An efficient framework for the analysis of big brain signals data

Supriya, Siuly, Hua Wang, Yanchun Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-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 languageEnglish
Title of host publicationDatabases Theory and Applications 29th Australasian Database Conference, ADC 2018, Proceedings
EditorsJunhu Wang, Gao Cong, Jinjun Chen, Jianzhong Qi
PublisherSpringer Verlag
Pages199-207
Number of pages9
ISBN (Print)9783319920122
DOIs
Publication statusPublished - 2018
Externally publishedYes
Event29th Australasian Database Conference, ADC 2018 - Gold Coast, Australia
Duration: 24 May 201827 May 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10837 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference29th Australasian Database Conference, ADC 2018
Country/TerritoryAustralia
CityGold Coast
Period24/05/1827/05/18

Keywords

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

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