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Focal and Generalized Seizures Distinction by Rebalancing Class Data and Random Forest Classification

Part of the Lecture Notes in Computer Science book series (LNIP,volume 12940)


Epileptic seizures are caused by abnormal electrical activity of brain cells, frequently accompanied by a short-lived loss of control or awareness. Epileptic seizures differ depending on their origin in the brain. They can be categorized as either focal or generalized in onset. The identification of seizure category is essential in brain surgery and in selecting medications that could help bring seizures under control. It is not always feasible to find out exactly if the seizure was generalized or focal without a thorough analysis of the continuous prolonged electroencephalographic (EEG) waveforms. In this study, we propose an automatic classification method based on Hjorth parameters measured in EEG. 1497 EEG signals from the Temple University Hospital Seizure Corpus (v.1.5.1) are used. Hjorth parameters (activity, complexity, and mobility) are extracted from these EEG records. To address class imbalance, data was rebalanced by Synthetic Minority Over Sampling (SMOTE). We also investigated the impact of changing the window length on the random forest classifier. For comparison, cost-sensitive learning has been applied by providing more weight to the minority class (generalized seizure) directly in the classifier. The performance of the proposed method was compared using accuracy, recall, and precision measures. Our method achieved a highest accuracy rate of 92.3% with a recall of 92.7% and precision of 91.8% using Hjorth parameters extracted from 10 s windows and rebalanced using SMOTE. A slight variation in performance measures occurred according to window size.


  • Generalized and focal seizures
  • EEG
  • Hjorth parameters
  • Weighted random forest classification

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Special thanks to Youssef Ouakrim for his technical contribution in coding stage. This research was supported by funding from Fonds de recherche du Quebec- Nature et technologies (L.A.A) and the Canada Research Chair on Biomedical Data Mining (950–231214).

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Correspondence to Lina Abou-Abbas .

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Abou-Abbas, L., Jemal, I., Henni, K., Mitiche, A., Mezghani, N. (2021). Focal and Generalized Seizures Distinction by Rebalancing Class Data and Random Forest Classification. In: Rojas, I., Castillo-Secilla, D., Herrera, L.J., Pomares, H. (eds) Bioengineering and Biomedical Signal and Image Processing. BIOMESIP 2021. Lecture Notes in Computer Science(), vol 12940. Springer, Cham.

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