An efficient framework for the analysis of big brain signals data

Supriya; Siuly; Hua Wang; Yanchun Zhang

doi:10.1007/978-3-319-92013-9_16

An efficient framework for the analysis of big brain signals data

Supriya, Siuly, Hua Wang, Yanchun Zhang

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

9 Citations (Scopus)

Abstract

Big Brain Signals Data (BBSD) analysis is one of the most difficult challenges in the biomedical signal processing field for modern treatment and health monitoring applications. BBSD analytics has been recently applied towards aiding the process of care delivery and disease exploration. The main purpose of this paper is to introduce a framework for the analysis of BBSD of time series EEG in biomedical signal processing for identification of abnormalities. This paper presents a data analysis framework combining complex network and machine learning techniques for the analysis of BBSD in time series form. The proposed method is tested on an electroencephalogram (EEG) time series database as the implanted electrodes in the brain generate huge amounts of time series data in EEG. The pilot study in this paper has examined that the proposed methodology has the capability to analysis massive size of brain signals data and also can be used for handling any other biomedical signal data in time series form (e.g. electrocardiogram (ECG); Electromyogram (EMG)). The main benefit of the proposed methodology is to provide an effective way for analyzing the vast amount of BBSD generated from the brain to care patients with better outcomes and also help technicians for making intelligent decisions system.

Original language	English
Title of host publication	Databases Theory and Applications 29th Australasian Database Conference, ADC 2018, Proceedings
Editors	Junhu Wang, Gao Cong, Jinjun Chen, Jianzhong Qi
Publisher	Springer Verlag
Pages	199-207
Number of pages	9
ISBN (Print)	9783319920122
DOIs	https://doi.org/10.1007/978-3-319-92013-9_16
Publication status	Published - 2018
Externally published	Yes
Event	29th Australasian Database Conference, ADC 2018 - Gold Coast, Australia Duration: 24 May 2018 → 27 May 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10837 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	29th Australasian Database Conference, ADC 2018
Country/Territory	Australia
City	Gold Coast
Period	24/05/18 → 27/05/18

Keywords

Big data
Biomedical signal
Classification
Complex network
EEG
Feature extraction
Machine learning

Access to Document

10.1007/978-3-319-92013-9_16

Cite this

Supriya, Siuly, Wang, H., & Zhang, Y. (2018). An efficient framework for the analysis of big brain signals data. In J. Wang, G. Cong, J. Chen, & J. Qi (Eds.), Databases Theory and Applications 29th Australasian Database Conference, ADC 2018, Proceedings (pp. 199-207). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10837 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-92013-9_16

Supriya ; Siuly ; Wang, Hua et al. / An efficient framework for the analysis of big brain signals data. Databases Theory and Applications 29th Australasian Database Conference, ADC 2018, Proceedings. editor / Junhu Wang ; Gao Cong ; Jinjun Chen ; Jianzhong Qi. Springer Verlag, 2018. pp. 199-207 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "An efficient framework for the analysis of big brain signals data",

abstract = "Big Brain Signals Data (BBSD) analysis is one of the most difficult challenges in the biomedical signal processing field for modern treatment and health monitoring applications. BBSD analytics has been recently applied towards aiding the process of care delivery and disease exploration. The main purpose of this paper is to introduce a framework for the analysis of BBSD of time series EEG in biomedical signal processing for identification of abnormalities. This paper presents a data analysis framework combining complex network and machine learning techniques for the analysis of BBSD in time series form. The proposed method is tested on an electroencephalogram (EEG) time series database as the implanted electrodes in the brain generate huge amounts of time series data in EEG. The pilot study in this paper has examined that the proposed methodology has the capability to analysis massive size of brain signals data and also can be used for handling any other biomedical signal data in time series form (e.g. electrocardiogram (ECG); Electromyogram (EMG)). The main benefit of the proposed methodology is to provide an effective way for analyzing the vast amount of BBSD generated from the brain to care patients with better outcomes and also help technicians for making intelligent decisions system.",

keywords = "Big data, Biomedical signal, Classification, Complex network, EEG, Feature extraction, Machine learning",

author = "Supriya and Siuly and Hua Wang and Yanchun Zhang",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2018.; 29th Australasian Database Conference, ADC 2018 ; Conference date: 24-05-2018 Through 27-05-2018",

year = "2018",

doi = "10.1007/978-3-319-92013-9_16",

language = "English",

isbn = "9783319920122",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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}

Supriya, Siuly, Wang, H & Zhang, Y 2018, An efficient framework for the analysis of big brain signals data. in J Wang, G Cong, J Chen & J Qi (eds), Databases Theory and Applications 29th Australasian Database Conference, ADC 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10837 LNCS, Springer Verlag, pp. 199-207, 29th Australasian Database Conference, ADC 2018, Gold Coast, Australia, 24/05/18. https://doi.org/10.1007/978-3-319-92013-9_16

An efficient framework for the analysis of big brain signals data. / Supriya; Siuly; Wang, Hua et al.

Databases Theory and Applications 29th Australasian Database Conference, ADC 2018, Proceedings. ed. / Junhu Wang; Gao Cong; Jinjun Chen; Jianzhong Qi. Springer Verlag, 2018. p. 199-207 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10837 LNCS).

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

TY - GEN

T1 - An efficient framework for the analysis of big brain signals data

AU - Supriya,

AU - Siuly,

AU - Wang, Hua

AU - Zhang, Yanchun

N1 - Publisher Copyright: © Springer International Publishing AG, part of Springer Nature 2018.

PY - 2018

Y1 - 2018

N2 - Big Brain Signals Data (BBSD) analysis is one of the most difficult challenges in the biomedical signal processing field for modern treatment and health monitoring applications. BBSD analytics has been recently applied towards aiding the process of care delivery and disease exploration. The main purpose of this paper is to introduce a framework for the analysis of BBSD of time series EEG in biomedical signal processing for identification of abnormalities. This paper presents a data analysis framework combining complex network and machine learning techniques for the analysis of BBSD in time series form. The proposed method is tested on an electroencephalogram (EEG) time series database as the implanted electrodes in the brain generate huge amounts of time series data in EEG. The pilot study in this paper has examined that the proposed methodology has the capability to analysis massive size of brain signals data and also can be used for handling any other biomedical signal data in time series form (e.g. electrocardiogram (ECG); Electromyogram (EMG)). The main benefit of the proposed methodology is to provide an effective way for analyzing the vast amount of BBSD generated from the brain to care patients with better outcomes and also help technicians for making intelligent decisions system.

AB - Big Brain Signals Data (BBSD) analysis is one of the most difficult challenges in the biomedical signal processing field for modern treatment and health monitoring applications. BBSD analytics has been recently applied towards aiding the process of care delivery and disease exploration. The main purpose of this paper is to introduce a framework for the analysis of BBSD of time series EEG in biomedical signal processing for identification of abnormalities. This paper presents a data analysis framework combining complex network and machine learning techniques for the analysis of BBSD in time series form. The proposed method is tested on an electroencephalogram (EEG) time series database as the implanted electrodes in the brain generate huge amounts of time series data in EEG. The pilot study in this paper has examined that the proposed methodology has the capability to analysis massive size of brain signals data and also can be used for handling any other biomedical signal data in time series form (e.g. electrocardiogram (ECG); Electromyogram (EMG)). The main benefit of the proposed methodology is to provide an effective way for analyzing the vast amount of BBSD generated from the brain to care patients with better outcomes and also help technicians for making intelligent decisions system.

KW - Big data

KW - Biomedical signal

KW - Classification

KW - Complex network

KW - EEG

KW - Feature extraction

KW - Machine learning

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SN - 9783319920122

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

SP - 199

EP - 207

BT - Databases Theory and Applications 29th Australasian Database Conference, ADC 2018, Proceedings

A2 - Wang, Junhu

A2 - Cong, Gao

A2 - Chen, Jinjun

A2 - Qi, Jianzhong

PB - Springer Verlag

T2 - 29th Australasian Database Conference, ADC 2018

Y2 - 24 May 2018 through 27 May 2018

ER -

Supriya, Siuly, Wang H, Zhang Y. An efficient framework for the analysis of big brain signals data. In Wang J, Cong G, Chen J, Qi J, editors, Databases Theory and Applications 29th Australasian Database Conference, ADC 2018, Proceedings. Springer Verlag. 2018. p. 199-207. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-92013-9_16

An efficient framework for the analysis of big brain signals data

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Keywords

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