Frequency Bands Selection for Seizure Classification and Forecasting Using NLP, Random Forest and SVM Models

Ziwei Wang, Paolo Mengoni*

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

2 Citations (Scopus)


Individualized treatment is crucial for epileptic patients with different types of seizures. The difference among patients impacts the drug choice as well as the surgery procedure. With the advance in machine learning, automatic seizure detection could ease the manual time-consuming and labour-intensive procedure for diagnose seizure in the clinical setting. In this paper, we propose a electroencephalography frequency bands selection method that exploits Natural Language Processing (NLP) features from individual’s condition and patients with same seizure types. We used Temple University Hospital (TUH) EEG seizure corpus and conducted experiments with various input data for different seizure types classified using Random Forest (RF) and Support Vector Machine (SVM). The results show that with reduced frequency bands the performance slightly deviates from the whole frequency bands, thus leading to possible resource-efficient implementation for seizure detection.

Original languageEnglish
Title of host publicationBrain Informatics
Subtitle of host publication14th International Conference, BI 2021, Virtual Event, September 17–19, 2021, Proceedings
EditorsMufti Mahmud, M Shamim Kaiser, Stefano Vassanelli, Qionghai Dai, Ning Zhong
PublisherSpringer Cham
Number of pages11
ISBN (Electronic)9783030869939
ISBN (Print)9783030869922
Publication statusPublished - 15 Sept 2021
Event14th International Conference on Brain Informatics, BI 2021 - Virtual, Online
Duration: 17 Sept 202119 Sept 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference14th International Conference on Brain Informatics, BI 2021
CityVirtual, Online

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

  • Classification
  • Electroencephalography
  • Frequency bands selection
  • Natural Language Processing
  • Seizure


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