Using interictal seizure-free EEG data to recognise patients with epilepsy based on machine learning of brain functional connectivity
文献类型：期刊论文
| 作者 | Cao, Jun4; Grajcar, Kacper4; Shan, Xiaocai4,5; Zhao, Yifan4; Zou, Jiaru4,5; Chen, Liangyu1; Li, Zhiqing1; Grunewald, Richard2; Zis, Panagiotis2; De Marco, Matteo2 |
| 刊名 | BIOMEDICAL SIGNAL PROCESSING AND CONTROL
 |
| 出版日期 | 2021-05-01 |
| 卷号 | 67页码:13 |
| ISSN号 | 1746-8094 |
| 关键词 | qEEG Classification Brain connectivity Correlation Coherence |
| DOI | 10.1016/j.bspc.2021.102554 |
| 英文摘要 | Most seizures in adults with epilepsy occur rather infrequently and as a result, the interictal EEG plays a crucial role in the diagnosis and classification of epilepsy. However, empirical interpretation, of a first EEG in adult patients, has a very low sensitivity ranging between 29?55 %. Useful EEG information remains buried within the signals in seizure-free EEG epochs, far beyond the observational capabilities of any specialised physician in this field. Unlike most of the existing works focusing on either seizure data or single-variate method, we introduce a multi-variate method to characterise sensor level brain functional connectivity from interictal EEG data to identify patients with generalised epilepsy. A total of 9 connectivity features based on 5 different measures in time, frequency and time-frequency domains have been tested. The solution has been validated by the K-Nearest Neighbour algorithm, classifying an epilepsy group (EG) vs healthy controls (HC) and subsequently with another cohort of patients characterised by non-epileptic attacks (NEAD), a psychogenic type of disorder. A high classification accuracy (97 %) was achieved for EG vs HC while revealing significant spatio-temporal deficits in the frontocentral areas in the beta frequency band. For EG vs NEAD, the classification accuracy was only about 73 %, which might be a reflection of the well-described coexistence of NEAD with epileptic attacks. Our work demonstrates that seizure-free interictal EEG data can be used to accurately classify patients with generalised epilepsy from HC and that more systematic work is required in this direction aiming to produce a clinically useful diagnostic method. |
| WOS关键词 | SCALP EEG ; COMMON ; SYNCHRONIZATION ; WAKEFULNESS ; DIAGNOSIS ; NETWORK ; MODEL |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| 出版者 | ELSEVIER SCI LTD |
| WOS记录号 | WOS:000640912500005 |
| 源URL | [http://ir.iggcas.ac.cn/handle/132A11/101157]  |
| 专题 | 中国科学院地质与地球物理研究所 |
| 通讯作者 | Chen, Liangyu |
| 作者单位 | 1.China Med Univ, Shengjing Hosp, Dept Neurosurg, Shenyang, Peoples R China 2.Sheffield Teaching Hosp NHS Fdn Trust, Royal Hallamshire Hosp, Dept Neurosci, Sheffield, S Yorkshire, England 3.Royal Devon & Exeter NHS Fdn Trust, Exeter EX2 5DW, Devon, England 4.Cranfield Univ, Sch Aerosp Transport & Mfg, Cranfield MK43 0AL, Beds, England 5.Chinese Acad Sci, Inst Geol & Geophys, Beijing 100029, Peoples R China |
| 推荐引用方式 GB/T 7714 | Cao, Jun,Grajcar, Kacper,Shan, Xiaocai,et al. Using interictal seizure-free EEG data to recognise patients with epilepsy based on machine learning of brain functional connectivity[J]. BIOMEDICAL SIGNAL PROCESSING AND CONTROL,2021,67:13. |
| APA | Cao, Jun.,Grajcar, Kacper.,Shan, Xiaocai.,Zhao, Yifan.,Zou, Jiaru.,...&Sarrigiannis, Ptolemaios G..(2021).Using interictal seizure-free EEG data to recognise patients with epilepsy based on machine learning of brain functional connectivity.BIOMEDICAL SIGNAL PROCESSING AND CONTROL,67,13. |
| MLA | Cao, Jun,et al."Using interictal seizure-free EEG data to recognise patients with epilepsy based on machine learning of brain functional connectivity".BIOMEDICAL SIGNAL PROCESSING AND CONTROL 67(2021):13. |
入库方式： OAI收割
来源：地质与地球物理研究所
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