A distributed descriptor characterizing structural irregularity of EEG time series for epileptic seizure detection

Zhenning Mei; Xian Zhao; Hongyu Chen; Wei Chen

doi:10.1109/EMBC.2018.8512919

A distributed descriptor characterizing structural irregularity of EEG time series for epileptic seizure detection

Zhenning Mei, Xian Zhao, Hongyu Chen, Wei Chen

Future Everyday

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

2 Citations (Scopus)

Abstract

This paper presents a novel descriptor aiming at anomaly detection in sequential data, like epileptic seizure detection with EEG time series. The descriptor is derived from the eigenvalue decomposition (EVD) of a Hankel-form data matrix generated from the raw time series. Simulation trials imply that the descriptor is capable of characterizing the structural aspect of a time series. In addition, we deploy the proposed descriptor as a feature extractor and apply it on Bonn Seizure Database which is widely used in seizure detection. The high accuracies on classification problems are comparable with the state-of-the-art so validate the effectiveness of our method.

Original language	English
Title of host publication	40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
Place of Publication	Piscataway
Publisher	Institute of Electrical and Electronics Engineers
Pages	3386-3389
Number of pages	4
ISBN (Electronic)	978-1-5386-3646-6
DOIs	https://doi.org/10.1109/EMBC.2018.8512919
Publication status	Published - 26 Oct 2018
Event	40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 - Hawaii Convention Center, Honolulu, United States Duration: 18 Jul 2018 → 21 Jul 2018 Conference number: 40 https://embc.embs.org/2018/

Conference

Conference	40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
Abbreviated title	EMBC 2018
Country/Territory	United States
City	Honolulu
Period	18/07/18 → 21/07/18
Other	"Learning from the Past, Looking to the Future"
Internet address	https://embc.embs.org/2018/

Keywords

Databases, Factual
Electroencephalography
Epilepsy
Humans
Seizures
Signal Processing, Computer-Assisted

Access to Document

10.1109/EMBC.2018.8512919

Cite this

Mei, Z., Zhao, X., Chen, H., & Chen, W. (2018). A distributed descriptor characterizing structural irregularity of EEG time series for epileptic seizure detection. In 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 (pp. 3386-3389). Article 8512919 Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/EMBC.2018.8512919

@inproceedings{2cb637d6167a46c9be5fd52facebbcbd,

title = "A distributed descriptor characterizing structural irregularity of EEG time series for epileptic seizure detection",

abstract = "This paper presents a novel descriptor aiming at anomaly detection in sequential data, like epileptic seizure detection with EEG time series. The descriptor is derived from the eigenvalue decomposition (EVD) of a Hankel-form data matrix generated from the raw time series. Simulation trials imply that the descriptor is capable of characterizing the structural aspect of a time series. In addition, we deploy the proposed descriptor as a feature extractor and apply it on Bonn Seizure Database which is widely used in seizure detection. The high accuracies on classification problems are comparable with the state-of-the-art so validate the effectiveness of our method.",

keywords = "Databases, Factual, Electroencephalography, Epilepsy, Humans, Seizures, Signal Processing, Computer-Assisted",

author = "Zhenning Mei and Xian Zhao and Hongyu Chen and Wei Chen",

year = "2018",

month = oct,

day = "26",

doi = "10.1109/EMBC.2018.8512919",

language = "English",

pages = "3386--3389",

booktitle = "40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018, EMBC 2018 ; Conference date: 18-07-2018 Through 21-07-2018",

url = "https://embc.embs.org/2018/",

}

Mei, Z, Zhao, X, Chen, H & Chen, W 2018, A distributed descriptor characterizing structural irregularity of EEG time series for epileptic seizure detection. in 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018., 8512919, Institute of Electrical and Electronics Engineers, Piscataway, pp. 3386-3389, 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018, Honolulu, United States, 18/07/18. https://doi.org/10.1109/EMBC.2018.8512919

A distributed descriptor characterizing structural irregularity of EEG time series for epileptic seizure detection. / Mei, Zhenning; Zhao, Xian; Chen, Hongyu et al.
40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018. Piscataway: Institute of Electrical and Electronics Engineers, 2018. p. 3386-3389 8512919.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - A distributed descriptor characterizing structural irregularity of EEG time series for epileptic seizure detection

AU - Mei, Zhenning

AU - Zhao, Xian

AU - Chen, Hongyu

AU - Chen, Wei

N1 - Conference code: 40

PY - 2018/10/26

Y1 - 2018/10/26

N2 - This paper presents a novel descriptor aiming at anomaly detection in sequential data, like epileptic seizure detection with EEG time series. The descriptor is derived from the eigenvalue decomposition (EVD) of a Hankel-form data matrix generated from the raw time series. Simulation trials imply that the descriptor is capable of characterizing the structural aspect of a time series. In addition, we deploy the proposed descriptor as a feature extractor and apply it on Bonn Seizure Database which is widely used in seizure detection. The high accuracies on classification problems are comparable with the state-of-the-art so validate the effectiveness of our method.

AB - This paper presents a novel descriptor aiming at anomaly detection in sequential data, like epileptic seizure detection with EEG time series. The descriptor is derived from the eigenvalue decomposition (EVD) of a Hankel-form data matrix generated from the raw time series. Simulation trials imply that the descriptor is capable of characterizing the structural aspect of a time series. In addition, we deploy the proposed descriptor as a feature extractor and apply it on Bonn Seizure Database which is widely used in seizure detection. The high accuracies on classification problems are comparable with the state-of-the-art so validate the effectiveness of our method.

KW - Databases, Factual

KW - Electroencephalography

KW - Epilepsy

KW - Humans

KW - Seizures

KW - Signal Processing, Computer-Assisted

UR - http://www.scopus.com/inward/record.url?scp=85055366598&partnerID=8YFLogxK

U2 - 10.1109/EMBC.2018.8512919

DO - 10.1109/EMBC.2018.8512919

M3 - Conference contribution

C2 - 30441114

AN - SCOPUS:85055366598

SP - 3386

EP - 3389

BT - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018

PB - Institute of Electrical and Electronics Engineers

CY - Piscataway

T2 - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018

Y2 - 18 July 2018 through 21 July 2018

ER -

Mei Z, Zhao X, Chen H, Chen W. A distributed descriptor characterizing structural irregularity of EEG time series for epileptic seizure detection. In 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018. Piscataway: Institute of Electrical and Electronics Engineers. 2018. p. 3386-3389. 8512919 doi: 10.1109/EMBC.2018.8512919

A distributed descriptor characterizing structural irregularity of EEG time series for epileptic seizure detection

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this