Epileptic Seizure Prediction Based on Permutation Entropy
Epilepsy is a chronic non-communicable disorder of the brain that affects individuals of all ages. It is caused by a sudden abnormal discharge of brain neurons leading to temporary dysfunction. In this regard, if seizures could be predicted a reasonable period of time before their occurrence, epilep...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2018-07-01
|
| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fncom.2018.00055/full |
| _version_ | 1818189710572388352 |
|---|---|
| author | Yanli Yang Mengni Zhou Yan Niu Conggai Li Rui Cao Bin Wang Pengfei Yan Yao Ma Jie Xiang |
| author_facet | Yanli Yang Mengni Zhou Yan Niu Conggai Li Rui Cao Bin Wang Pengfei Yan Yao Ma Jie Xiang |
| author_sort | Yanli Yang |
| collection | DOAJ |
| description | Epilepsy is a chronic non-communicable disorder of the brain that affects individuals of all ages. It is caused by a sudden abnormal discharge of brain neurons leading to temporary dysfunction. In this regard, if seizures could be predicted a reasonable period of time before their occurrence, epilepsy patients could take precautions against them and improve their safety and quality of life. However, the potential that permutation entropy(PE) can be applied in human epilepsy prediction from intracranial electroencephalogram (iEEG) recordings remains unclear. Here, we described the novel application of PE to track the dynamical changes of human brain activity from iEEG recordings for the epileptic seizure prediction. The iEEG signals of 19 patients were obtained from the Epilepsy Centre at the University Hospital of Freiburg. After preprocessing, PE was extracted in a sliding time window, and a support vector machine (SVM) was employed to discriminate cerebral state. Then a two-step post-processing method was applied for the purpose of prediction. The results showed that we obtained an average sensitivity (SS) of 94% and false prediction rates (FPR) with 0.111 h−1. The best results with SS of 100% and FPR of 0 h−1 were achieved for some patients. The average prediction horizon was 61.93 min, leaving sufficient treatment time before a seizure. These results indicated that applying PE as a feature to extract information and SVM for classification could predict seizures, and the presented method shows great potential in clinical seizure prediction for human. |
| first_indexed | 2024-12-11T23:47:09Z |
| format | Article |
| id | doaj.art-854e64f298244230a0b8ffefe8fbab6d |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-12-11T23:47:09Z |
| publishDate | 2018-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-854e64f298244230a0b8ffefe8fbab6d2022-12-22T00:45:36ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882018-07-011210.3389/fncom.2018.00055377469Epileptic Seizure Prediction Based on Permutation EntropyYanli Yang0Mengni Zhou1Yan Niu2Conggai Li3Rui Cao4Bin Wang5Pengfei Yan6Yao Ma7Jie Xiang8College of Information and Computer Science, Taiyuan University of Technology, Taiyuan, ChinaCollege of Information and Computer Science, Taiyuan University of Technology, Taiyuan, ChinaCollege of Information and Computer Science, Taiyuan University of Technology, Taiyuan, ChinaCentre for AI, Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW, AustraliaSoftware College, Taiyuan University of Technology, Taiyuan, ChinaCollege of Information and Computer Science, Taiyuan University of Technology, Taiyuan, ChinaCollege of Information and Computer Science, Taiyuan University of Technology, Taiyuan, ChinaCollege of Information and Computer Science, Taiyuan University of Technology, Taiyuan, ChinaCollege of Information and Computer Science, Taiyuan University of Technology, Taiyuan, ChinaEpilepsy is a chronic non-communicable disorder of the brain that affects individuals of all ages. It is caused by a sudden abnormal discharge of brain neurons leading to temporary dysfunction. In this regard, if seizures could be predicted a reasonable period of time before their occurrence, epilepsy patients could take precautions against them and improve their safety and quality of life. However, the potential that permutation entropy(PE) can be applied in human epilepsy prediction from intracranial electroencephalogram (iEEG) recordings remains unclear. Here, we described the novel application of PE to track the dynamical changes of human brain activity from iEEG recordings for the epileptic seizure prediction. The iEEG signals of 19 patients were obtained from the Epilepsy Centre at the University Hospital of Freiburg. After preprocessing, PE was extracted in a sliding time window, and a support vector machine (SVM) was employed to discriminate cerebral state. Then a two-step post-processing method was applied for the purpose of prediction. The results showed that we obtained an average sensitivity (SS) of 94% and false prediction rates (FPR) with 0.111 h−1. The best results with SS of 100% and FPR of 0 h−1 were achieved for some patients. The average prediction horizon was 61.93 min, leaving sufficient treatment time before a seizure. These results indicated that applying PE as a feature to extract information and SVM for classification could predict seizures, and the presented method shows great potential in clinical seizure prediction for human.https://www.frontiersin.org/article/10.3389/fncom.2018.00055/fullepilepsyelectroencephalogrampermutation entropypredictionsupport vector machine (SVM) |
| spellingShingle | Yanli Yang Mengni Zhou Yan Niu Conggai Li Rui Cao Bin Wang Pengfei Yan Yao Ma Jie Xiang Epileptic Seizure Prediction Based on Permutation Entropy Frontiers in Computational Neuroscience epilepsy electroencephalogram permutation entropy prediction support vector machine (SVM) |
| title | Epileptic Seizure Prediction Based on Permutation Entropy |
| title_full | Epileptic Seizure Prediction Based on Permutation Entropy |
| title_fullStr | Epileptic Seizure Prediction Based on Permutation Entropy |
| title_full_unstemmed | Epileptic Seizure Prediction Based on Permutation Entropy |
| title_short | Epileptic Seizure Prediction Based on Permutation Entropy |
| title_sort | epileptic seizure prediction based on permutation entropy |
| topic | epilepsy electroencephalogram permutation entropy prediction support vector machine (SVM) |
| url | https://www.frontiersin.org/article/10.3389/fncom.2018.00055/full |
| work_keys_str_mv | AT yanliyang epilepticseizurepredictionbasedonpermutationentropy AT mengnizhou epilepticseizurepredictionbasedonpermutationentropy AT yanniu epilepticseizurepredictionbasedonpermutationentropy AT conggaili epilepticseizurepredictionbasedonpermutationentropy AT ruicao epilepticseizurepredictionbasedonpermutationentropy AT binwang epilepticseizurepredictionbasedonpermutationentropy AT pengfeiyan epilepticseizurepredictionbasedonpermutationentropy AT yaoma epilepticseizurepredictionbasedonpermutationentropy AT jiexiang epilepticseizurepredictionbasedonpermutationentropy |