JPWO2019163836A1 - EEG analysis method, program, and EEG analyzer that support epilepsy diagnosis - Google Patents

Abstract

Obtain brain wave signals corresponding to the plurality of electrodes installed on the head, perform sample entropy analysis on the brain wave signals, calculate sample entropy scores corresponding to each of the plurality of electrodes, and calculate a plurality of sample entropy scores based on the sample entropy scores. It is determined whether or not each part of the brain corresponding to the electrode corresponds to the epileptic seizure onset or epileptogenic part.

Description

  The present invention relates to an electroencephalogram analysis method and an electroencephalogram analysis device that support epilepsy diagnosis.

  Epilepsy is an abnormal neural activity that occurs in a network of brain cells (cell bodies), and is a disease that causes seizures such as convulsions and impaired consciousness. About 1% of the population is said to have epilepsy regardless of race or gender, and there are also about 1 million patients in Japan. Of these, seizures in about 70% of patients can be suppressed by selecting appropriate antiepileptic drugs, but seizures in the remaining patients cannot be suppressed. Such uncontrollable pathology is called "refractory epilepsy" .Particularly, in epilepsy, which occurs when a part of the brain is abnormally excited, the abnormally excited lesions during an attack become epileptic. Surgery has been performed to reduce or cure seizures by resection.

  In the brain, it is very difficult to distinguish between “normal part” and “abnormal part”. In order to evaluate the abnormal part of the epileptogenic part, not only evaluation of brain images such as MRI and medical examinations, but also In recent years, evaluation methods based on analysis using brain wave signals have been studied and developed.

  EEG changes reflecting human physiological activities, and analyzing EEG signals is useful for determining the presence or absence of diseases such as brain metabolic and inflammatory brain disorders, sleep disorders, and epilepsy. It is widely used in medical field diagnosis and medical equipment. Non-Patent Document 1 discloses that in epilepsy diagnosis, epileptic activity is determined by determining the presence or absence of epileptic discharge in an intermittent epileptic seizure. The epileptic discharge in the intermittent epileptic seizure is an activity that discontinuously protrudes from the background activity, and it is described from the findings of the electroencephalogram that a doctor interprets spike waves, acute waves, and slow waves to diagnose epilepsy . It has also been pointed out that it is necessary to pay attention to performing unnecessary treatment by misreading EEG.

  Non-Patent Literature 2 discloses that in epilepsy diagnosis, localization diagnosis of epilepsy is performed in combination with the distribution of epileptic discharge in EEG, medical history / neural image processing, long-term video monitoring, magnetoencephalography (MEG), and the like. Being done. It is important to find the onset of epileptic seizures, but many document that it may be difficult to find the onset of epileptic seizures due to the effects of electromyograms and body movements .

  Nonpatent Literature 3 discloses, in the local diagnosis of epilepsy surgery medical care, in order to accurately determine the epileptic focus when excision of the epileptic focus, in recent years, brain regions showing epileptic abnormal waves from intracranial EEG analysis Among them, a region showing abnormal high frequency oscillation (HFO) at the time of epileptic seizure or intermittent epileptic seizure is found, and a strong association with epileptic focus to be resected is pointed out. This is a new technique for determining the focus of the intracranial electroencephalogram of the HFO, and the HFO (60 Hz to 250 Hz) at the time of an epileptic seizure is captured using multiple band frequency analysis, which is a time-frequency analysis. What I paid attention to. It is described that as a result of applying this method to a magnetoencephalogram during an epileptic seizure, useful results were obtained during an epileptic seizure.

  Patent Literature 1 discloses that in an epilepsy diagnosis, as an analysis using an electroencephalogram signal, an error signal between a value obtained by simulating a Duffing oscillator representing nonlinear vibration in chaos dynamics of a mechanical vibrometer and an electroencephalogram signal is minimized. It is described that a temporal change of the obtained parameter C between the normal time and the time of epileptic seizure is calculated, and the normal time and the time of epileptic occurrence can be evaluated by the parameter C. Note that the parameter C corresponds to the nonlinear strength of the nonlinear oscillator, and that the parameter C converges to 0 means that the nonlinearity is lost. It is disclosed that at the onset of epilepsy, this non-linearity leads to disappearance, that is, a phenomenon such as "fluctuation" which is not a constant rhythm is no longer observed.

JP 2015-47452 A

Hiroshi Shigeto, “EEG Test (Special Issue: Latest Knowledge of Basic and Clinical Research: Diagnosis of Epilepsy)”, Nihon Rinsho 72, 5th, Nihon Rinsho Co., Ltd., May 2014, pp. 809-817 Shozo Tobimatsu, "Tests for Diagnosis of Epilepsy", Journal of the Japanese Society of Internal Medicine, Vol. 105, No. 8, Japan Society of Internal Medicine, August 10, 2016, pp. 1366-1374. Koji Iida et al., “Frequency Analysis of Magnetoencephalogram Waveforms During Epileptic Rhythmic Waves”, Clinical EEG 52, No. 3, Nagai Shoten Co., Ltd., published March 1, 2010, pp. 135-144.

  The method of diagnosing the epileptic lesion (epileptic part or epileptic seizure onset (part where epileptic seizure activity starts)) by reading EEG is based on the physician's expertise, skill, experience, etc. There is no denying the possibility that there will be variations in the identification of Also, a method of analyzing an electroencephalogram signal to evaluate an epileptic lesion is not sufficiently established as an analysis method, and there is room for improvement.

  Although it is important to identify the onset of epileptic seizures, advanced medical care techniques are required for doctors to read brain waves and make a local diagnosis during the epileptic seizure immediately before the epileptic seizure. Furthermore, it is very difficult to analyze the electroencephalogram signal to evaluate the epileptic seizure onset.

  In the past, epileptic seizures could not be diagnosed / evaluated unless epileptic seizures were caused, so patients (subjects) would have prolonged epileptic seizures for localization of lesions. Waiting for the patient, the burden was great for the patient (subject) and the doctor.

  An object of the present invention is to provide an electroencephalogram analysis method and an electroencephalogram analysis device that support epilepsy diagnosis, which enables simple and objectively stable determination of epilepsy lesions.

  According to an aspect of the present invention, there is provided an electroencephalogram analysis method for supporting epilepsy diagnosis, wherein an electroencephalogram signal corresponding to a plurality of electrodes installed on a head is obtained, and the electroencephalogram signal is subjected to sample entropy analysis, Calculate a sample entropy score corresponding to each of the plurality of electrodes, and based on the sample entropy score, each brain site corresponding to the plurality of electrodes corresponds to an epileptic seizure initiation part or epileptic part It is determined whether or not.

  According to another aspect of the present invention, there is provided an electroencephalogram analyzer for assisting epilepsy diagnosis, wherein an electroencephalogram signal acquisition unit for acquiring electroencephalogram signals corresponding to a plurality of electrodes installed on the head, A calculating section that performs sample entropy analysis and calculates a sample entropy score corresponding to each of the plurality of electrodes, and a portion of each brain corresponding to the plurality of electrodes is used to generate an epileptic seizure based on the sample entropy score. A determination unit that determines whether or not the part corresponds to a start part or an epileptogenic part.

  According to another aspect of the present invention, there is provided an electroencephalogram analyzer for assisting epilepsy diagnosis, wherein an electroencephalogram signal acquisition unit for acquiring electroencephalogram signals corresponding to a plurality of electrodes installed on the head, A calculation unit that performs sample entropy analysis and calculates a sample entropy score corresponding to each of the plurality of electrodes, and displays a brain image simulating the brain on a display device, and displays the brain corresponding to each of the plurality of electrodes. A display control unit that displays information based on the sample entropy score corresponding to the electrode at a position on the image.

It is a figure which shows the brain part in which the electrode of 13 patients was arranged, the number of electrodes, etc. (A) is a diagram showing a sample entropy score of an electrode placed in an area determined to be an epileptic seizure onset (SOZ), and (b) is an ablation area (RA-) excluding an epileptic seizure onset. It is a figure which shows the sample entropy score of the electrode installed in the area | region determined as SOZ), and (c) shows the sample entropy score of the electrode installed in the area | region determined as the outside (Outside RA) of the ablation area. FIG. It is a figure which compared the sample entropy score for every analysis period (non-seizure period (inter), just before seizure (pre), seizure (ictal)) for every patient (Pt). For 13 patients, the sample entropy score for each region during the analysis period (the epileptic seizure onset (SOZ), the resected area excluding the epileptic seizure onset (RA-SOZ), and the outside of the resected area (Outside RA)) was calculated. It is the figure which compared. It is a figure which shows the average value and standard deviation of the sample entropy score for every area | region in each analysis period. It is an image figure showing transition of local epileptic seizure occurrence. It is a figure showing an outline of an electroencephalogram analysis method and an electroencephalogram analysis device concerning a 1st embodiment of the present invention. It is a schematic structure figure of an electroencephalogram analysis device concerning a 1st embodiment of the present invention. 5 is a flowchart illustrating a procedure of an electroencephalogram analysis method according to the first embodiment of the present invention. FIG. 4 is a diagram illustrating an example of an image displayed on a display device. (A) is the ROC curve for the epileptic seizure onset, and (b) is the ROC curve for the excision area excluding the epileptic seizure onset. FIG. 12 is a diagram illustrating values related to the ROC curve illustrated in FIG. 11. FIG. 7 is a diagram showing a sample entropy score measured by each electrode during intermittent epileptic seizures, a standardized variable (Z score) of the sample entropy score, and a comparison result of the Z score with the threshold of the original part and the threshold of the origin in patient 1. It is. It is a schematic diagram of a brain and an electrode, and is a figure which shows the comparison result shown in FIG. FIG. 7 is a diagram showing a sample entropy score and a Z score measured at each electrode during an epileptic seizure in a patient 1 and a comparison result of the Z score with an intrinsic part threshold and an origin part threshold. It is a schematic diagram of a brain and an electrode, and is a figure which shows the comparison result shown in FIG. FIG. 9 is a diagram showing sample entropy scores and Z scores measured by each electrode during intermittent epileptic seizures in patient 7, and comparison results of the Z score with the intrinsic part threshold and the origin part threshold. It is a schematic diagram of a brain and an electrode, and is a figure which shows the comparison result shown in FIG. FIG. 9 is a diagram showing a sample entropy score and a Z score measured at each electrode during an epileptic seizure in a patient 7, and a comparison result of the Z score with an intrinsic part threshold and an origin part threshold. FIG. 20 is a schematic diagram of the brain and the electrodes, showing the comparison results shown in FIG. 19. FIG. 9 is a diagram showing sample entropy scores and Z scores measured by each electrode during intermittent epileptic seizures in a patient 12, and comparison results of the Z score with the original part threshold and the starting part threshold. FIG. 22 is a schematic diagram of the brain and the electrodes, showing the comparison results shown in FIG. 21. FIG. 7 is a diagram showing a sample entropy score and a Z score measured at each electrode during an epileptic seizure in a patient 12, and comparison results of the Z score with a threshold value of an intrinsic part and a threshold value of an origin part. FIG. 24 is a schematic diagram of the brain and the electrodes, showing the comparison results shown in FIG. 23. It is a schematic structure figure of an electroencephalogram analysis device concerning a 2nd embodiment of the present invention. FIG. 4 is a diagram illustrating an example of an image displayed on a display device.

  First, the contents of the clinical research that led the inventor to complete the present invention will be described.

  In focal epileptic seizures, the brain waves in the frequency band from the beta band (14 to 40 Hz) to the gamma band (30 to 80 Hz) measured by an electroencephalogram (EEG) measuring device are the origin of epileptic discharge. We focused on the synchronous activity of the interneurons that link the seizure onset zone of epileptic seizures (SOZ: seizureonsetzone) and the surrounding epileptogenic zone. In addition, the inventors have hypothesized that interneuron synchronization is relevant to the EEG regularity and can be detected by analyzing the EEG regularity. A clinical study was conducted based on the above-mentioned attention and assumptions.

(Patient selection)
Between 2008 and 2013, 13 pediatric patients (8 males and 5 females) undergoing intracranial EEG monitoring of surgical treatment of refractory epilepsy secondary to cortical dysplasia (FCD) Analyzed retrospectively. The location of the electrodes placed in the patient's brain was individually set according to clinical history, seizure symptoms, neuroimaging, and scalp EEG findings. All patients met the following criteria. (1) A clearly visible cortical dysplasia lesion on MRI was observed. (2) Postoperative histopathological diagnosis revealed FCD type II. (3) As a result of the postoperative seizure, the epileptic seizure disappeared, and it was evaluated as having no sign and was completely cured. FIG. 1 shows the characteristics of the patient, the number of electrodes subjected to data analysis described later, and the like. All methods were performed according to guidelines approved by the hospital's research ethics committee. All patients or their families obtained informed consent approved by the hospital's research ethics committee for study participation.

(Intracranial EEG recording)
A subdural grid electrode was implanted to cover the FCD lesion to acquire a patient's EEG signal. The distance between the electrodes was in the range of 8 to 10 mm. EEG data (electroencephalogram signals) were acquired using a sampling rate of 1 kHz.

(Determination of epileptic seizure onset (SOZ) and resection area (RA))
The resection area (RA) based on the determination of the onset of epileptic seizures (SOZ) and epileptogenic part is determined using standard methods. Briefly, the onset of epileptic seizures (SOZ) was determined by a clinical neurophysiologist to correspond to the electrode that recorded the earliest appearing low amplitude, fast EEG activity immediately before the seizure. In fact, the onset of epileptic seizures (SOZ) was determined corresponding to one electrode in nine patients and two electrodes in four patients. The resected area (RA) was determined by a comprehensive assessment of intracranial video EEG, MRI and MEG findings, seizures and electroencephalographic findings on intracranial electrodes after neuropsychological and neurological evaluation, and surgical resection Site determined. The resection area (RA) can also be said to be a site determined by a clinical neurophysiologist. Further, the outside (Outside RA) of the resection region is a portion that can be said to be a healthy portion. The epileptic seizure onset and epileptogenic part are collectively referred to as epileptic lesions.

(How to select / acquire EEG data)
The brain region where the electrodes of the 13 patients are arranged, the number of electrodes, and the like are as shown in FIG. The EEG data selected one typical epileptic seizure with a duration of at least 30 seconds (mean 55.3 ± standard deviation 26.1 seconds) for every 13 patients and three 20 seconds Was extracted and selected. One is during the non-seizure period, one is immediately before the seizure, and one is during the seizure. The non-seizure period included 20 seconds of activity during non-REM sleep at least one hour after the onset of the epileptic seizure. Immediately before the seizure, there was 20 seconds of activity just before the seizure. The seizure included the activity of 20 seconds immediately after the onset of the seizure. The brain wave data was acquired as EEG data (brain wave signal). The selected electroencephalogram data was carefully examined at all analysis intervals to ensure that it did not contain any artifacts.

  The analyzed 20 second EEG data (brain wave signal) during the non-seizure period showed no difference when compared with the other two randomly selected 20 seconds (separated from seizure activity for at least 1 hour) for reference It was confirmed. It can be said that the non-seizure EEG data (brain wave signal) was stable as data to be analyzed. Therefore, it is considered that EEG data in a non-seizure period can be widely used as long as it is generally at rest.

(EEG analysis)
In this clinical study, in performing sample entropy analysis, multi-scale entropy analysis (MSE) that can quantify the regularity of EGG data (electroencephalographic signals) in multiple frequency bands (Reference: Costa, M. Goldberger, AL & Peng , CK Multiscale entropy analysis of complex physiologic time series. Phys Rev Lett. 89, 068102 (2002)). Multi-scale entropy analysis (MSE) is a sample entropy analysis (Reference: Richman, JS & Moorman, JR Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol. 278, 2039? 2049 (2000)) Has been proposed as a time series analysis method that extends the above. The multi-scale entropy analysis is obtained by adding and averaging original data so as not to overlap, reconstructing, and calculating sample entropy (SampEn) reconstructed by a plurality of addition numbers (time axis). In other words, a low number of additions indicates the complexity of a high frequency band, and a high number of additions indicates the complexity of a low frequency band. (References: Takahashi, T., Cho, RY, Mizuno, T., Kikuchi, M., Murata, T., Takahashi, K. & Wada, Y. Antipsychotics reverse abnormal EEG complexity in drug-naive schizophrenia: a multiscale entropy analysis. Neuroimage. 51, 173-182 (2010)). For example, the lower the calculated sample entropy score, the lower the value means that the EEG (encephalogram) rhythm is more regular.

  Here, the electroencephalogram signals of the non-seizure period, the period immediately before the seizure, and the seizure obtained through a total of 1164 electrodes (average 90 ± standard deviation 15 per patient) of 13 patients, that is, The sample entropy score was calculated by performing sample entropy analysis on all of the electroencephalogram signals for a total of 39 periods for 13 analysis periods per patient x 3 analysis periods (20 seconds per analysis period). More specifically, the sample entropy analysis was performed by multi-scale entropy analysis (Costa et al. 2002), and the sample entropy scores of a plurality of frequency bands were calculated.

First, the original EEG sequence X = [X ₁ , X ₂ ,... X _N ] is subjected to coarse-graining processing by a time scale factor (τ) set as a width of a non-overlapping window. The other coarse-grained time series Y (τ) = [Y ₁ , Y ₂ ,... Y] is defined as the following equation (1).

The time series Y (1) is identical to the original time series, which represents a short time scale, while higher τ values represent a longer time scale. The sample entropy score (MSE (τ)) with τ was calculated for each series Y (τ). MSE (τ) depends on three parameters: N (total number of data points), m (number of consecutive data points to compare), and r (noise threshold for measuring time series consistency). MSE (τ) in the coarse-grained series Z = [Z ₁ , Z ₂ ,... Z _N ] is defined as the following equation (2).

Z ^m is (N-m) is a vector of m time series of length, | representing the distance between points in a space of dimension m ZmiZim and ZmjZjm | Zmi-Zmj || Zim- Zjm. (Note that for details of the sample entropy algorithm, see Richman and Moorman, 2000).

  Thus, the sample entropy score is the negative natural log of the conditional probability that for all data points (N), two sequences similar to each other for the first point m remain the same at the next point (m + 1). is there.

  All EEG data (brain wave signals) are down-sampled at a sampling rate of 200 Hz and are efficiently analyzed in the sampled frequency band. Specifically, a sample entropy score for each of a plurality of frequency bands corresponding to the time scale factors τ = 1 to 20 was calculated.

  In this analysis, referring to the values of m = 2 and r = 0.2 in the case of showing that it provides good statistical verification for multi-scale entropy analysis of EEG signals, each analysis period ( For 20 seconds, N = 4000 (ie, 20 seconds × 200 Hz), m = 2, and r = 0.2.

  Regarding the three parameters on which MSE (τ) depends, where N = 2000 (that is, 10 seconds when corresponding to 200 Hz downsampling) and m = 1, 2, 3 r = 0. If it is 15 to 0.3, it is considered that equivalent results can be obtained. Further, each parameter can be set conveniently depending on the sensitivity and the analysis performance of the electrode and the receiving device of the electroencephalogram signal.

  FIG. 2 shows the calculation results of the sample entropy scores of all the electrodes (1164) of 13 patients, the epileptic seizure onset (SOZ), the excision area excluding the epileptic seizure onset (RA-SOZ), and the excision area. It is shown in a graph for each area determined by a clinical neurophysiologist, outside the area (Outside RA). In each graph, the time scale factor τ = 1 to 20 is set on the horizontal axis, and the sample entropy score is set on the vertical axis, and the sample entropy is calculated for each of the non-seizure period (Interictal), the period immediately before seizure (Preictal), and the time of seizure (Ictal). The average value of the score was shown by a tie curve, and the standard deviation of the sample entropy score was also displayed. N shown in each graph corresponds to an epileptic seizure onset (SOZ), an excision area excluding the epileptic seizure onset (RA-SOZ), and an area outside the excision area (Outside RA), respectively. The number of electrodes to be used. 17 (n = 17) electrodes are epileptic seizure onset (SOZ), 308 (n = 308) electrodes are excision area excluding epileptic seizure onset (RA-SOZ), 839 (n = 839) ) Corresponded to the outside of the resection area. On the horizontal axis, τ = 1 is 200 Hz, τ = 2 is 100 Hz, τ = 3 is 66.7 Hz,... Τ = 12 is 16.7 Hz, τ = 13 is 15.3 Hz, and τ = 14 is 14.3 Hz. .

  As can be seen from FIG. 2, the sample entropy curve of the epileptic seizure onset (SOZ) can be clearly distinguished in three analysis periods (non-seizure period, immediately before seizure, and seizure), excluding the epileptic seizure onset. The sample entropy curve of the resected area (RA-SOZ) was clearly distinguishable between the time of the seizure and the other two analysis periods. Looking at a frequency band suitable for distinction, 14 Hz to 100 Hz, the lower limit is preferably 20 Hz, and more preferably 28.6 Hz. The upper limit is preferably 80 Hz, more preferably 70 Hz. Note that the downsampling frequency (sampling rate) was 200 Hz in this analysis, but by changing the downsampling frequency to 500 Hz or 1000 Hz, it is possible to set the upper limit to 150 Hz or 200 Hz for analysis. Become.

  FIG. 2 shows that analysis of the frequency band, preferably from the beta band to the gamma band, more preferably the frequency band of the gamma band, can greatly contribute to the improvement of the detection rate of the epileptic epithelium and the epileptic seizure onset. This is because epileptic discharge in the high frequency band of 100 to 200 Hz, which is higher than the frequency band from the beta band to the gamma band, is discontinuously and suddenly observed, whereas the epileptic discharge is discontinuously and suddenly observed. It can be considered that epileptic electroencephalograms in all frequency bands are also observable as background activities during interictal periods.

  In this analysis, the frequency for downsampling was set to 200 Hz, so the average value of the sample entropy score of the time scale factor τ = 3 to 7 corresponding to about 30 to 70 Hz in the gamma frequency band (30 to 80 Hz) was calculated as follows: This was adopted as a sample entropy score to be analyzed thereafter (see equations (3) and (4)).

  In this analysis, the time scale factor corresponding to about 30 to 70 Hz is used in the gamma frequency band (30 to 80 Hz), but the time scale factor corresponding to the beta frequency band (14 Hz to 40 Hz) is also used. Needless to say. That is, it is possible to use the brain wave signal not only in the gamma frequency band but also in the beta frequency band.

(Result of analysis)
The calculated sample entropy scores for each of the 13 patients were organized as follows. FIG. 3 compares the sample entropy scores for each analysis period (non-seizure period (inter), immediately before seizure period (pre), and seizure period (ictal)) for each patient (Pt). n represents the number of electrodes analyzed for each patient. The results are shown by error bars, which are the mean ± standard deviation of the sample entropy scores of all the electrodes during each analysis period. Significant differences were assessed by the Steel-Dwass test. P values (significance level) <0.05 were considered significant and marked with an asterisk (*). For example, in patient 1 (Pt1), the average value of the sample entropy score is 1.386, and the standard deviation is 0, which is the analysis result of the electroencephalogram signals obtained from all the electrodes (n = 104) during the non-seizure period. 0.0724 is shown by an error bar, indicating that the Steel-Dwass test showed a significant difference compared with the time of the seizure, but a significant difference compared with the time immediately before the seizure. Was not observed.

  In some patients with a small ratio of resected area (RA) to the total number of electrodes, the sample entropy score increased significantly from the time immediately before the seizure to the time of the seizure. 7 compared with the time before the seizure and at the time of the seizure, and 9 cases from the non-seizure period to the time of the seizure significantly decreased. Entropy scores tended to decrease. These results were analyzed to indicate that the sample entropy score could be particularly low in the resected area (RA) at the time of the seizure.

  FIG. 4 compares the sample entropy scores (SampEn) of the 13 patients for each analysis period in each region (the epileptic seizure onset, the excision region excluding the epileptic seizure onset, and the outside of the excision region). . In FIG. 4, a P value <0.01 is considered significant and is indicated by an asterisk (*).

  The sample entropy score at the onset of epileptic seizures (SOZ) was smaller than the other regions in the non-seizure period and showed the lowest score. In the period immediately before the seizure, the low score of the onset of epileptic seizure (SOZ) in the non-seizure period tended to be scattered, but during the seizure, the resection area (RA-SOZ) and the onset of epileptic seizure (SOZ) were low. Score. That is, the resection area (RA) had a low score. FIG. 5 shows the average value and the standard deviation of the sample entropy score for each region during each analysis period.

<Non-seizure period>
The sample entropy score (SampEn) was calculated for the resected area excluding the onset of epileptic seizures (RA-SOZ): (mean 1.20, standard deviation ± 0.23) and outside the resected area (Outside RA): (mean 1. 33, standard deviation ± 0.13), the score at the onset of epileptic seizure (SOZ): (mean 0.93, standard deviation ± 0.37) was significantly lower (Fig. 4, both p <0) .01). The RA-SOZ score was significantly lower than the Outside RA score (p <0.01). In summary, during the non-seizure period, the lowest sample entropy score (most regular) was always found at the onset of epileptic seizures (SOZ).

<Before onset>
The sample entropy score (SampEn) was calculated from the score at the onset of epileptic seizure (SOZ): (mean 1.05, standard deviation ± 0.32) outside the resection area (Outside RA): (mean 1.30, standard (P <0.01). However, the significant difference from the resected area (RA-SOZ) excluding the onset of epileptic seizures: (mean 1.19, standard deviation ± 0.24) was small. In summary, in the period immediately before seizure, the score increased at the onset of epileptic seizures (SOZ), and there was no difference with the score of the resected area (RA), resulting in an approximate score (it became less regular in SOZ) ).

<At seizure>
The sample entropy score (SampEn) was the epileptic seizure onset (SOZ): (mean 0.85, standard deviation ± 0.32) and the excision area excluding the epileptic seizure onset (RA-SOZ): (mean 0. 97, standard deviation ± 0.30), both were significantly lower than the outside of the resection area (Outside RA): (mean 1.27, standard deviation ± 0.17) (both p <0.01). In summary, during seizures, a decreasing sample entropy score propagated beyond the onset of epileptic seizures (SOZ).

  From the above, the transition of epileptic seizures was imaged as shown in FIG.

  (1) During the non-seizure period, the sample entropy score is low in the epileptic seizure onset (SOZ). This indicates that neuronal synchronization is expressed at a high level in the epileptic seizure onset (SOZ). On the other hand, the excision area (RA-SOZ) excluding the epileptic seizure onset is more asynchronous than the epileptic seizure onset, but more synchronous than the excision area outside (Outside RA) to prevent seizures. .

  (2) Immediately before the seizure, the sample entropy score increases at the epileptic seizure onset (SOZ). This indicates that the balance between suppression and excitatory neurons centered on the epileptic seizure onset breaks down, and the epileptic seizure onset assimilate with the resected area excluding the surrounding epileptic seizure onset.

  (3) At the time of a seizure, the sample entropy score decreases extremely in the entire resection area (RA). This is because neuronal hypersynchronization propagates throughout the resection area (RA), causing seizures.

  Furthermore, the inventor has obtained the following idea to support epilepsy diagnosis.

  (1) A threshold is set for a sample entropy score (SampEn) in a non-emergent stage, and a range of an electroencephalogram in which the sample entropy score is equal to or less than the threshold can be a marker of the epileptic seizure onset or epileptic part. .

  (2) A threshold may be set for a temporal change in the sample entropy score (SampEn) at the onset of the epileptic seizure just before the seizure, and may be used as a timing marker for epileptic seizure onset.

  (3) A threshold is set for the sample entropy score (SampEn) at the time of an epileptic seizure, and the range of the electroencephalogram where the sample entropy score is equal to or less than the threshold value can be a marker for the epileptic seizure onset or epileptogenic part .

  (4) When compared between patients, the sample entropy score (SampEn) is distributed between patients in a range of a large value or in a range of a small value. Ingenuity is required.

  (5) As a marker of the epileptic seizure onset or epileptogenic part, the sample entropy score (SampEn) in the non-seizure period (epileptic intermittent period) is the most effective as compared to the period immediately before the epileptic seizure or the epileptic seizure Be a marker.

  An electroencephalogram analysis method and an electroencephalogram analysis device 100 that support epilepsy diagnosis according to an embodiment of the present invention that embodies the idea obtained by the above clinical study will be described with reference to the drawings. In the following description, when the method is the same as the above clinical study, the description is omitted.

  First, an electroencephalogram analysis method and an electroencephalogram analysis device 100 according to the first embodiment of the present invention will be described with reference to FIGS. FIG. 7 is a diagram showing an outline of the electroencephalogram analysis method and the electroencephalogram analysis device 100, FIG. 8 is a schematic configuration diagram of the electroencephalogram analysis device 100, and FIG. 9 is a flowchart showing a procedure of the electroencephalogram analysis method.

  As shown in FIG. 7, an electroencephalogram analysis method and an electroencephalogram analysis apparatus 100 according to the present embodiment analyze an electroencephalogram signal measured by a plurality of electrodes 1 installed on a head 2 of a subject. There are two methods for measuring the electroencephalogram signal: a method of installing the electrode 1 on the scalp and a method of installing the electrode 1 in the skull. The electroencephalogram analysis method and the electroencephalogram analyzer 100 correspond to both. It is. The arrangement of the electrodes 1 corresponds to the electrode arrangement according to the international 10-20 method, the electrode arrangement specifying a brain region as in clinical research, and the arrangement of 60 to 120 electrodes. Generally, when the electrodes 1 are placed in the skull, the electrodes 1 are preferably placed in a region where the lesion is sufficiently covered, and the electrodes 1 are preferably placed at equal intervals.

  As shown in FIG. 8, an electroencephalogram analyzer 100 according to the present embodiment includes a computer 10 that acquires and processes an electroencephalogram signal from the electrode 1, a display device 20 that displays a processing result of the computer 10, and a computer 10. And a threshold value input device 30 for inputting a threshold value used for processing to be performed.

  The computer 10 includes a CPU (central processing unit), a ROM (read only memory), a RAM (random access memory), a bus connecting these, an input / output interface, and the like. The RAM stores data in the processing of the CPU, and the ROM stores programs executed by the CPU, set values of the programs, and the like in advance.

  The computer 10 includes an electroencephalogram signal acquisition unit 11, a sample entropy score analysis / calculation unit 12, a standardized variable calculation unit 13, a determination unit 14, and a display control unit 15.

  The electroencephalogram signal acquisition unit 11 acquires an electroencephalogram signal measured by the electrode 1. That is, the electroencephalogram signal measured by the electrode 1 is input to the electroencephalogram signal acquisition unit 11. The electroencephalogram signal acquisition unit 11 may directly acquire an electroencephalogram signal measured by the electrode 1 installed on the subject's head, or may obtain an electroencephalogram signal from a recording medium such as a memory that records the electroencephalogram signal measured by the electrode 1. A signal may be obtained. However, it is considered that industrial use is higher if the electroencephalogram signal measured by the electrode 1 is temporarily recorded on a recording medium such as a memory, and the electroencephalogram signal acquisition unit 11 acquires the electroencephalogram signal recorded on the recording medium.

  The sample entropy score analysis / calculation unit 12 performs sample entropy analysis on the brain wave signal acquired by the brain wave signal acquisition unit 11 and calculates a sample entropy score corresponding to each of the plurality of electrodes 1.

  The standardized variable calculator 13 calculates a standardized variable of the sample entropy score corresponding to each of the plurality of electrodes 1.

  The determination unit 14 determines whether each brain site corresponding to the plurality of electrodes 1 corresponds to the epileptic seizure onset or epileptogenic region. Here, the epileptogenic region is a site of abnormal cranial nerve excitation that causes an epileptic seizure, and is a site where seizure can be suppressed by resection. The epileptic seizure onset is the site where seizure activity begins. In general, the epileptic seizure onset is included in the epileptogenic part, and the epileptic seizure onset and the epileptogenic part are collectively called an epileptic lesion.

  The display control unit 15 performs a process of displaying the determination result of the determination unit 14 on the display device 20.

  The display device 20 is a liquid crystal display, a projector, or the like.

  The threshold value input device 30 is for inputting a threshold value that is used as a criterion for determination by the determination unit 14, and includes a mouse, a keyboard, a touch panel, and the like.

  The brain wave analysis method according to the present embodiment will be described with reference to FIG. The procedures of steps 1 to 6 described below are performed by the CPU executing a program stored in the ROM. That is, the computer 10 stores a program for executing the procedure of Steps 1 to 6 (a program for executing an electroencephalogram analysis method for supporting epilepsy diagnosis).

<Acquisition of EEG signal>
In step 1, the electroencephalogram signal acquisition unit 11 acquires an electroencephalogram signal measured by the electrode 1. An electroencephalogram signal measured by the plurality of electrodes 1 installed on the subject's head 2 is acquired as follows. For example, when 100 electrodes 1 are placed on the head 2, 100 EEG signals corresponding to the 100 electrodes 1 are acquired (see FIG. 7). As the analysis period of the electroencephalogram, an electroencephalogram signal of any one of the three analysis periods of the intermittent epileptic seizure, the period immediately before the epileptic seizure, and the epileptic seizure is acquired, and the analysis period is specified. The epileptic intermittent period (non-seizure period) includes an activity of 10 seconds to 20 seconds separated by at least about 10 minutes after the onset of the epileptic seizure. The period immediately before an epileptic seizure includes the activity of 10 seconds to 20 seconds immediately before the seizure. The seizure includes the activity of 10 to 20 seconds at the time of the seizure. Each analysis period is preferably at least 20 seconds, but a continuous time period of about 10 seconds is sufficient. The intermittent epileptic period, the period immediately before the epileptic seizure, and the identification of the epileptic seizure are determined not only by a doctor's consultation but also by using data from a heart rate measurement device, electrocardiogram measurement device, video monitoring, etc. It is possible to specify.

<Sample entropy analysis (calculation of sample entropy score)>
In step 2, the sample entropy score analysis / calculation unit 12 performs sample entropy analysis on the electroencephalogram signal acquired by the electroencephalogram signal acquisition unit 11 to calculate a sample entropy score corresponding to each of the plurality of electrodes 1. In the present embodiment, analysis was performed by selecting a sample entropy score in a frequency band of about 30 to 70 Hz from a gamma frequency band (30 Hz to 80 Hz). For example, when an electroencephalogram signal corresponding to 100 electrodes is acquired, 100 sample entropy scores corresponding to each of the 100 electrodes are calculated (see FIG. 7). Sample entropy analysis includes sample entropy (references: Richman, JS & Moorman, JR Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol. 278, 2039? 2049 (2000)), and several others. Phys Rev Lett. 89, Physics Rev Lett. 89, Multiscale entropy analysis (MSE) for quantifying the regularity of electroencephalographic signals in the frequency range of the spectrum (Reference: Costa, M. Goldberger, AL & Peng, CK 068102 (2002)). The sample entropy score indicates the regularity of the digital waveform by a numerical value. For example, a lower value means that the EEG (electroencephalogram) rhythm is more regular and abnormal.

<Calculation of standardized variables for sample entropy score>
In step 3, the standardized variable calculator 13 calculates a standardized variable of the sample entropy score corresponding to each of the plurality of electrodes 1. Specifically, a standardized variable of each sample entropy score is calculated using a plurality of sample entropy scores as a population. The standardized variable corresponds to each of the plurality of electrodes 1. The standardized variable is a Z value (= (sample entropy score−mean value) / standard deviation) or a deviation value. For example, when an electroencephalogram signal corresponding to 100 electrodes 1 is acquired and 100 sample entropy scores are calculated, a standardized variable corresponding to each of the 100 electrodes 1 is defined as a population of 100 sample entropy scores. Is calculated.

<Determination of epilepsy lesion>
In Steps 4 and 5, the determination unit 14 determines whether each brain part corresponding to the plurality of electrodes 1 corresponds to an epileptic seizure onset or epileptogenic part. First, in step 4, a standardized variable corresponding to each of the plurality of electrodes 1 is compared with a threshold. The threshold is set by the threshold input device 30. Specifically, a doctor inputs a threshold value by operating a keyboard. Note that the threshold value may be set in advance in the storage unit of the electroencephalogram analyzer 100 instead of inputting from the keyboard. The threshold is a threshold for determining an epileptogenic part (hereinafter, referred to as “primary part threshold”) and a threshold for determining an epileptic seizure onset part (hereinafter, “origin part threshold”). ) Is set. The origin threshold is smaller than the intrinsic threshold.

  In step 5, the epileptic seizure onset and epileptogenic part are determined based on the comparison result in step 4. Specifically, in step 4, when the standardized variable corresponding to a certain electrode 1 is equal to or less than the threshold value of the intrinsic part, the part of the brain corresponding to the electrode 1 is determined to be an epileptic part. When the standardized variable corresponding to a certain electrode 1 is equal to or smaller than the threshold value of the origin part, the part of the brain corresponding to the electrode 1 is determined to be the epileptic seizure onset part. If the standardized variable corresponding to a certain electrode 1 is larger than the intrinsic part threshold, the part of the brain corresponding to the certain electrode 1 is relatively determined to be a healthy part (step 6).

  Therefore, for example, a standardized variable is calculated for each of the 100 electroencephalogram signals obtained via the 100 electrodes 1 installed on the head 2 to measure the electroencephalogram of the subject, The comparison is made to determine whether or not the site of the brain corresponding to each electrode 1 is an epileptogenic site. As a result, the part corresponding to the electrode 1 not determined as the epileptogenic part is relatively determined to be a healthy part. Further, the calculated 100 standardized variables are also compared with the threshold value of the onset part, and it is evaluated whether the part of the brain corresponding to each electrode 1 is the onset part of the epileptic seizure onset.

  Note that, in steps 4 to 6, the case where the determination unit 14 compares the standardized variable of the sample entropy score with the threshold value has been described. However, the comparison target used in the determination unit 14 is not limited to the standardized variable of the sample entropy score as long as the information is based on the sample entropy score. Alternatively, the sample entropy score itself may be used.

  The flow of the above steps 1 to 6 is executed for each of the intermittent epileptic seizures, the period immediately before the epileptic seizure, and the analysis period for the epileptic seizure.

<Display of judgment result>
The determination results (steps 5 and 6) by the determination unit 14 are displayed on the display device 20. The process of displaying the result of the determination by the determination unit 14 on the display device 20 is performed by the display control unit 15. FIG. 10 shows an example of an image displayed on the display device 20. The display control unit 15 displays the brain image 21 imitating the brain on the display device 20 and displays the determination result of the determination unit 14 at a position on the brain image 21 corresponding to each of the plurality of electrodes 1. The position of the electrode 1 on the brain image 21 is specified on the display device 20 based on the electrode position information stored in the electroencephalogram analyzer 100.

  As a method of displaying the determination result, for example, as shown in FIG. 10, the site determined to be the epileptic seizure onset is indicated by ☆, the site determined to be an epileptic site is indicated by Δ, and The determined site is indicated by a circle. As described above, instead of displaying each part with a symbol, the respective parts may be displayed with a color. Further, in addition to displaying the symbols and colors, the numerical values of the standardized variables calculated by the standardized variable calculation unit 13 may be displayed.

<About threshold>
The threshold set using the analysis result of the clinical study will be described. Using Z value as a standardized variable for determining the epileptic seizure onset (SOZ) and the excision area (RA-SOZ) excluding the epileptic seizure onset, the sensitivity (Sensitivity) during the non-seizure period, immediately before the seizure, and during the seizure ) And Specificity were calculated. Sensitivity is a value defined as “probability of correctly determining what should be determined as positive” for a certain test. The specificity is a value defined as “probability that a test that should be determined to be negative is correctly determined to be negative” for a certain test. An ROC curve summarizing the results is shown in FIG. As shown in FIGS. 11A and 11B, the area under the ROC curve (Area under ROC curve) is 0.99 (non-seizure period) and 0.87 (immediately before seizure) in the epileptic seizure onset (SOZ). Stage), 0.90 (at the time of seizure), and 0.67 (non-seizure period), 0.64 (just before seizure), 0.81 in the resected area (RA-SOZ) excluding the epileptic seizure onset. (At the time of a seizure), and it can be seen that a threshold value serving as an effective criterion can be set. In particular, the sensitivity and specificity during the non-seizure period and during the seizure are practically useful as markers. The best cut-off value as a threshold as a criterion was defined as the Z score at which the Youden index (sensitivity + specificity -1) became the maximum. FIG. 12 shows the area under the ROC curve (Area under ROC curve) and the Youden index in each analysis period for the evaluation of the epileptic seizure onset (SOZ) and the excision area (RA-SOZ) excluding the epileptic seizure onset. 5 is a table summarizing the maximum value, the best cutoff Z value at which the Youden index becomes the maximum (best cutoff value), and the sensitivity and specificity at the best cutoff Z value. The best cutoff Z value, sensitivity and specificity, and Youden index shown in FIG. 12 correspond to the ROC curves shown in FIGS. The sensitivity and specificity for detecting the onset of epileptic seizures (SOZ) are 100% and 97.1% of the best cutoff Z value (−2.09) in the non-seizure period (Interictal), ) Is 76.5% and 85.5% for the best cutoff Z value (−0.70), and 88.2% and 86 for the best cutoff Z value (−1.00) for seizures (Ictal). 0.9%. The sensitivity and specificity for detecting the resected area (RA-SOZ) excluding the onset of epileptic seizures were 54.2% and 73.7% of the best cutoff Z value (-0.12) in the non-seizure period (Interictal). 8%, 44.2% and 76.9% of the best cutoff Z value (-0.12) immediately before the seizure (Preictal), and the best cutoff Z value (-0.19) during the seizure (Ictal) 67.4% and 84.7%, both of which showed high values as criteria.

  The criterion (threshold) for determining the excision area (RA-SOZ) excluding the epileptic seizure onset is applied to the criterion (threshold) for determining the epileptogenic part. The reference (threshold) during the non-seizure period is applied to the evaluation of the electroencephalogram signal during the intermittent seizure period.

  Next, specific examples will be described. In the present embodiment, three representative patients (patients 1, 7, and 12) among the 13 patients in the clinical study were used as subjects, and the electroencephalogram signals obtained from each patient were analyzed by simulation using the electroencephalogram analysis method according to the present embodiment. did. The characteristics and the like of the patients 1, 7, and 12 as subjects are as shown in FIG. 1 described above.

  First, the patient 1 will be described with reference to FIGS.

  FIG. 13 shows the sample entropy score measured at each electrode 1 during the epileptic intermittent period (non-seizure period), a standardized variable (Z score) of the sample entropy score, and the Z score, the intrinsic part threshold, the origin part threshold, and the like. It is a figure showing the comparison result of. FIG. 14 is a schematic diagram of the brain and the electrode 1, and is a diagram showing the comparison results shown in FIG. FIG. 15 is a diagram showing sample entropy scores and Z scores measured at each electrode 1 during an epileptic seizure, and comparison results of the Z score with the intrinsic part threshold and the starting part threshold. FIG. 16 is a schematic diagram of the brain and the electrode 1, and is a diagram showing the comparison results shown in FIG.

  The installation positions of the electrodes 1 on the brain of the patient 1 are as shown in FIGS. 14 and 16. In the patient 1, 104 electrodes 1 were installed on the right cerebral hemisphere.

  The sample entropy score, the standardized variable (Z score) of the sample entropy score, and the comparison results of the Z score with the intrinsic part threshold and the starting part threshold shown in FIGS. 13 and 15 are obtained by the method described with reference to FIG. . In FIGS. 13 and 15, when the Z score is equal to or less than the intrinsic portion threshold, it is indicated by ●, and when the Z score is equal to or less than the originating portion threshold, it is indicated by Δ. Is indicated by x. The best cutoff Z value shown in FIG. 12 is used for the original part threshold and the starting part threshold.

  In FIGS. 14 and 16, based on the comparison results of FIGS. 13 and 15, the electrode 1 having a Z score equal to or less than the threshold of the intrinsic portion is indicated by a dotted line, and the electrode 1 having a Z score equal to or less than the threshold of the origin is indicated by a dashed line. , And the Z-score is larger than the intrinsic part threshold, and the electrode 1 is indicated by a solid line. That is, the part of the brain corresponding to the electrode 1 indicated by the dotted line is determined to be an epileptogenic part, and the part of the brain corresponding to the electrode 1 indicated by the dashed line is determined to be the epileptic seizure onset. Means that the part of the brain corresponding to the electrode 1 indicated by the solid line is determined to be a healthy part. In FIGS. 14 and 16, a site determined as an epileptic site by a clinical neurophysiologist is indicated by a dotted line region, and a site determined as an epileptic seizure onset is indicated by a solid line region.

  As can be seen from FIGS. 14 and 16, the epileptic lesion (epileptic part and epileptic seizure onset) determined by the electroencephalogram analysis method according to the present embodiment, and the epileptic lesion determined by a clinical neurophysiologist. Was closely related.

  17 to 20 show an embodiment relating to the patient 7, and FIGS. 21 to 24 show an embodiment relating to the patient 12. 17 and 21 are diagrams corresponding to FIG. 13, and show the relationship between the sample entropy score, the Z score, and the Z score measured at each electrode 1 during the intermittent epileptic seizure, and the original part threshold and the starting part threshold. It is a figure showing a comparison result. 18 and 22 are diagrams corresponding to FIG. 14 and are schematic diagrams of the brain and the electrode 1, and are diagrams illustrating the comparison results shown in FIG. 17 and FIG. FIGS. 19 and 23 are diagrams corresponding to FIG. 15, and show sample entropy scores, Z scores measured at each electrode 1 during an epileptic seizure, and comparison results of the Z score with the intrinsic portion threshold and the starting portion threshold. FIG. 20 and 24 are diagrams corresponding to FIG. 16 and are schematic diagrams of the brain and the electrode 1, and are diagrams illustrating the comparison results shown in FIGS. 19 and 23.

  The installation positions of the electrodes 1 on the brain of the patient 7 are as shown in FIGS. 18 and 20. In the patient 7, 105 electrodes 1 were installed on the left cerebral hemisphere. The installation positions of the electrodes 1 on the brain of the patient 12 are as shown in FIGS. 22 and 24. In the subject 12, 99 electrodes 1 were installed on the left cerebral hemisphere.

  As can be seen from FIGS. 18, 20, 22, and 24, also in patients 7 and 12, the epileptic lesions (epileptic part and epileptic seizure onset) determined by the electroencephalogram analysis method according to the present embodiment, There was a close association with epileptic lesions as determined by a clinical neurophysiologist.

  As can be seen from the above examples, by analyzing a patient's electroencephalogram signal by the electroencephalogram analysis method according to the present embodiment, the epileptogenic part, or the epileptic seizure onset, and thus the healthy part can be determined. Can assist in the diagnosis of

  In the above example, the case where the intermittent period of epileptic seizure and the electroencephalogram signal measured at the time of epileptic seizure was used was shown. It should be noted that an analysis method using an electroencephalogram signal measured immediately before an epileptic seizure can similarly obtain a result.

  In the above-described embodiment, the best cutoff Z value shown in FIG. 12 is used as the original part threshold and the starting part threshold. The thresholds used as the original part threshold and the starting part threshold are not necessarily limited to the best cutoff Z value. The threshold may be set to a value within a predetermined range. The range of the threshold value to be set is on the lower limit side with respect to the best cutoff Z value, preferably (best cutoff Z value−0.50), more preferably (best cutoff Z value−0.25). It is. On the upper limit side, the value is preferably (best cutoff Z value + 0.50), and more preferably (best cutoff Z value + 0.25). In particular, the evaluation criteria for the onset of epileptic seizures during the intermittent epileptic seizure are extremely excellent (Area under ROC curve: 0.99). .00). Comparing with the Z scores shown in FIGS. 13, 15, 17, 19, 21, and 23, it can be seen that a practically effective determination result can be obtained even if these thresholds are set.

  The lower and upper thresholds that can be set, the sensitivity and specificity for each threshold, and the Youden index are shown below. From these values, the practical validity of this analysis method can be confirmed.

Intrinsic threshold for intermittent seizures:
Threshold: +0.38 sensitivity 72.3%, specificity 49.1%, Youden index 0.21
Threshold: +0.13... Sensitivity 62.2%, specificity 61.6%, Youden index 0.23
Threshold value: -0.37 sensitivity 44.6%, specificity 80.7%, Youden index 0.25
Threshold: -0.62 ... sensitivity 38.2%, specificity 87.6%, Youden index 0.26
Origin threshold for intermittent epileptic seizures:
Threshold value: -1.59: sensitivity 100%, specificity 93.7%, Youden index 0.94
Threshold value: -1.84: sensitivity 100%, specificity 95.9%, Youden index 0.96
Threshold: -2.34: sensitivity 88.2%, specificity 98.1%, Youden index 0.86
Threshold value: -2.59: sensitivity 76.4%, specificity 98.7%, Youden index 0.75
Threshold value: -3.09 sensitivity 64.7%, specificity 99.5%, Youden index 0.64
Genitalia threshold for epileptic seizures:
Threshold: +0.31 sensitivity 79.1%, specificity 63.4%, Youden index 0.42
Threshold: +0.06... Sensitivity 73.5%, specificity 76.5%, Youden index 0.50
Threshold: -0.44 sensitivity 58.8%, specificity 89.9%, Youden index 0.49
Threshold value: -0.69: sensitivity 49.8%, specificity 93.4%, Youden index 0.43
Origin threshold for seizures:
Threshold: -0.50 sensitivity 88.3%, specificity 78.4%, Youden index 0.67
Threshold value: -0.75 sensitivity 88.2%, specificity 83.0%, Youden index 0.71
Threshold: -1.25 ... sensitivity 70.6%, specificity 90.1%, Youden index 0.61
Threshold value: -1.50: sensitivity 64.7%, specificity 91.6%, Youden index 0.56

  These sensitivities, specificities, and Youden indices correspond to the ROC curves shown in FIGS.

  According to the above embodiment, the following effects can be obtained.

  The epileptic lesion (epileptic part and epileptic seizure onset) determined by the electroencephalogram analysis method according to the present embodiment is closely related to the epileptic lesion determined by a clinical neurophysiologist. Was done. Therefore, by analyzing the electroencephalogram signal of the patient by the electroencephalogram analysis method according to the present embodiment, the epileptogenic part, the epileptic seizure onset part, and the healthy part can be evaluated, so that the diagnosis of the doctor can be supported. Therefore, according to the present embodiment, it is possible to easily, objectively and stably determine an epilepsy lesion.

  In addition, according to the present embodiment, the epilepsy lesion can be determined using quantified numerical values such as scores and variables, so that simple and objectively stable determination can be performed. As described above, since an objective determination can be provided, it is possible to support a doctor's diagnosis. In particular, since the onset of epileptic seizures can be evaluated objectively, it can contribute to the development of new treatments in the future.

  Further, in the present embodiment, a Z-score having all sample entropy scores as a population is calculated for the calculated sample entropy score, and the calculated Z-score is compared with a threshold for the Z-score. It is possible to make a determination without depending on the variation in the entropy score. Therefore, highly accurate determination can be made for any patient.

  In a conventional epilepsy diagnosis, a doctor interprets an electroencephalogram immediately before an epileptic seizure when an epileptic seizure is actually induced to diagnose the epileptic seizure onset (see Non-Patent Document 2 P1371). However, according to the present embodiment, since the epileptogenic part, particularly the epileptic seizure initiation part can be evaluated using the electroencephalogram signal in the intermittent epileptic seizure, the patient needs to wait for the epileptic seizure to be induced. And the burden on the patient can be greatly reduced.

  In addition, it is very important to identify the onset of epileptic seizures, especially in the local diagnosis of epilepsy surgery, because it is related to postoperative seizure suppression. Determining localization by reading EEG requires advanced medical techniques. However, according to this embodiment, since the epileptic epithelium, particularly the epileptic seizure onset can be evaluated using the intermittent epileptic electroencephalographic signal, postoperative seizure Epilepsy treatment for suppression can be performed.

  Further, by sequentially changing the threshold value input from the threshold value input device 30, the tendency of the epilepsy lesion can be grasped.

  Hereinafter, a modified example of the present embodiment will be described.

  In the first embodiment described above, analysis was performed by selecting a sample entropy score of a frequency band of about 30 to 70 Hz (scale factor τ = 3 to 7) from a plurality of frequency bands obtained by the multi-scale entropy analysis. However, the measured electroencephalogram signal may be of a frequency band extracted in advance by a combination of a high-pass / low-pass filter. Further, a predetermined frequency band may be extracted from the measured brain wave signal by a high-pass / low-pass filter provided in the brain wave analyzer 100.

  The frequency band for which the sample entropy score is calculated is not only the gamma band (30 to 80 Hz) and the beta band (14 to 40 Hz), but also the entire frequency band (scale factor τ) obtained by performing multiscale entropy analysis on the acquired brain wave signal. = 1 to n), a frequency band having a large standard deviation (scattering degree and dispersion of data) of the sample entropy score may be selected.

  Next, an electroencephalogram analysis method and an electroencephalogram analysis device 200 that support epilepsy diagnosis according to a second embodiment of the present invention will be described with reference to FIGS. FIG. 25 is a schematic configuration diagram of the electroencephalogram analyzer 200, and FIG. 26 is a diagram illustrating an example of an image displayed on the display device 20. Hereinafter, only differences from the first embodiment will be described, and the same components as those in the first embodiment will be denoted by the same reference numerals as those in the first embodiment, and description thereof will be omitted.

  As shown in FIG. 25, the electroencephalogram analysis apparatus 200 does not include the determination unit 14 included in the electroencephalogram analysis method and the electroencephalogram analysis apparatus 100 according to the first embodiment. Therefore, in the electroencephalogram analyzer 200, the standardized variable of the sample entropy score calculated by the standardized variable calculator 13 is output to the display controller 15. The display control unit 15 performs a process of displaying the standardized variable of the sample entropy score on the display device 20. Specifically, the display control unit 15 displays the brain image 21 simulating the brain on the display device 20 and displays the brain image 21 corresponding to each of the plurality of electrodes 1 on the brain image 21 as shown in FIG. , The standardized variable of the sample entropy score corresponding to the electrode 1 is displayed.

  The information displayed on the display device 20 is not limited to the standardized variable of the sample entropy score, but may be any information based on the sample entropy score, and may be the sample entropy score itself. Further, the correlation of the sample entropy scores of a plurality of frequency bands (the scale factor τ = 1 to 20 on the horizontal axis and the sample entropy score on the vertical axis) obtained by the multi-scale entropy analysis may be graphically displayed (FIG.). . Since the correlation of the sample entropy scores of a plurality of frequency bands can be confirmed at a plurality of parts of the brain 2 where the electrode 1 is installed, the difference in the regularity of the electroencephalogram signal between the plurality of parts of the brain 2 can be grasped in more detail. . Therefore, the features and advantages of using multi-scale entropy analysis (MSE) for sample entropy analysis can be utilized.

  As described above, in the electroencephalogram analyzer 200, the display device 20 does not display the determination result of the epilepsy lesion, but displays only information based on the sample entropy score obtained by analyzing the electroencephalogram signal. Then, based on the information displayed on the display device 20, the doctor determines an epilepsy lesion.

  Also in the present embodiment, since the epilepsy lesion can be determined using quantified numerical values such as scores and variables, simple and objectively stable determination can be performed.

  As described above, the embodiment of the present invention has been described. However, the above embodiment is only a part of an application example of the present invention, and the technical scope of the present invention is not limited to the specific configuration of the above embodiment. Absent.

  This application claims the priority based on Japanese Patent Application No. 2018-31233 filed with the Japan Patent Office on February 23, 2018, the entire content of which is incorporated herein by reference.

  The electroencephalogram analysis method and the electroencephalogram analysis device according to the present invention support epilepsy diagnosis and epilepsy treatment by a doctor, and have wide industrial applicability. In other words, the present invention can be said to be an epilepsy treatment apparatus and an epilepsy treatment method using the electroencephalogram analysis method and the electroencephalogram analysis apparatus. The method of analyzing an electroencephalogram signal is specifically applicable to computer software (applications) and programs, as well as electroencephalographs and analyzers incorporating these computer software.

  In particular, the epileptic epithelium and the epileptic seizure initiation part can be effectively evaluated by installing the electrode 1 on the scalp and acquiring an electroencephalogram signal in the intermittent epileptic seizure, so that the electroencephalogram measurement method and electroencephalogram analysis according to the present invention The device can be expected to significantly expand its industrial applicability regardless of medical practice. An electroencephalogram measurement method and an electroencephalogram analyzer based on an electroencephalogram signal in an intermittent epileptic seizure can be fundamentally different from a method and an apparatus premised on acquiring an electroencephalogram signal during an epileptic seizure or immediately before an epileptic seizure. For example, the holding mechanism for holding the electrode 1 placed on the scalp or in the skull does not correspond to various seizures such as limbs during an epileptic seizure. Further, in the future, application to an apparatus for acquiring an electroencephalogram signal at a place other than a medical facility may be considered.

According to an aspect of the present invention , based on the sample entropy score, to determine whether each brain site corresponding to the plurality of electrodes corresponds to the epileptic seizure onset or epileptogenic part , a brain wave analysis method for supporting epilepsy diagnosis acquires a brain wave signals corresponding to the plurality of electrodes placed on the head, the brain wave signal to the sample entropy analysis, corresponding to each of the plurality of electrodes calculating the sample entropy score.
Further, according to an aspect of the present invention, based on the standardized variable of the sample entropy score, whether each brain site corresponding to the plurality of electrodes corresponds to the epileptic seizure onset or epileptogenic part A brain wave analysis method for supporting an epilepsy diagnosis for determining a brain wave signal corresponding to the plurality of electrodes installed on the head, obtaining a brain wave signal by sample entropy analysis, The sample entropy score corresponding to each of the electrodes is calculated, and the sample entropy score corresponding to each of the plurality of electrodes is used as a population, and a standardized variable of the sample entropy score corresponding to each of the plurality of electrodes is calculated. calculate.
According to another aspect of the present invention, there is provided an electroencephalogram analysis program for supporting epilepsy diagnosis, comprising: a step of acquiring an electroencephalogram signal corresponding to a plurality of electrodes installed on a head; and a sample entropy analysis of the electroencephalogram signal. And calculating a sample entropy score corresponding to each of the plurality of electrodes, and, based on the sample entropy score, a part of the brain corresponding to the plurality of electrodes is an epileptic seizure onset or epilepsy Determining whether or not the image corresponds to the intrinsic part.

Claims (9)

An electroencephalogram analysis method for supporting epilepsy diagnosis,
Acquire brain wave signals corresponding to the multiple electrodes installed on the head,
The brain wave signal is subjected to sample entropy analysis to calculate a sample entropy score corresponding to each of the plurality of electrodes,
An electroencephalogram analysis method for supporting epilepsy diagnosis for determining whether or not each brain part corresponding to the plurality of electrodes corresponds to an epileptic seizure onset or epileptogenic part based on the sample entropy score. An electroencephalogram analysis method for supporting epilepsy diagnosis according to claim 1,
The sample entropy score corresponding to each of the plurality of electrodes is used as a population, and a standardized variable of a sample entropy score corresponding to each of the plurality of electrodes is calculated.
An electroencephalogram analysis method for supporting epilepsy diagnosis for determining whether or not each brain part corresponding to the plurality of electrodes corresponds to an epileptic seizure onset or epileptogenic part based on the standardized variables. An electroencephalogram analysis method for supporting epilepsy diagnosis according to claim 2,
The part of the brain corresponding to the electrode where the standardized variable is equal to or less than the first threshold is determined as an epileptic part,
An electroencephalogram analysis method for assisting epilepsy diagnosis in which a part of the brain corresponding to an electrode whose standardized variable is equal to or smaller than a second threshold smaller than the first threshold is determined to be an epileptic seizure onset. An electroencephalogram analysis method for supporting epilepsy diagnosis according to any one of claims 1 to 3,
An electroencephalogram analysis method for assisting epilepsy diagnosis, wherein the electroencephalogram signal is an electroencephalogram signal during an epileptic seizure or during an epileptic seizure. An electroencephalogram analyzer that supports epilepsy diagnosis,
An electroencephalogram signal acquisition unit that acquires electroencephalogram signals corresponding to a plurality of electrodes installed on the head,
A calculation unit that performs a sample entropy analysis of the brain wave signal and calculates a sample entropy score corresponding to each of the plurality of electrodes,
Based on the sample entropy score, each of the brain regions corresponding to the plurality of electrodes, a determination unit to determine whether the epileptic seizure onset or epileptogenic part corresponds to,
An electroencephalogram analyzer that supports epilepsy diagnosis. An electroencephalogram analyzer for supporting epilepsy diagnosis according to claim 5,
The calculation unit, the sample entropy score corresponding to each of the plurality of electrodes as a population, further calculates a standardized variable of the sample entropy score corresponding to each of the plurality of electrodes,
The determination unit assists epilepsy diagnosis to determine whether each brain site corresponding to the plurality of electrodes corresponds to an epileptic seizure onset or epileptogenic part based on the standardized variables. Brain wave analyzer. An electroencephalogram analyzer that supports epilepsy diagnosis according to claim 5 or 6,
Further comprising an input device for inputting a threshold for determination by the determination unit,
The determination unit, based on a comparison between the information based on the sample entropy score and the threshold, whether each brain site corresponding to the plurality of electrodes corresponds to an epileptic seizure onset or epileptogenic part An electroencephalogram analyzer that supports epilepsy diagnosis to determine An electroencephalogram analyzer that supports epilepsy diagnosis according to any one of claims 5 to 7,
An electroencephalogram analyzer for assisting epilepsy diagnosis, wherein the electroencephalogram signal is an electroencephalogram signal during an epileptic seizure or during an epileptic seizure. An electroencephalogram analyzer that supports epilepsy diagnosis,
An electroencephalogram signal acquisition unit that acquires electroencephalogram signals corresponding to a plurality of electrodes installed on the head,
A calculation unit that performs a sample entropy analysis of the brain wave signal and calculates a sample entropy score corresponding to each of the plurality of electrodes,
A display control unit that displays a brain image simulating the brain on a display device, and displays information based on the sample entropy score corresponding to the electrode at a position on the brain image corresponding to each of the plurality of electrodes. An electroencephalogram analyzer that supports epilepsy diagnosis.

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