Key electrode contact identification and electrical stimulation control method based on high-frequency oscillation

Abstract

The present invention discloses a method for identifying key electrode contacts and controlling electrical stimulation based on high-frequency oscillation. The contact identification method includes: first, collecting SEEG electroencephalogram signals before and after epileptic seizures through an electrode contact network, then identifying high-frequency oscillation signals in the SEEG electroencephalogram signals, extracting the high-frequency oscillation signals corresponding to each electrode contact, and finally, determining the key electrode contacts for epilepsy monitoring by performing correlation analysis on the high-frequency oscillation signals corresponding to different electrode contacts before and after epileptic seizures. The control method includes: first, identifying the key electrode contacts using the contact identification method, then real-time monitoring the high-frequency oscillation signals of the key electrode contacts, and finally determining whether electrical stimulation is required based on the high-frequency oscillation signals of the key electrode contacts. In response to the need for electrical stimulation, controlling the key electrode contacts to output electrical pulses.

Description

Technical Field

[0001] The present invention relates to the technical field of electroencephalogram analysis, and particularly to a method for identifying key electrode contacts based on high-frequency oscillations and controlling electrical stimulation. Background Art

[0002] Epilepsy is a chronic disease in which the brain neurons suddenly discharge abnormally, resulting in a transient brain dysfunction. It is the second largest disease of the nervous system. Approximately 70% of epilepsy patients can be controlled by drugs, and the remaining patients are drug-resistant epilepsy patients who require surgical treatment, with a cure rate of about 40% - 90%. However, due to the current medical technology limitations, reasons such as the inability to accurately locate the pre-operative assessment or the epileptogenic focus being located in an important functional area, most patients are not suitable for surgical resection. Such patients need to rely on neuromodulation to relieve epileptic seizures, so how to determine the modulation area becomes particularly important. Summary of the Invention

[0003] In view of the deficiencies in the prior art, the present invention proposes a method for identifying key electrode contacts based on high-frequency oscillations and controlling electrical stimulation. It can determine the key electrode contacts in the epilepsy monitoring network and perform electrical stimulation on the area corresponding to the key electrode contacts.

[0004] In a first aspect, a method for identifying key electrode contacts for epilepsy monitoring based on high-frequency oscillations is provided, including:

[0005] Collecting SEEG electroencephalogram signals before and after epileptic seizures through an electrode contact network;

[0006] Identifying high-frequency oscillation signals in the SEEG electroencephalogram signals and extracting the high-frequency oscillation signals corresponding to each electrode contact;

[0007] Determining the key electrode contacts for epilepsy monitoring by performing correlation analysis on the high-frequency oscillation signals corresponding to different electrode contacts before and after epileptic seizures.

[0008] Combined with the first aspect, in the first possible implementation manner of the first aspect, the identifying of the high-frequency oscillation signals in the SEEG electroencephalogram signals includes:

[0009] Calculating the envelope of the SEEG electroencephalogram signals through Hilbert transform and calculating the screening threshold according to the envelope;

[0010] Screening out the segment signals in the SEEG electroencephalogram signals according to the screening threshold;

[0011] Calculating the Stockwell entropy of each segment signal and determining the amplitude threshold according to the maximum Stockwell entropy;

[0012] Identifying the high-frequency oscillation signals in the SEEG electroencephalogram signals according to the amplitude threshold.

[0013] Combined with the first implementation manner of the first aspect, in the second implementation manner of the first aspect, a signal whose envelope exceeds the amplitude threshold and whose time width exceeds the preset time width threshold is a high-frequency oscillation signal.

[0014] Combined with the first aspect, in the third implementation manner of the first aspect, determining the key electrode contacts through correlation analysis includes:

[0015] By performing correlation analysis on the corresponding high-frequency oscillation signals, the cross-correlation values between different electrode contacts before a seizure and the cross-correlation values between different electrode contacts after a seizure are respectively calculated;

[0016] According to the brain regions to which the electrode contacts belong, the average cross-correlation values between different brain regions before a seizure and the average cross-correlation values between different brain regions after a seizure are calculated;

[0017] The average cross-correlation values between different brain regions before and after the seizure are compared to determine the key brain region, and the electrode contacts corresponding to the key brain region are the key electrode contacts.

[0018] In a second aspect, a method for controlling electrical stimulation based on high-frequency oscillations is provided, including:

[0019] Collecting SEEG electroencephalogram signals before and after a seizure through an electrode contact network;

[0020] Identifying high-frequency oscillation signals in the SEEG electroencephalogram signals and extracting the high-frequency oscillation signals corresponding to each electrode contact;

[0021] Determining the key electrode contacts for epilepsy monitoring by performing correlation analysis on the high-frequency oscillation signals corresponding to different electrode contacts before and after a seizure;

[0022] Real-time monitoring of the high-frequency oscillation signals of the key electrode contacts;

[0023] Based on the high-frequency oscillation signals of the key electrode contacts, determining whether electrical stimulation is required, and in response to the need for electrical stimulation, controlling the key electrode contacts to output electrical pulses.

[0024] Combined with the second aspect, in the first implementation manner of the second aspect, the identifying the high-frequency oscillation signals in the SEEG electroencephalogram signals includes:

[0025] Calculating the envelope of the SEEG electroencephalogram signal through Hilbert transform and calculating the screening threshold according to the envelope;

[0026] Filtering out the segment signals in the SEEG electroencephalogram signal according to the screening threshold;

[0027] Calculate the Stockwell entropy of each segment signal and determine the amplitude threshold based on the maximum Stockwell entropy;

[0028] Identify high-frequency oscillation signals in the SEEG electroencephalogram signal according to the amplitude threshold.

[0029] Combined with the first implementation manner of the second aspect, in the second implementation manner of the second aspect, a signal whose envelope exceeds the amplitude threshold and whose time width exceeds the preset time width threshold is a high-frequency oscillation signal.

[0030] Combined with the second aspect, in the third implementation manner of the second aspect, determining the key electrode contacts through correlation analysis includes:

[0031] By performing correlation analysis on the corresponding high-frequency oscillation signals, calculate the cross-correlation values between different electrode contacts before and after epileptic seizures respectively;

[0032] According to the brain regions to which the electrode contacts belong, calculate the average cross-correlation values between different brain regions before epileptic seizures and the average cross-correlation values between different brain regions after epileptic seizures;

[0033] Compare the average cross-correlation values between different brain regions before and after seizures to determine the key brain region, and the electrode contacts corresponding to the key brain region are the key electrode contacts.

[0034] Combined with the second aspect, in the fourth implementation manner of the second aspect, determining whether electrical stimulation is required based on the high-frequency oscillation signals of the key electrode contacts includes:

[0035] Classify each time point of the high-frequency oscillation signal through a support vector machine;

[0036] Predict the probability of epileptic seizures according to the classification results;

[0037] Determine whether the probability of epileptic seizures exceeds the preset threshold, and in response to the probability of epileptic seizures exceeding the preset threshold, control the key electrode contacts to output electrical pulses.

[0038] Combined with the fourth implementation manner of the second aspect, in the fifth implementation manner of the second aspect, it further includes: in response to the probability of epileptic seizures exceeding the preset threshold, determine the predicted time of epileptic seizures, and in response to the predicted time being greater than 0, control the key electrode contacts to output electrical pulses.

[0039] Beneficial effects: By adopting the key electrode contact recognition and electrical stimulation control method based on high-frequency oscillation of the present invention, the key electrode contacts corresponding to the key brain regions for epilepsy monitoring can be determined through the key electrode contact recognition method. An epilepsy monitoring network can be constructed through the key electrode contacts to continuously monitor the high-frequency oscillation signals of each key brain region, and by analyzing the high-frequency oscillation signals of the key brain regions, corresponding electrode contacts are controlled to emit electrical pulses to regulate the key brain regions. Description of the Drawings

[0040] In order to more clearly illustrate the specific embodiments of the present invention, the drawings required for use in the specific embodiments will be briefly introduced below. In all the drawings, the elements or parts are not necessarily drawn to actual scale.

[0041] Figure 1 Flowchart of the key electrode contact recognition method for epilepsy monitoring based on high-frequency oscillation provided by an embodiment of the present invention;

[0042] Figure 2 For Figure 1 Flowchart of the recognition method for high-frequency oscillation signals in the key electrode contact recognition method for epilepsy monitoring based on high-frequency oscillation shown;

[0043] Figure 3 For Figure 1 Flowchart of the method for determining key electrode contacts through correlation analysis in the key electrode contact recognition method for epilepsy monitoring based on high-frequency oscillation shown;

[0044] Figure 4 Flowchart of the electrical stimulation control method based on high-frequency oscillation provided by an embodiment of the present invention;

[0045] Figure 5 Schematic diagram of SEEG electroencephalogram signals collected in an embodiment of the present invention;

[0046] Figure 6 Schematic diagram of high-frequency oscillation signals recognized by using the high-frequency oscillation signal recognition method provided by the present invention. Specific Embodiments

[0047] The embodiments of the technical solutions of the present invention will be described in detail below with reference to the drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention, and thus are only examples and should not be used to limit the protection scope of the present invention.

[0048] As Figure 1 The flowchart of the key electrode contact recognition method for epilepsy monitoring based on high-frequency oscillation shown, the recognition method includes:

[0049] Step 1, collect SEEG electroencephalogram signals before and after epileptic seizures through an electrode contact network;

[0050] Step 2: Identify the high-frequency oscillation signals in the SEEG electroencephalogram signals and extract the high-frequency oscillation signals corresponding to each electrode contact;

[0051] Step 3: Determine the key electrode contacts for epilepsy monitoring by performing a correlation analysis on the high-frequency oscillation signals corresponding to different electrode contacts before and after an epileptic seizure.

[0052] Specifically, first, the SEEG electroencephalogram signals before and after an epileptic seizure can be collected through an electrode contact network. The electrode contact network includes multiple electrodes implanted into the patient's intracranial cavity according to a preoperative design plan. All electrodes can be signal-connected to a digital electroencephalograph recorder. Through these electrodes, the digital electroencephalograph recorder can collect the SEEG electroencephalogram signals of the corresponding electrode contacts. The collected SEEG electroencephalogram signals are as Figure 5 shown. It should be understood that the implantation method of the electrodes and the connection method with the digital electroencephalograph recorder both belong to the prior art and will not be elaborated here.

[0053] Then, the high-frequency oscillation signals in the collected SEEG electroencephalogram signals can be identified, and the high-frequency oscillation signals corresponding to each electrode contact when a high-frequency oscillation event occurs can be extracted to obtain the high-frequency oscillation signals of each electrode contact before an epileptic seizure and the high-frequency oscillation signals of each electrode contact after an epileptic seizure.

[0054] Finally, the correlation of the high-frequency oscillation signals of each electrode contact before an epileptic seizure can be analyzed to determine the correlation between the electrode contacts before an epileptic seizure. And the correlation of the high-frequency oscillation signals of each electrode contact after an epileptic seizure can be analyzed to determine the correlation between the electrode contacts after an epileptic seizure. The key electrodes in the electrode contact network, that is, the key electrode contacts for epilepsy monitoring, can be determined through the change in the correlation between the electrode contacts before and after an epileptic seizure.

[0055] In this embodiment, optionally, as Figure 2 shown, the identification of the high-frequency oscillation signals in the SEEG electroencephalogram signals includes:

[0056] Step 2-1: Calculate the envelope of the SEEG electroencephalogram signals through Hilbert transform and calculate the screening threshold according to the envelope;

[0057] Step 2-2: Screen out the segment signals in the SEEG electroencephalogram signals according to the screening threshold;

[0058] Step 2-3: Calculate the Stockwell entropy of each segment signal and determine the amplitude threshold according to the maximum Stockwell entropy;

[0059] Step 2-4: Identify high-frequency oscillation signals in the SEEG electroencephalogram signals according to the amplitude threshold.

[0060] Specifically, first, the Hilbert transform can be used to calculate the envelope of the SEEG electroencephalogram signals. It should be understood that in the technical solution of this embodiment, the Hilbert transform is used to calculate the signal envelope, but the present invention is not limited thereto, and other methods can also be used to calculate the signal envelope, such as the fast Fourier transform, empirical mode decomposition method, Hilbert-Huang transform, etc.

[0061] The screening threshold for pre-screening the SEEG electroencephalogram signals can be calculated through the calculated envelope. The calculation method of the screening threshold is as follows:

[0062] Threshold=E+3SD

[0063]

[0064]

[0065] where N represents the number of points of the envelope A(t).

[0066] Then, screen the SEEG electroencephalogram signals of each electrode contact according to the screening threshold, and screen out the segment signals in the SEEG electroencephalogram signals whose envelope is greater than the screening threshold.

[0067] After that, the Stockwell entropy calculation method is used to calculate the Stockwell entropy of each screened segment signal. The specific calculation method is as follows:

[0068]

[0069]

[0070]

[0071]

[0072] τ=1,2,......,N τ ,n=1,2,......,N τ

[0073] where SE(τ) is the Stockwell entropy of a certain segment at the τ point, N τ is the length of the segment, T is the sampling period, Q is the number of frequency division points, p(m,τ) is the normalized Stockwell entropy, seg(τ) is the segment signal in the SEEG electroencephalogram signal whose envelope is greater than the screening threshold, is the Stockwell entropy transform of seg(τ).

[0074] After calculating the Stockwell entropy of all segment signals, compare the Stockwell entropy of all segment signals to determine the maximum Stockwell entropy, and determine the amplitude threshold for screening high-frequency oscillation signals based on the maximum Stockwell entropy. In the technical solution of this embodiment, 70% of the maximum Stockwell entropy is defined as the amplitude threshold. It should be understood that the embodiment of the present invention defines 70% of the maximum Stockwell entropy as the amplitude threshold, but the present invention is not limited thereto.

[0075] Finally, filter the SEEG electroencephalogram signals of each electrode contact through an FIR equiripple filter with a passband of 80 - 490 Hz, stopbands of 70 and 500 Hz, and a stopband attenuation of 60 dB, calculate the envelope of the filtered signal, and compare the envelope of the processed filtered signal with the amplitude threshold. Among them, the filtered signal whose envelope exceeds the amplitude threshold and whose time width exceeds the preset time width threshold is a high-frequency oscillation signal.

[0076] In this embodiment, optionally, as Figure 3 shown, determining the key electrode contacts through correlation analysis includes:

[0077] Step 3-1: Through correlation analysis of the corresponding high-frequency oscillation signals, calculate the cross-correlation values between different electrode contacts before and after epileptic seizures respectively;

[0078] Step 3-2: According to the brain regions to which the electrode contacts belong, calculate the average cross-correlation values between different brain regions before epileptic seizures and the average cross-correlation values between different brain regions after epileptic seizures;

[0079] Step 3-3: Compare the average cross-correlation values between different brain regions before and after seizures to determine the key brain regions, and the electrode contacts corresponding to the key brain regions are the key electrode contacts.

[0080] Specifically, first, for each high-frequency oscillation signal of all the extracted electrode contacts, the cross-correlation values between different electrode contacts before epileptic seizures and the cross-correlation values between different electrode contacts after epileptic seizures can be calculated according to the start time of the high-frequency oscillation signal. The specific calculation method of the cross-correlation value is as follows:

[0081]

[0082]

[0083]

[0084] where x a 、ya High-frequency oscillation signals respectively for two electrode contacts, a is the time width of the high-frequency oscillation signal, E is the expected value operator, L is the vector length of the high-frequency oscillation signal, and c(b) is the cross-correlation value.

[0085] Then, according to the positions of each electrode contact, the brain regions corresponding to each electrode contact are determined. The brain regions refer to the various regions divided by modern medicine for the brain. Based on the cross-correlation values between the electrode contacts corresponding to each brain region before the seizure and the cross-correlation values between the electrode contacts corresponding to each brain region after the seizure, the Wilcoxon rank sum test method can be used to statistically calculate the average cross-correlation value between each brain region before the seizure and the average cross-correlation value between each brain region after the seizure respectively.

[0086] Finally, the average cross-correlation value between each brain region before the seizure is compared with the average cross-correlation value between each brain region after the seizure. If the average cross-correlation value between two brain regions after the seizure is significantly higher or lower than the average cross-correlation value between the same two brain regions before the seizure, it indicates that the cross-correlation between these two brain regions is significantly enhanced or weakened. These two brain regions belong to the key brain regions for epilepsy monitoring, and the electrode contacts located within the regions of these two brain regions are the key electrode contacts.

[0087] It should be understood that in the technical solution of this embodiment, the average cross-correlation value between two brain regions after the seizure being significantly higher or lower than the average cross-correlation value between two brain regions before the seizure can be evaluated through the P value obtained from the significance test in statistics. Generally, P < 0.05 indicates a statistical difference, P < 0.01 indicates a significant statistical difference, and P < 0.001 indicates an extremely significant statistical difference. Its meaning is that the probability that the difference between samples is caused by sampling error is less than 0.05, 0.01, 0.001, but the present invention is not limited to this.

[0088] As Figure 4 shown in the flowchart of the electrical stimulation control method based on high-frequency oscillation, the control method includes:

[0089] S1. Collect SEEG electroencephalogram signals before and after epilepsy seizures through an electrode contact network;

[0090] S2. Identify high-frequency oscillation signals in the SEEG electroencephalogram signals and extract the high-frequency oscillation signals corresponding to each electrode contact;

[0091] S3. Determine the key electrode contacts for epilepsy monitoring by performing correlation analysis on the high-frequency oscillation signals corresponding to different electrode contacts before and after epilepsy seizures;

[0092] S4. Real-time monitor the high-frequency oscillation signals of the key electrode contacts;

[0093] S5. Determine whether electrical stimulation is required based on the high-frequency oscillation signal of the key electrode contact. In response to the need for electrical stimulation, control the key electrode contact to output electrical pulses.

[0094] Specifically, first, the SEEG electroencephalogram signals before and after epileptic seizures can be collected through an electrode contact network. The electrode contact network includes multiple electrodes implanted in the patient's intracranial according to the preoperative design plan. All electrodes can be signal-connected to a digital electroencephalograph. Through these electrodes, the digital electroencephalograph can collect the SEEG electroencephalogram signals of the corresponding electrode contacts. The collected SEEG electroencephalogram signals are as Figure 5 shown. It should be understood that the implantation method of the electrodes and the connection method with the digital electroencephalograph both belong to the prior art and will not be elaborated here.

[0095] Then, the high-frequency oscillation signals in the collected SEEG electroencephalogram signals can be identified, and the high-frequency oscillation signals of each electrode contact can be extracted, obtaining the high-frequency oscillation signals of each electrode contact before epileptic seizures and the high-frequency oscillation signals of each electrode contact after epileptic seizures.

[0096] After that, the correlation of the high-frequency oscillation signals of each electrode contact before epileptic seizures can be analyzed to determine the correlation between the electrode contacts before epileptic seizures. And the correlation of the high-frequency oscillation signals of each electrode contact after epileptic seizures can be analyzed to determine the correlation between the electrode contacts after epileptic seizures. The key electrodes in the electrode contact network, that is, the key electrode contacts for epilepsy monitoring, can be determined through the change in the correlation between the electrode contacts before and after epileptic seizures.

[0097] Then, the SEEG electroencephalogram signals of the key electrode contacts are collected in real time through the digital electroencephalograph, and the high-frequency oscillation signals in the collected SEEG electroencephalogram signals are identified. Finally, it is determined whether electrical stimulation of the brain is required according to the identified high-frequency oscillation signals of the key electrode contacts. If not, the SEEG electroencephalogram signals of the key electrode contacts continue to be collected. Otherwise, electrical stimulation is performed using an existing electrical stimulator.

[0098] In this embodiment, optionally, as Figure 2 shown, the identification of the high-frequency oscillation signals in the SEEG electroencephalogram signals includes:

[0099] S2-1. Calculate the envelope of the SEEG electroencephalogram signal through Hilbert transform and calculate the screening threshold according to the envelope;

[0100] S2-2. Screen out the segment signals in the SEEG electroencephalogram signal according to the screening threshold;

[0101] S2-3. Calculate the Stockwell entropy of each segment signal, and determine the amplitude threshold according to the maximum Stockwell entropy;

[0102] S2-4. Identify high-frequency oscillation signals in the SEEG electroencephalogram signals according to the amplitude threshold.

[0103] Specifically, first, the Hilbert transform can be used to calculate the envelope of the SEEG electroencephalogram signal. It should be understood that in the technical solution of this embodiment, the Hilbert transform is used to calculate the signal envelope, but the present invention is not limited thereto, and other methods can also be used to calculate the signal envelope, such as the fast Fourier transform, the empirical mode decomposition method, the Hilbert-Huang transform, etc.

[0104] The screening threshold for pre-screening the SEEG electroencephalogram signal can be calculated through the calculated envelope. The calculation method of the screening threshold is as follows:

[0105] Threshold = E + 3SD

[0106]

[0107]

[0108] where N represents the number of points of the envelope A(t).

[0109] Then, screen the SEEG electroencephalogram signals of each electrode contact according to the screening threshold, and screen out the segment signals in the SEEG electroencephalogram signals whose envelopes are greater than the screening threshold.

[0110] After that, use the Stockwell entropy calculation method to calculate the Stockwell entropy of each screened segment signal. The specific calculation method is as follows:

[0111]

[0112]

[0113]

[0114]

[0115] τ = 1, 2,......, N τ , n = 1, 2,......, N τ

[0116] where SE(τ) is the Stockwell entropy of a certain segment at the τ point, N τis the length of the segment, T is the sampling period, Q is the number of frequency division points, p(m,τ) is the normalized Stockwell entropy, and seg(τ) is the segment signal in the SEEG electroencephalogram signal screened according to the screening threshold. is the Stockwell entropy transform of seg(τ).

[0117] After calculating the Stockwell entropy of all segment signals, compare the Stockwell entropy of all segment signals to determine the maximum Stockwell entropy, and determine the amplitude threshold for screening high-frequency oscillation signals according to the maximum Stockwell entropy. In the technical solution of this embodiment, 70% of the maximum Stockwell entropy is defined as the amplitude threshold. It should be understood that in the embodiment of the present invention, 70% of the maximum Stockwell entropy is defined as the amplitude threshold, but the present invention is not limited thereto.

[0118] Finally, filter the SEEG electroencephalogram signal of each electrode contact through an FIR equiripple filter with a passband of 80 - 490 Hz, stopbands of 70 and 500 Hz, and a stopband attenuation of 60 dB, calculate the envelope of the filtered signal, and compare the processed envelope of the filtered signal with the amplitude threshold. Among them, the filtered signal whose envelope exceeds the amplitude threshold and whose time width exceeds the preset time width threshold is a high-frequency oscillation signal.

[0119] In this embodiment, optionally, as Figure 3 shown, determining the key electrode contacts through correlation analysis includes:

[0120] S3-1. Through correlation analysis of the corresponding high-frequency oscillation signals, calculate the cross-correlation values between different electrode contacts before epileptic seizures and the cross-correlation values between different electrode contacts after epileptic seizures respectively;

[0121] S3-2. According to the brain regions to which the electrode contacts belong, calculate the average cross-correlation values between different brain regions before epileptic seizures and the average cross-correlation values between different brain regions after epileptic seizures;

[0122] S3-3. Compare the average cross-correlation values between different brain regions before and after seizures to determine the key brain regions, and the electrode contacts corresponding to the key brain regions are the key electrode contacts.

[0123] Specifically, first, for each high-frequency oscillation signal of all electrode contacts extracted, the cross-correlation values between each electrode contact before epileptic seizures and the cross-correlation values between each electrode contact after epileptic seizures can be calculated according to the starting time of the high-frequency oscillation signal. The specific calculation method of the cross-correlation value is as follows:

[0124]

[0125]

[0126]

[0127] where x a and y a are respectively the high-frequency oscillation signals of two electrode contacts, a is the time width of the high-frequency oscillation signal, E is the expectation operator, L is the vector length of the high-frequency oscillation signal, and c(b) is the cross-correlation value.

[0128] Then, according to the position where each electrode contact is located, the brain regions corresponding to each electrode contact are determined. The brain regions refer to the various regions divided by modern medicine for the brain. Based on the cross-correlation values between the electrode contacts corresponding to each brain region before the seizure and the cross-correlation values between the electrode contacts corresponding to each brain region after the seizure, the Wilcoxon rank sum test method can be used to respectively statistically calculate the average cross-correlation values between each brain region before the seizure and the average cross-correlation values between each brain region after the seizure.

[0129] Finally, the average cross-correlation values between each brain region before the seizure are compared with the average cross-correlation values between each brain region after the seizure. If the average cross-correlation value between two brain regions after the seizure is significantly higher or lower than the average cross-correlation value between the same two brain regions before the seizure, it indicates that the cross-correlation between these two brain regions is significantly enhanced or weakened. These two brain regions are key brain regions for epilepsy monitoring, and the electrode contacts located within the coverage areas of these two brain regions are key electrode contacts.

[0130] It should be understood that in the technical solution of this embodiment, the average cross-correlation value between two brain regions after the seizure being significantly higher or lower than the average cross-correlation value between two brain regions before the seizure can be evaluated through the P value obtained from the significance test in statistics. Generally, P < 0.05 indicates a statistically significant difference, P < 0.01 indicates a significant statistically significant difference, and P < 0.001 indicates an extremely significant statistically significant difference. The meaning is that the probability that the difference between samples is caused by sampling error is less than 0.05, 0.01, 0.001, but the present invention is not limited to this.

[0131] In this embodiment, optionally, determining whether electrical stimulation is required based on the high-frequency oscillation signal of the key electrode contact includes:

[0132] S5-1. Classify each time point of the high-frequency oscillation signal through a support vector machine;

[0133] S5-2. Predict the epilepsy seizure probability according to the classification result;

[0134] S5-3. Determine whether the epilepsy seizure probability exceeds a preset threshold, and in response to the epilepsy seizure probability exceeding the preset threshold, control the key electrode contact to output an electrical pulse.

[0135] Specifically, first, the high-frequency oscillation signals of the extracted key electrode contacts can be input into a trained support vector machine for classification to determine whether each time point in the high-frequency oscillation signals belongs to the target class or the non-target class. Among them, the target class is the high-frequency oscillation signal during epileptic seizures, and the non-target class is the high-frequency oscillation signal when epilepsy has not occurred. Finally, calculate the correct classification ratio of the target class or the non-target class. This correct classification ratio is the epileptic seizure probability. If the epileptic seizure probability exceeds the preset threshold, it indicates that epilepsy will occur, and the electrical stimulator can be controlled to output electrical pulses through the electrodes of the key electrode contacts for electrical stimulation. Otherwise, no electrical stimulation is required.

[0136] In this embodiment, the SEEG electroencephalogram signals of different patients before and after epileptic seizures can be collected, and the high-frequency oscillation signals therein can be identified by using the above method. According to the firing rate of the high-frequency oscillation signals, that is, the number of high-frequency oscillation signals per unit time, a training set of the support vector machine can be constructed. The firing rate of the high-frequency oscillation signals in the 10 seconds before epileptic seizures in each key brain region can be used as the non-target class, and other time periods can be used as the target class.

[0137] Based on the training set data, a support vector machine is constructed to obtain a trained support vector machine. It should be understood that in the technical solution of this embodiment, the training process of the support vector machine belongs to the prior art and will not be elaborated here.

[0138] In this embodiment, optionally, it further includes: in response to the epileptic seizure probability exceeding the preset threshold, determining the predicted time of epileptic seizure, and in response to the predicted time being greater than 0, controlling the key electrode contacts to output electrical pulses.

[0139] Specifically, when the epileptic seizure probability exceeds the preset threshold, all time points belonging to the epileptic seizure class can be sorted in chronological order to obtain a time series, and the predicted time of epileptic seizure can be determined according to the initial time point in the time series and the time point corresponding to the preset threshold. If the predicted time is greater than 0, electrical stimulation needs to be immediately performed through the electrical stimulator. Otherwise, if the predicted time is less than or equal to 0, it indicates that epilepsy has already occurred, and it is no longer necessary to perform electrical stimulation at this time.

[0140] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered by the scope of the claims and the description of the present invention.

Claims

1. A key electrode contact recognition method for epilepsy monitoring based on high-frequency oscillation, characterized in that Including: Collecting SEEG electroencephalogram signals before and after epileptic seizures through an electrode contact network; Identifying high-frequency oscillation signals in the SEEG electroencephalogram signals and extracting the high-frequency oscillation signals corresponding to each electrode contact; Determining the key electrode contacts for epilepsy monitoring by performing correlation analysis on the high-frequency oscillation signals corresponding to different electrode contacts before and after epileptic seizures; The identification of high-frequency oscillation signals in the SEEG electroencephalogram signals includes: Calculating the envelope of the SEEG electroencephalogram signals through Hilbert transform and calculating the screening threshold according to the envelope; Screening out the segment signals in the SEEG electroencephalogram signals according to the screening threshold; Calculating the Stockwell entropy of each segment signal and determining the amplitude threshold according to the maximum Stockwell entropy; Filtering the SEEG electroencephalogram signals of each electrode contact, calculating the envelope of the filtered signals, and the signals whose envelope of the filtered signals exceeds the amplitude threshold and the time width exceeds the preset time width threshold are high-frequency oscillation signals; Determining the key electrode contacts through correlation analysis includes: By performing correlation analysis on the corresponding high-frequency oscillation signals, calculating the cross-correlation values between different electrode contacts before epileptic seizures and the cross-correlation values between different electrode contacts after epileptic seizures respectively; Calculating the average cross-correlation values between different brain regions before epileptic seizures and the average cross-correlation values between different brain regions after epileptic seizures according to the brain regions to which the electrode contacts belong; Comparing the average cross-correlation values between different brain regions before and after seizures. If the average cross-correlation value between two brain regions after seizures is significantly higher or lower than the average cross-correlation value between the same two brain regions before seizures, these two brain regions belong to the key brain regions, and the electrode contacts corresponding to the key brain regions are the key electrode contacts.

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