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Detection method for epileptic seizure signals based on BNI

A technology for epilepsy seizures and detection methods, applied in the fields of diagnostic recording/measurement, medical science, instruments, etc., can solve problems such as unused epilepsy prediction, and achieve the effect of predicting the time in advance and perceiving the lesions early.

Active Publication Date: 2020-07-17
HANGZHOU DIANZI UNIV
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Problems solved by technology

Despite its apparent value, the BNI remains unused for epilepsy prediction purposes

Method used

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  • Detection method for epileptic seizure signals based on BNI
  • Detection method for epileptic seizure signals based on BNI
  • Detection method for epileptic seizure signals based on BNI

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Embodiment Construction

[0040]In this study, we mainly discuss the effectiveness of the prediction method from the perspective of micro-neurons: From the perspective of micro-neurons, NMM is used to fit Depth EEG signals and clarify the network structure, dynamic equations and generation of epileptic discharges The relationship between. The concept of BNI was introduced in order to quantify the pathological degree to which a given network can induce seizures. This is the first time BNI has been used as a predictor of seizures. The results show that the proposed method based on Depth EEG signals achieves good results in only four patient samples. On average, the predicted time of onset was detected 2461.74 seconds ago, which is twice as early as that of the NPDC-based method.

[0041] Such as figure 1 As shown, this embodiment includes the following steps:

[0042] Step (1), collecting EEG data and preprocessing. The Depth EEG data in the present invention are collected by ***** Children's Hospit...

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Abstract

The invention discloses a detection method for epileptic seizure signals based on brain network ictogenicity (BNI). The invention discusses the effectiveness of the prediction method from the perspective of micro-neurons, from the perspective of the micro-neurons, neural mass model (NMM) is used to fit brain depth electrode electroencephalogram (Depth EEG) signals and clarify the relationship among a network structure, a dynamic equation and epileptic discharge generation. In order to quantify the pathological degree that a given network can cause epileptic seizures, the concept of the brain network ictogenicity (BNI) is introduced, and the BNI is used as a predictor of the epileptic seizures for the first time. The method shortens the detection time, reduces the number of implanted electrodes, and can observe a good prediction effect.

Description

technical field [0001] The invention belongs to the field of brain network structure analysis, and relates to a method for detecting epileptic seizure signals by calculating Brain Network Ictogenicity (BNI) based on brain function network characteristics and modeling nerve quality. Background technique [0002] Epilepsy is a neurological disease characterized by sudden onset. Long-term frequent seizures have a serious impact on the patient's body, mind, and cognition. Unfortunately, epilepsy is the fourth most common neurological disorder, affecting more than 65 million people worldwide. Against this backdrop, it is clear that a new method that can predict seizures and protect them from harm in these patients is clearly needed. [0003] With the clarification of the epilepsy classification, studies have shown that focal epilepsy has a certain predictability. For example, a survey study conducted in the United States suggested that there may be a preictal state from the int...

Claims

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Application Information

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IPC IPC(8): A61B5/0476A61B5/00G06K9/46
CPCA61B5/4094A61B5/7235A61B5/7275A61B5/7203A61B5/725A61B5/316A61B5/369G06V10/426
Inventor 胡月静柏雨露汪茜高云园张启忠
Owner HANGZHOU DIANZI UNIV
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