Comparison of empirical mode decomposition and coarse-grained procedure for detecting pre-ictal and ictal condition in electroencephalography signal
This study evaluates the use of multiscale signal analysis to detect and predict seizures by finding the ictal and pre-ictal condition in electroencephalography (EEG) recordings. There are three processing stages in this study. The first is to decompose EEG signals by using empirical mode decomposit...
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| Format: | Article |
| Language: | English |
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Elsevier
2020-01-01
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| Series: | Informatics in Medicine Unlocked |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352914820300137 |
| _version_ | 1828306388047101952 |
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| author | Inung Wijayanto Rudy Hartanto Hanung Adi Nugroho |
| author_facet | Inung Wijayanto Rudy Hartanto Hanung Adi Nugroho |
| author_sort | Inung Wijayanto |
| collection | DOAJ |
| description | This study evaluates the use of multiscale signal analysis to detect and predict seizures by finding the ictal and pre-ictal condition in electroencephalography (EEG) recordings. There are three processing stages in this study. The first is to decompose EEG signals by using empirical mode decomposition (EMD) and a coarse-grained (CG) procedure to obtain signal information in multiple scales. The second is extracting the features by calculating the fractal dimension of the decomposed signals. Eventually, k-NN, Random Forest, and support vector machine (SVM) classifiers are used to classify ictal and pre-ictal conditions. We evaluate the system using a public dataset from Bonn University. The combination of EMD with five IMFs, FD, and SVM is used for seizure detection (normal vs. ictal) and the three-class problem (normal vs. pre-ictal vs. ictal). The accuracy for seizure detection is 100%. For the three-class problem, we achieved a highest accuracy of 99.7%, and sensitivity and specificity of 99.7% and 99.9%, respectively. The combination of CG, FD, and SVM is proposed to predict a seizure (normal vs. pre-ictal) and achieves a maximum classification accuracy from 99.3% to 100%. These results indicate that the use of EMD with five IMFs is suitable for detecting seizures, while CG is suitable for predicting seizures in EEG signals. |
| first_indexed | 2024-04-13T14:43:29Z |
| format | Article |
| id | doaj.art-9222fe07886a4853a3adcbbeffa7a8ec |
| institution | Directory Open Access Journal |
| issn | 2352-9148 |
| language | English |
| last_indexed | 2024-04-13T14:43:29Z |
| publishDate | 2020-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Informatics in Medicine Unlocked |
| spelling | doaj.art-9222fe07886a4853a3adcbbeffa7a8ec2022-12-22T02:42:50ZengElsevierInformatics in Medicine Unlocked2352-91482020-01-0119100325Comparison of empirical mode decomposition and coarse-grained procedure for detecting pre-ictal and ictal condition in electroencephalography signalInung Wijayanto0Rudy Hartanto1Hanung Adi Nugroho2Department of Electrical and Information Engineering, Universitas Gadjah Mada, Yogyakarta, Indonesia; School of Electrical Engineering, Telkom University, Bandung, IndonesiaDepartment of Electrical and Information Engineering, Universitas Gadjah Mada, Yogyakarta, IndonesiaDepartment of Electrical and Information Engineering, Universitas Gadjah Mada, Yogyakarta, Indonesia; Corresponding author.This study evaluates the use of multiscale signal analysis to detect and predict seizures by finding the ictal and pre-ictal condition in electroencephalography (EEG) recordings. There are three processing stages in this study. The first is to decompose EEG signals by using empirical mode decomposition (EMD) and a coarse-grained (CG) procedure to obtain signal information in multiple scales. The second is extracting the features by calculating the fractal dimension of the decomposed signals. Eventually, k-NN, Random Forest, and support vector machine (SVM) classifiers are used to classify ictal and pre-ictal conditions. We evaluate the system using a public dataset from Bonn University. The combination of EMD with five IMFs, FD, and SVM is used for seizure detection (normal vs. ictal) and the three-class problem (normal vs. pre-ictal vs. ictal). The accuracy for seizure detection is 100%. For the three-class problem, we achieved a highest accuracy of 99.7%, and sensitivity and specificity of 99.7% and 99.9%, respectively. The combination of CG, FD, and SVM is proposed to predict a seizure (normal vs. pre-ictal) and achieves a maximum classification accuracy from 99.3% to 100%. These results indicate that the use of EMD with five IMFs is suitable for detecting seizures, while CG is suitable for predicting seizures in EEG signals.http://www.sciencedirect.com/science/article/pii/S2352914820300137Coarse-grained (CG) procedureElectroencephalography (EEG)Empirical mode decomposition (EMD)Fractal dimensionIctalPre-ictal |
| spellingShingle | Inung Wijayanto Rudy Hartanto Hanung Adi Nugroho Comparison of empirical mode decomposition and coarse-grained procedure for detecting pre-ictal and ictal condition in electroencephalography signal Informatics in Medicine Unlocked Coarse-grained (CG) procedure Electroencephalography (EEG) Empirical mode decomposition (EMD) Fractal dimension Ictal Pre-ictal |
| title | Comparison of empirical mode decomposition and coarse-grained procedure for detecting pre-ictal and ictal condition in electroencephalography signal |
| title_full | Comparison of empirical mode decomposition and coarse-grained procedure for detecting pre-ictal and ictal condition in electroencephalography signal |
| title_fullStr | Comparison of empirical mode decomposition and coarse-grained procedure for detecting pre-ictal and ictal condition in electroencephalography signal |
| title_full_unstemmed | Comparison of empirical mode decomposition and coarse-grained procedure for detecting pre-ictal and ictal condition in electroencephalography signal |
| title_short | Comparison of empirical mode decomposition and coarse-grained procedure for detecting pre-ictal and ictal condition in electroencephalography signal |
| title_sort | comparison of empirical mode decomposition and coarse grained procedure for detecting pre ictal and ictal condition in electroencephalography signal |
| topic | Coarse-grained (CG) procedure Electroencephalography (EEG) Empirical mode decomposition (EMD) Fractal dimension Ictal Pre-ictal |
| url | http://www.sciencedirect.com/science/article/pii/S2352914820300137 |
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