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

Ziwei Wang; Paolo Mengoni

doi:10.1007/978-3-030-86993-9_29

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 proceeding › Conference proceeding › peer-review

2 Citations (Scopus)

Abstract

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 language	English
Title of host publication	Brain Informatics
Subtitle of host publication	14th International Conference, BI 2021, Virtual Event, September 17–19, 2021, Proceedings
Editors	Mufti Mahmud, M Shamim Kaiser, Stefano Vassanelli, Qionghai Dai, Ning Zhong
Publisher	Springer Cham
Pages	310-320
Number of pages	11
Edition	1st
ISBN (Electronic)	9783030869939
ISBN (Print)	9783030869922
DOIs	https://doi.org/10.1007/978-3-030-86993-9_29
Publication status	Published - 15 Sept 2021
Event	14th International Conference on Brain Informatics, BI 2021 - Virtual, Online Duration: 17 Sept 2021 → 19 Sept 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12960
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	14th International Conference on Brain Informatics, BI 2021
City	Virtual, Online
Period	17/09/21 → 19/09/21

Scopus Subject Areas

Theoretical Computer Science
Computer Science(all)

User-Defined Keywords

Classification
Electroencephalography
Frequency bands selection
Natural Language Processing
Seizure

Access to Document

10.1007/978-3-030-86993-9_29

Cite this

Wang, Z., & Mengoni, P. (2021). Frequency Bands Selection for Seizure Classification and Forecasting Using NLP, Random Forest and SVM Models. In M. Mahmud, M. S. Kaiser, S. Vassanelli, Q. Dai, & N. Zhong (Eds.), Brain Informatics: 14th International Conference, BI 2021, Virtual Event, September 17–19, 2021, Proceedings (1st ed., pp. 310-320). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12960). Springer Cham. https://doi.org/10.1007/978-3-030-86993-9_29

Wang, Ziwei ; Mengoni, Paolo. / Frequency Bands Selection for Seizure Classification and Forecasting Using NLP, Random Forest and SVM Models. Brain Informatics: 14th International Conference, BI 2021, Virtual Event, September 17–19, 2021, Proceedings. editor / Mufti Mahmud ; M Shamim Kaiser ; Stefano Vassanelli ; Qionghai Dai ; Ning Zhong. 1st. ed. Springer Cham, 2021. pp. 310-320 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{b2198cf1cc984e6bb6d89a6edbddc48d,

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abstract = "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{\textquoteright}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.",

keywords = "Classification, Electroencephalography, Frequency bands selection, Natural Language Processing, Seizure",

author = "Ziwei Wang and Paolo Mengoni",

note = "Funding Information: This work is partly supported by the “Teaching Development Grant” - Hong Kong Baptist University, Hong Kong, China Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 14th International Conference on Brain Informatics, BI 2021 ; Conference date: 17-09-2021 Through 19-09-2021",

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doi = "10.1007/978-3-030-86993-9_29",

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Wang, Z & Mengoni, P 2021, Frequency Bands Selection for Seizure Classification and Forecasting Using NLP, Random Forest and SVM Models. in M Mahmud, MS Kaiser, S Vassanelli, Q Dai & N Zhong (eds), Brain Informatics: 14th International Conference, BI 2021, Virtual Event, September 17–19, 2021, Proceedings. 1st edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12960, Springer Cham, pp. 310-320, 14th International Conference on Brain Informatics, BI 2021, Virtual, Online, 17/09/21. https://doi.org/10.1007/978-3-030-86993-9_29

Frequency Bands Selection for Seizure Classification and Forecasting Using NLP, Random Forest and SVM Models. / Wang, Ziwei; Mengoni, Paolo.
Brain Informatics: 14th International Conference, BI 2021, Virtual Event, September 17–19, 2021, Proceedings. ed. / Mufti Mahmud; M Shamim Kaiser; Stefano Vassanelli; Qionghai Dai; Ning Zhong. 1st. ed. Springer Cham, 2021. p. 310-320 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12960).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - Frequency Bands Selection for Seizure Classification and Forecasting Using NLP, Random Forest and SVM Models

AU - Wang, Ziwei

AU - Mengoni, Paolo

N1 - Funding Information: This work is partly supported by the “Teaching Development Grant” - Hong Kong Baptist University, Hong Kong, China Publisher Copyright: © 2021, Springer Nature Switzerland AG.

PY - 2021/9/15

Y1 - 2021/9/15

N2 - 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.

AB - 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.

KW - Classification

KW - Electroencephalography

KW - Frequency bands selection

KW - Natural Language Processing

KW - Seizure

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DO - 10.1007/978-3-030-86993-9_29

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AN - SCOPUS:85115877323

SN - 9783030869922

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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EP - 320

BT - Brain Informatics

A2 - Mahmud, Mufti

A2 - Kaiser, M Shamim

A2 - Vassanelli, Stefano

A2 - Dai, Qionghai

A2 - Zhong, Ning

PB - Springer Cham

T2 - 14th International Conference on Brain Informatics, BI 2021

Y2 - 17 September 2021 through 19 September 2021

ER -

Wang Z, Mengoni P. Frequency Bands Selection for Seizure Classification and Forecasting Using NLP, Random Forest and SVM Models. In Mahmud M, Kaiser MS, Vassanelli S, Dai Q, Zhong N, editors, Brain Informatics: 14th International Conference, BI 2021, Virtual Event, September 17–19, 2021, Proceedings. 1st ed. Springer Cham. 2021. p. 310-320. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-86993-9_29

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

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