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KDD 2022 | epileptic wave prediction based on hierarchical graph diffusion learning

2022-06-23 13:21:00 Zhiyuan community

subject :BrainNet: Using hierarchical graph diffusion learning in SEEG Epileptic waves are predicted in the data (BrainNet: Epileptic Wave Detection from SEEG with Hierarchical Graph Diffusion Learning)

 

author : Chenjunru ( Zhejiang University ), Yang Yang ( Zhejiang University ), Yu Tao ( Zhejiang University ), Fanyingying ( Zhejiang University ), Moxiaolong ( Noel Medical ), Yang Ji ( Emory University )

 

meeting :28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

 

Epilepsy is one of the most common serious neurological diseases , It is characterized by abnormal neurophysiological activities , Characterized by seizures or behavioral abnormalities . By the end of 2021 year , There are more than 6,500 Ten thousand people with epilepsy , About one-third of epilepsy patients are resistant to drugs . let me put it another way , Drugs cannot effectively control the condition of these patients , Surgical removal of brain regions involved in seizures is considered the only effective treatment . To assess the location of the seizure area (SOZ) Or so-called epileptic foci , And guide epilepsy surgery , It is necessary to record the discharge activity of the patient's brain . There are usually two types of electrophysiological monitoring methods : Cortical EEG (EEG) And stereoscopic EEG (SEEG). The former is non-invasive , The latter is intrusive ( That is, electrodes need to be inserted into the brain ), Therefore, it also contains more deep three-dimensional information . for example , When SOZ Located in the deep structure of the brain ( For example, hippocampus or insular body ) in , Or when the laterality of seizures is unknown , Noninvasive detection will not be able to locate the exact seizure focus , here ,SEEG Methods are necessary .

 

In order to promote the development of epilepsy treatment , In this paper, the research team from the third class hospital , A collection of data based on real patient records SEEG Data sets , The data set contains high-frequency multi-channel data of multiple epileptic patients SEEG The signal ( On average, each patient has a continuous 53 Hours 、 The size is 77GB The record of ). Based on this dataset , The task of automatic prediction of epileptic waves is further proposed and studied in this paper . This paper aims to 1 As an example , Further introduce the details of this paper . chart 1(a) The top of the shows a human brain , An electrode with three channels is inserted in the upper left corner of the human brain . After inserting electrode , The doctor can collect and monitor the patient's SEEG data ; Pictured 1(a) At the bottom of the , Actually SEEG Data can be considered as continuous multi-channel time series . get SEEG After the data , The goal of this paper is to construct an automatic data-driven method to determine the time and location of epileptic waves ( In the figure 1(a) Marked with a yellow rectangle ).
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