K-Means clustering based time series weighting with epileptic seizure detection
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Tarih
2018
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In detecting epilepsy and classifying epileptic attacks, the electrical activity of the brain is used as an important data source. In this study, the data set for automatic classification of epilepsy from EEG signals was taken from the UCI machine learning data repository. This dataset consists of raw time domain EEG signals. Apart from epilepsy, there are four different classes. These classes consist of a total of five classes, EEG recorded when the eyes are open, EEG recorded when the eyes are closed, EEG recorded from people with the tumor zone, and EEG recorded from healthy people. To differentiate the epileptic condition from the other classes, only the raw EEG signals were categorized without any feature extraction from the time domain EEG signals. In this study, k-averages cluster-based time series weighting (KOKTZSA) method was applied to raw EEG signals as pre, processing to classify the five-class epilepsy data set with high accuracy and then used to classify the weighted data set in Random Forest and C4.5 decision tree classification algorithms have been used. The raw EEG signal obtained a classification accuracy of 70.55% for the C4.5 decision tree classification algorithm and 96.86% for the data cluster C4.5 decision tree weighted by the KOKTZSA. Raw EEG signal, random forest classification algorithm obtained 81% classification accuracy while data set weighted with KOKTZSA achieved 99.33% classification accuracy with random forest classification algorithm. The obtained results show that the proposed hybrid model achieves a high classification accuracy without extracting any features from the EEG signal. This has greatly reduced the high computational cost. © 2018 IEEE.
Açıklama
4th Electric Electronics, Computer Science, Biomedical Engineerings' Meeting, EBBT 2018 -- 18 April 2018 through 19 April 2018 -- -- 137380
Anahtar Kelimeler
Classification, Electroencephalography, Epileptic Seizure Detection, K Averages Clustering-Basedfeature Weighting, Epileptik Nöbet Tespiti, Elektroensefalografi, K Ortalamalar Kümeleme Tabanlı Öznitelik Ağırlıklandırma, Sınıflandırma
Kaynak
2018 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting, EBBT 2018
WoS Q Değeri
Scopus Q Değeri
N/A
