Fusion Algorithm for Imbalanced EEG Data Processing in Seizure Detection.
Purpose: Seizure detection algorithms (SDAs) based on electroencephalography (EEG) have been described in previous studies, but the imbalanced data distribution of ictal and interictal states continue to pose a technical challenge. This study proposes a novel algorithm to address the imbalanced clas...
| Publicado en: | Seizure Vol. 91; pp. 207 - 212 |
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| Autores principales: | , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Oct2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=152293431&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 152293431 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10591311 O00 jtl: Seizure issn: 10591311 maglogo: N pubinfo: dt: Oct2021 vid: 91 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 152293431 152293431 NLM34229229 152293431 10.1016/j.seizure.2021.06.023 NLM34229229 152293431 ppf: 207 ppct: 5 formats: tig: atl: Fusion Algorithm for Imbalanced EEG Data Processing in Seizure Detection. aug: au: Jiang, Zhen Zhao, Wenshan affil: School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China sug: subj: Seizures Diagnosis Electroencephalography Algorithms Signal Processing, Computer Assisted Human ab: Purpose: Seizure detection algorithms (SDAs) based on electroencephalography (EEG) have been described in previous studies, but the imbalanced data distribution of ictal and interictal states continue to pose a technical challenge. This study proposes a novel algorithm to address the imbalanced classification problem and improve seizure detection performance.Method: The proposed algorithm is designed based on hybrid sampling and a cost-sensitive (CS) classifier. Hybrid sampling resamples the imbalanced EEG data at data-level, and the CS classifier, which is used as an algorithm-level tool, reduces the overall misclassification cost of seizure detection. The synthetic minority oversampling technique and undersampling TomekLink technique are combined to reduce the imbalanced ratio between ictal and interictal states while retaining the generalization ability. Finally, CS support vector machine classifies the resampled EEG feature vectors, assigning different cost sensitive parameters to moderate the poor performance resulting from the imbalanced distribution problem.Result: The proposed algorithm improved the average sensitivity and AUC by 46.67% and 0.0482, respectively, compared with the original results without using the imbalanced EEG data processing (IEDP) technique. Experimental results showed that an average sensitivity and AUC of 86.34% and 0.9837, respectively, could be obtained across all cases. Finally, a performance evaluation showed that the proposed algorithm outperformed published methods in terms seizure detection.Conclusion: A fusion algorithm combining data- and algorithm-level methods can achieve high sensitivity and AUC compared with existing IEDP methods. Thus, SDA performance can be improved, enabling their clinical use with EEG-based SMSs. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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