using them as training data.

[0046] Figure 6 shows examples of extracted spectra from epileptic and non-epileptic seizures used as training data. The extracted spectra in this figure are shown in grayscale, as in Figure 4. Epileptic and non-epileptic seizures, which are difficult to distinguish at first glance from body movements (tremors), have significantly different characteristics when comparing the extracted spectra, which are the results of time-frequency analysis of EEG signals.

[0047] In the learning process of the brain disease assessment unit 140, transfer learning may be performed using a pre-trained network that has been trained in advance using another image database, etc. In addition, in the learning process of the brain disease assessment unit 140, learning may be performed using only the extracted spectra that are training data.

[0048] [Evaluation Experiment Results] Figure 7 shows a table of evaluation results of disease presence / absence determination results based on various head-mounted electrodes and time windows. Note that "A1" and "A2" were used as reference electrodes in all experiments. Regarding head-mounted electrodes, "C3-C4" indicates that measurements were taken using electrodes placed at positions "C3" and "C4" based on the International 10-20 System. The same applies to other electrodes such as "Fp1-Fp2" and "O1-O2." In the evaluation experiments, EEG signal data for temporal lobe epilepsy and psychogenic non-epileptic seizures from patients hospitalized in the epilepsy department of the hospital to which the inventor belongs between 2014 and 2022 and undergoing video EEG monitoring were collected. Training data included 40 patients with temporal lobe epilepsy (91 seizures) and 40 patients with psychogenic non-epileptic seizures (82 seizures) from 2014 to 2020, while test data included 8 patients with temporal lobe epilepsy (15 seizures) and 11 patients with psychogenic non-epileptic seizures (18 seizures) from 2021 to 2022. The brain disease assessment unit 140 was configured using ResNet34, a type of CNN. In addition, during the DSA time window extraction process, the extracted spectrum was normalized to 224 x 224 pixels to match the input to ResNet34. In other words, the extracted spectrum can be said to be a "224 x 224 x 3" dimensional spatiotemporal feature based on the EEG signal. The threshold for determining the presence or absence of a disease was set to "0.5."

[0049] In the evaluation experiment, the AUC (Area Under the Curve), accuracy, sensitivity, and specificity were calculated based on the results obtained under each condition and used for evaluation. From this table, it can be seen that the highest accuracy of 87.9% was achieved with the head electrode "Cz" and the time window "middle 1 / 3." The accuracy of a non-trained reader is estimated to be around 50%. Therefore, by using the results of this information processing device as diagnostic auxiliary information, it is expected to provide a simple measurement method for differentiation using emergency EEG and outpatient EEG.

[0050] Figure 8 shows the ROC curve for the head electrode "Cz" and time window "middle 1 / 3" that achieved the highest accuracy. This ROC curve shows that the increase in the true positive rate is significantly greater than the increase in the false positive rate. In other words, the ROC curve demonstrates the high usefulness of this method.

[0051] Figure 9 shows a table of the evaluation results of brain function assessment using EEG signal data for generalized epilepsy and psychogenic non-epileptic seizures. The table of evaluation results can be interpreted in the same way as in Figure 7 . From this table, it can be seen that the highest accuracy of "79.9%" was achieved with the head electrode "Cz" and the time window "middle 1 / 3." It was also confirmed that similarly good results were obtained for frontal lobe epilepsy and other conditions, although not shown. This demonstrates that this method works effectively regardless of the type of epilepsy.

[0052] [Actions and Effects of the Embodiment] The information processing device 1 calculates time-frequency information (e.g., a cut-out spectrum) within a set time range from EEG signals acquired from multiple electrodes based on time-frequency analysis, and calculates a value related to the presence of a brain disease (e.g., a probability of the presence or absence of a brain disease, or a binary value indicating the presence or absence of a disease) based on the time-frequency information. The time-frequency information within a set time range is calculated from EEG signals acquired from multiple electrodes based on time-frequency analysis. Then, a value related to the presence of a brain disease is calculated based on the calculated time-frequency information. This makes it possible to present information related to the presence of a brain disease. Furthermore, by using a small number of electrodes, it becomes possible to present information related to the presence of a brain disease based on EEG input acquired from a small number of channels.

[0053] Furthermore, the value relating to the incidence of a brain disease may include the probability of the incidence of the brain disease, in which case the probability of the incidence of the brain disease can be presented.

[0054] Furthermore, the EEG signals may be acquired from one or more head electrodes placed on the head (for example, one or more electrodes placed on the Cz or other parts of the head) and one or more reference electrodes (for example, one or more reference electrodes placed on the earlobes, mastoid processes, etc.). This makes it possible to calculate time-frequency information within a set time range based on time-frequency analysis from the EEG signals acquired from the one or more head electrodes placed on the head and one or more reference electrodes.

[0055] Alternatively, the head electrode may be a single electrode located at Cz in the International 10-20 system. This allows time-frequency information within a set time range to be calculated based on time-frequency analysis from EEG signals acquired from a single head electrode located at Cz in the International 10-20 system and one or more reference electrodes. In this case, only one electrode, Cz, is required to be located on the head, allowing time-frequency information to be appropriately calculated with a small number of electrodes.

[0056] The time-frequency analysis may be density spectrum array analysis, which makes it possible to appropriately calculate time-frequency information in a set time range based on the density spectrum array analysis.

[0057] Furthermore, the brain disease may be a disease accompanied by a predetermined symptom (for example, seizures if the brain disease is epilepsy, or cognitive impairment if the brain disease is dementia), and the set time range may be a range that uses the start point of the predetermined symptom as a reference point. Time-frequency information is calculated within the range that uses the start point of the predetermined symptom as a reference point, and based on the calculated time-frequency information within the range that uses the start point of the predetermined symptom as a reference point, it is possible to calculate a highly accurate value related to the presence of the brain disease.

[0058] The set time range may be a predetermined range before and after the reference point. By using the time-frequency information in the predetermined range before and after the reference point, it is possible to calculate a highly accurate value relating to the incidence of a brain disease.

[0059] Furthermore, the time-frequency information may be normalized based on the frequency band resolution of the density spectrum array analysis, and the value related to the brain disease incidence may be calculated based on the normalized time-frequency information. By using the time-frequency information normalized based on the frequency band resolution of the density spectrum array analysis, it is possible to calculate a highly accurate value related to the brain disease incidence.

[0060] Furthermore, the calculation of the value related to the incidence of a brain disease may be performed using a neural network model. This makes it possible to easily calculate the value related to the incidence of a brain disease using the neural network model. The neural network model may be a ResNet model.

[0061] The brain disease may also include epilepsy (various types of epilepsy, including temporal lobe epilepsy, frontal lobe epilepsy, and generalized epilepsy). This makes it possible to present information about epilepsy. Furthermore, by using a small number of electrodes, it becomes possible to present information about epilepsy based on the input of electroencephalograms acquired through a small number of channels.

[0062] [Modifications of the Embodiment] (1) In the above embodiment, the brain disease assessment unit 140 is configured as a neural network model, but this is not limiting. For example, the brain disease assessment unit 140 may be configured as a statistical machine learning model such as an SVM (support-vector machine). Furthermore, the brain disease assessment unit 140 may, for example, receive a cut-out spectrum as input, convert the spectrum into a lower-dimensional feature vector using a pre-trained ViT or the like, and then use a machine learning model such as an SVM to determine the converted feature vector. In other words, the brain disease assessment unit 140 may be configured as any mathematical model that can calculate a probability of disease presence or absence or binary information on disease presence or absence when it receives a cut-out spectrum as input.

[0063] (2) In addition to the aforementioned distinction (differentiation) between epilepsy and psychogenic non-epileptic seizures, for example, it may be possible to distinguish between epilepsy and syncope. In this case, for example, cut-out spectra from epileptic seizures (e.g., epileptic loss of consciousness, convulsions) and syncope (e.g., syncope loss of consciousness, convulsions) may be used as training data. Although not shown in the figure, a comparison of these cut-out spectra reveals different characteristics, which can be used to distinguish between epilepsy and syncope.

[0064] (3) In the above embodiment, density spectrum array analysis is used as the time-frequency analysis, but this is not limiting. For example, wavelet analysis may be used as the time-frequency analysis. This method is also applicable to various brain diseases.

[0065] In density spectrum array analysis, accuracy may decrease depending on the duration of the seizure. As a result of testing conducted by the present inventors, for example, with regard to epilepsy, density spectrum array analysis can ensure sufficient accuracy for epilepsy with a relatively long seizure duration (e.g., temporal lobe epilepsy, in which the seizure duration lasts for several tens of seconds), but accuracy may decrease compared to the above for epilepsy with a relatively short seizure duration (e.g., generalized epilepsy, in which the seizure duration lasts for several seconds). However, as a result of performing a learning process in the brain disease assessment unit 140 using an extracted spectrum generated by applying wavelet analysis, sufficient accuracy was obtained regardless of the type of epilepsy.

[0066] FIG. 10 is a flowchart showing an example of the information processing procedure in this modified example. The diagram is viewed in the same way as in FIG. 3, and steps similar to those in FIG. 3 are assigned the same reference numerals, and a repeated explanation will be omitted. After S110, the processing unit of the information processing device 1 performs wavelet analysis processing (S220). Specifically, for example, a wavelet transform is performed on the monopolar EEG signal described above, and the transition of frequency characteristics within the measurement time is plotted on a time-scale plane to generate an image. The processing unit of the information processing device 1 then proceeds to S130.

[0067] For example, the training process of the brain disease assessment unit 140 may be performed by applying both density spectrum array analysis and wavelet analysis. Specifically, for example, the training process of the brain disease assessment unit 140 may be performed using a cut-out spectrum generated by applying density spectrum array analysis (first trained model), and the training process of the brain disease assessment unit 140 may be performed using a cut-out spectrum generated by applying wavelet analysis (second trained model). Furthermore, during inference, for example, the cut-out spectrum generated by applying density spectrum array analysis and wavelet analysis may be input to the corresponding trained models, so that the output of each model can be referenced.

[0068] [Example] Next, an example of a brain disease diagnosis assistance device (brain disease diagnosis support device) to which the above-described information processing device 1 is applied or which includes the above-described information processing device 1 will be described. This device may be configured as a terminal, electronic device (electronic device), or the like. Here, an example of an epilepsy diagnosis assistance device will be described as an example of a brain disease. However, examples to which the present invention can be applied are not limited to this, and the present invention may also be applied to a diagnosis assistance device for other brain diseases such as dementia.

[0069] 11 is a diagram showing an example of the functional configuration of the epilepsy assessment assistance device 10. The epilepsy assessment assistance device 10 includes, for example, a processing unit 100, a storage unit 200, an EEG acquisition electrode group 5, an operation unit 310, a display unit 320, a communication unit 330, and an imaging unit 340.

[0070] The processing unit 100 is a processing device that comprehensively controls each part of the epilepsy assessment support device 10 in accordance with various programs such as system programs stored in the memory unit 200 and performs various processes related to video editing processing, and is configured with processing circuits such as a CPU, GPU, DSP, ASIC, and FPGA.

[0071] The processing unit 100 has, as its main functional units, for example, an EEG signal acquisition unit 110, a density spectrum array conversion unit 120, a time window extraction unit 130, a brain disease assessment unit 140, and a display control unit 150. For example, the display control unit 150 has a function of, upon receiving the output (assessment result) of the brain disease assessment unit 140, visualizing the assessment result and displaying it on the display unit 320. The other functional units correspond, for example, to the functional units included in the information processing device 1 in FIG. 1 .

[0072] The storage unit 200 is a storage device configured to include a memory circuit such as a ROM (Read Only Memory), a RAM, or a flash memory, a hard disk device, a magneto-optical disk device, or the like.

[0073] The storage unit 200 stores, for example, a brain disease diagnosis assistance program 210 and an electroencephalogram signal temporary storage unit 220 .

[0074] The brain disease diagnosis assistance program 210 is, for example, a program that is read by the processing unit 100 and executed as brain disease diagnosis assistance application processing.

[0075] The EEG signal temporary storage unit 220 is, for example, a buffer that stores the EEG signals acquired by the EEG signal acquisition unit 110. Note that the EEG signal temporary storage unit 220 may store imaging information (video information) acquired by the imaging unit 340 while synchronizing the time with the EEG signals.

[0076] The operation unit 310 includes an input device, such as an operation button or an operation switch, that allows the user to input various operations to the epilepsy assessment assistance device 10. The operation unit 310 may include, for example, a touch panel (not shown) that is integrated with the display unit 320, and this touch panel may function as an input interface between the user and the epilepsy assessment assistance device 10. The operation unit 310 may output, for example, an operation signal in accordance with a user operation to the processing unit 100. The operation unit 310 may also include an input device that accepts sound (including voice) input as user input.

[0077] The display unit 320 is a display device configured to include, for example, an LCD (Liquid Crystal Display) or an OELD (Organic Electro-luminescence Display), and performs various displays based on display signals output from the display control unit 150.

[0078] The display unit 320 may display information based on values ​​related to epilepsy under the control of the display control unit 150. For example, various types of information may be displayed, such as the probability of epilepsy being present or absent calculated by the brain disease assessment unit 140, information on values ​​(numerical information) such as binary values ​​indicating the presence or absence of the disease, and information on images (including graphs, etc.) based on these values ​​(image information). Furthermore, the display unit 320 may display time-frequency information within a set time range under the control of the display control unit 150. For example, information such as a cut-out spectrum (e.g., the one shown in FIG. 5 ) generated by the time window cut-out unit 130 may be displayed.

[0079] The epilepsy assessment assistance device 10 may transmit various types of information to a display device (display device) provided separately from the epilepsy assessment assistance device 10, and the display device may display the information received from the epilepsy assessment assistance device 10. Alternatively, the information processing device 1 may transmit various types of information to a display device, and the display device may display the information received from the information processing device 1.

[0080] The communication unit 330 is a communication device for transmitting and receiving information used within the device with an external information processing device. The communication method of the communication unit 330 can be various methods, such as a wired connection via a cable conforming to a predetermined communication standard such as Ethernet or USB (Universal Serial Bus), a wireless connection using a wireless communication technology conforming to a predetermined communication standard such as Wi-Fi (registered trademark) or 5G (fifth generation mobile communication system), or a connection using short-range wireless communication such as Bluetooth (registered trademark). The communication unit 330 may transmit various information to an external device or receive various information from an external device under the control of the processing unit 100.

[0081] The imaging unit 340 is an imaging device configured with an imaging element (semiconductor element) such as a CCD (Charge Coupled Device) image sensor or a CMOS (Complementary MOS) image sensor. The imaging unit 340, for example, forms an image of light emitted from an object to be imaged on the light-receiving plane of the imaging element using a lens (not shown), and converts the brightness of the image into an electrical signal through photoelectric conversion. The converted electrical signal is converted into a digital signal by, for example, an A / D (Analog-Digital) converter (not shown), and output to the processing unit 100. Note that the imaging unit 340 is not necessary.

[0082] The processing unit 100 of the epilepsy diagnosis support device 10 performs brain disease presence / absence determination processing, for example, in accordance with a brain disease diagnosis support program 210 stored in the storage unit 200. The brain disease presence / absence determination processing may be executed, for example, in accordance with the flowchart of FIG.

[0083] [Functions and Effects of the Example] The epilepsy assessment assistance device 10 of this example can achieve the same functions and effects as the above-described embodiment. The epilepsy assessment assistance device 10 may also include a display unit 320 that displays a value related to the incidence of a brain disease, thereby enabling a user to check the value related to the incidence of a brain disease. The display unit 320 may also display time-frequency information within a set time range, thereby enabling a user to check the time-frequency information within the set time range.

[0084] REFERENCE SIGNS LIST 1 Information processing device 5 Electroencephalogram acquisition electrode group 10 Epilepsy diagnosis assistance device 110 Electroencephalogram signal acquisition unit 120 Density spectrum array conversion unit 130 Time window extraction unit 140 Brain disease diagnosis unit

Claims

1. An information processing method for an information processing device, comprising: calculating time-frequency information within a set time range based on time-frequency analysis from EEG signals acquired from a plurality of electrodes; and calculating a value related to the incidence of a brain disease based on the time-frequency information.

2. The information processing method according to claim 1, wherein the value includes a probability of suffering from the brain disease.

3. The information processing method according to claim 1, wherein the electroencephalogram signal is acquired from one or more head electrodes and one or more reference electrodes placed on the head.

4. The information processing method according to claim 3, wherein the head electrode is one, and is an electrode placed at Cz in the International 10-20 system.

5. The information processing method according to claim 1, wherein the time-frequency analysis is density spectrum array analysis.

6. The information processing method according to claim 5, wherein the brain disease is a disease accompanied by a predetermined symptom, and the set time range is a range having the onset of the predetermined symptom as a reference point.

7. The information processing method according to claim 6, wherein the set time range is a predetermined range before and after the reference point.

8. The information processing method according to claim 5, wherein the time-frequency information is normalized based on a frequency band resolution of the density spectrum array analysis, and the value is calculated based on the normalized time-frequency information.

9. The information processing method according to claim 1, wherein the time-frequency analysis is wavelet analysis.

10. The information processing method according to claim 9, wherein the brain disease is a disease accompanied by a predetermined symptom, and the set time range is a range having the onset of the predetermined symptom as a reference point.

11. The information processing method according to claim 10, wherein the set time range is a predetermined range before and after the reference point.

12. The information processing method according to claim 1, wherein the calculation of the value is performed using a neural network model.

13. The information processing method according to claim 12, wherein the neural network model is a ResNet model.

14. An information processing method according to any one of claims 1 to 13, wherein the brain disease includes epilepsy.

15. A program for causing a computer to perform the following steps: calculate time-frequency information within a set time range based on time-frequency analysis from EEG signals acquired from multiple electrodes; and calculate a value related to the incidence of a brain disease based on the time-frequency information.

16. An information processing device having a processing unit that calculates time-frequency information within a set time range based on time-frequency analysis from EEG signals acquired from multiple electrodes, and calculates a value related to the incidence of a brain disease based on the time-frequency information.

17. A brain disease diagnosis assistance device comprising: the information processing device according to claim 16; and a display unit that displays information based on the value.

18. The brain disease diagnosis assistance device according to claim 17, wherein the display unit displays the time-frequency information.

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