A Study of Combined Lossy Compression and Seizure Detection on Epileptic EEG Signals

The Binh NGUYEN; Wanli MA; Dat TRAN

doi:10.1016/j.procs.2018.07.219

A Study of Combined Lossy Compression and Seizure Detection on Epileptic EEG Signals

The Binh NGUYEN, Wanli MA, Dat TRAN

Information Systems

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

8 Citations (Scopus)

133 Downloads (Pure)

Abstract

Electroencephalogram (EEG) has been widely used in diagnosing and detecting epileptic seizure. Large epileptic EEG databases have been built, the use of EEG compression is therefore becoming necessary. Epilepsy causes a change on EEG characteristics, especially on frequency, hence exploiting these features may improve the performance of EEG lossy compression techniques that are mostly working on frequency domain. In this paper, we propose a lossy compression method for epileptic EEG data, by exploiting the characteristics of EEG under epilepsy. Moreover, the recovered EEG signals processed by the proposed method are used by an EEG-based seizure detection system to evaluate the possibility of applying in real world as well as the impact of lossy compression on seizure detection. The results show that the proposed method gives a higher result, and applying the proposed method to EEG-based seizure detection system is feasible and has the advantage compared to using lossless ones.

Original language	English
Title of host publication	Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 22nd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES-2018)
Editors	Robert J. Howlett, Carlos Toro, Yulia Hicks, Lakhmi C. Jain
Place of Publication	Belgrade, Serbia
Publisher	Elsevier
Pages	156-165
Number of pages	10
Volume	126
DOIs	https://doi.org/10.1016/j.procs.2018.07.219
Publication status	Published - 3 Sept 2018
Event	22nd International Conference on Knowledge - Based and Intelligent Information and Engineering Systems - Metropol Palace Hotel , Belgrade, Serbia Duration: 3 Sept 2018 → 5 Sept 2018 http://kes2018.kesinternational.org/

Publication series

Name	Procedia Computer Science
Publisher	Elsevier BV
ISSN (Print)	1877-0509

Conference

Conference	22nd International Conference on Knowledge - Based and Intelligent Information and Engineering Systems
Abbreviated title	KES 2018
Country/Territory	Serbia
City	Belgrade
Period	3/09/18 → 5/09/18
Internet address	http://kes2018.kesinternational.org/

Access to Document

10.1016/j.procs.2018.07.219Licence: CC BY-NC-ND

PaperFinal published version, 517 KBLicence: CC BY-NC-ND

Cite this

NGUYEN, T. B., MA, W., & TRAN, D. (2018). A Study of Combined Lossy Compression and Seizure Detection on Epileptic EEG Signals. In R. J. Howlett, C. Toro, Y. Hicks, & L. C. Jain (Eds.), Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 22nd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES-2018) (Vol. 126, pp. 156-165). (Procedia Computer Science). Elsevier. https://doi.org/10.1016/j.procs.2018.07.219

NGUYEN, The Binh ; MA, Wanli ; TRAN, Dat. / A Study of Combined Lossy Compression and Seizure Detection on Epileptic EEG Signals. Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 22nd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES-2018). editor / Robert J. Howlett ; Carlos Toro ; Yulia Hicks ; Lakhmi C. Jain. Vol. 126 Belgrade, Serbia : Elsevier, 2018. pp. 156-165 (Procedia Computer Science).

@inproceedings{52d1676e27984d67ad1f711c49b89f14,

title = "A Study of Combined Lossy Compression and Seizure Detection on Epileptic EEG Signals",

abstract = "Electroencephalogram (EEG) has been widely used in diagnosing and detecting epileptic seizure. Large epileptic EEG databases have been built, the use of EEG compression is therefore becoming necessary. Epilepsy causes a change on EEG characteristics, especially on frequency, hence exploiting these features may improve the performance of EEG lossy compression techniques that are mostly working on frequency domain. In this paper, we propose a lossy compression method for epileptic EEG data, by exploiting the characteristics of EEG under epilepsy. Moreover, the recovered EEG signals processed by the proposed method are used by an EEG-based seizure detection system to evaluate the possibility of applying in real world as well as the impact of lossy compression on seizure detection. The results show that the proposed method gives a higher result, and applying the proposed method to EEG-based seizure detection system is feasible and has the advantage compared to using lossless ones.",

keywords = "EEG, Epileptic EEG signal, lossy compression, DWT-AAC, EEG-based seizure detection",

author = "NGUYEN, {The Binh} and Wanli MA and Dat TRAN",

year = "2018",

month = sep,

day = "3",

doi = "10.1016/j.procs.2018.07.219",

language = "English",

volume = "126",

series = "Procedia Computer Science",

publisher = "Elsevier",

pages = "156--165",

editor = "Howlett, {Robert J.} and Carlos Toro and Yulia Hicks and Jain, {Lakhmi C. }",

booktitle = "Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 22nd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES-2018)",

address = "Netherlands",

note = "22nd International Conference on Knowledge - Based and Intelligent Information and Engineering Systems, KES 2018 ; Conference date: 03-09-2018 Through 05-09-2018",

url = "http://kes2018.kesinternational.org/",

}

NGUYEN, TB, MA, W & TRAN, D 2018, A Study of Combined Lossy Compression and Seizure Detection on Epileptic EEG Signals. in RJ Howlett, C Toro, Y Hicks & LC Jain (eds), Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 22nd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES-2018). vol. 126, Procedia Computer Science, Elsevier, Belgrade, Serbia, pp. 156-165, 22nd International Conference on Knowledge - Based and Intelligent Information and Engineering Systems, Belgrade, Serbia, 3/09/18. https://doi.org/10.1016/j.procs.2018.07.219

A Study of Combined Lossy Compression and Seizure Detection on Epileptic EEG Signals. / NGUYEN, The Binh; MA, Wanli ; TRAN, Dat.
Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 22nd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES-2018). ed. / Robert J. Howlett; Carlos Toro; Yulia Hicks; Lakhmi C. Jain. Vol. 126 Belgrade, Serbia: Elsevier, 2018. p. 156-165 (Procedia Computer Science).

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

TY - GEN

T1 - A Study of Combined Lossy Compression and Seizure Detection on Epileptic EEG Signals

AU - NGUYEN, The Binh

AU - MA, Wanli

AU - TRAN, Dat

PY - 2018/9/3

Y1 - 2018/9/3

N2 - Electroencephalogram (EEG) has been widely used in diagnosing and detecting epileptic seizure. Large epileptic EEG databases have been built, the use of EEG compression is therefore becoming necessary. Epilepsy causes a change on EEG characteristics, especially on frequency, hence exploiting these features may improve the performance of EEG lossy compression techniques that are mostly working on frequency domain. In this paper, we propose a lossy compression method for epileptic EEG data, by exploiting the characteristics of EEG under epilepsy. Moreover, the recovered EEG signals processed by the proposed method are used by an EEG-based seizure detection system to evaluate the possibility of applying in real world as well as the impact of lossy compression on seizure detection. The results show that the proposed method gives a higher result, and applying the proposed method to EEG-based seizure detection system is feasible and has the advantage compared to using lossless ones.

AB - Electroencephalogram (EEG) has been widely used in diagnosing and detecting epileptic seizure. Large epileptic EEG databases have been built, the use of EEG compression is therefore becoming necessary. Epilepsy causes a change on EEG characteristics, especially on frequency, hence exploiting these features may improve the performance of EEG lossy compression techniques that are mostly working on frequency domain. In this paper, we propose a lossy compression method for epileptic EEG data, by exploiting the characteristics of EEG under epilepsy. Moreover, the recovered EEG signals processed by the proposed method are used by an EEG-based seizure detection system to evaluate the possibility of applying in real world as well as the impact of lossy compression on seizure detection. The results show that the proposed method gives a higher result, and applying the proposed method to EEG-based seizure detection system is feasible and has the advantage compared to using lossless ones.

KW - EEG

KW - Epileptic EEG signal

KW - lossy compression

KW - DWT-AAC

KW - EEG-based seizure detection

UR - http://www.scopus.com/inward/record.url?scp=85056614019&partnerID=8YFLogxK

U2 - 10.1016/j.procs.2018.07.219

DO - 10.1016/j.procs.2018.07.219

M3 - Conference contribution

VL - 126

T3 - Procedia Computer Science

SP - 156

EP - 165

BT - Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 22nd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES-2018)

A2 - Howlett, Robert J.

A2 - Toro, Carlos

A2 - Hicks, Yulia

A2 - Jain, Lakhmi C.

PB - Elsevier

CY - Belgrade, Serbia

T2 - 22nd International Conference on Knowledge - Based and Intelligent Information and Engineering Systems

Y2 - 3 September 2018 through 5 September 2018

ER -

NGUYEN TB, MA W , TRAN D. A Study of Combined Lossy Compression and Seizure Detection on Epileptic EEG Signals. In Howlett RJ, Toro C, Hicks Y, Jain LC, editors, Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 22nd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES-2018). Vol. 126. Belgrade, Serbia: Elsevier. 2018. p. 156-165. (Procedia Computer Science). doi: 10.1016/j.procs.2018.07.219

A Study of Combined Lossy Compression and Seizure Detection on Epileptic EEG Signals

Abstract

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Cite this