Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Parallel real time seizure detection in large EEG data
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).ORCID iD: 0000-0001-6877-3702
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).ORCID iD: 0000-0002-9901-9857
2016 (English)In: IoTBD 2016 - Proceedings of the International Conference on Internet of Things and Big Data, SciTePress, 2016, p. 214-222Conference paper, Published paper (Refereed)
Abstract [en]

Electroencephalography (EEG) is one of the main techniques for detecting and diagnosing epileptic seizures. Due to the large size of EEG data in long term clinical monitoring and the complex nature of epileptic seizures, seizure detection is both data-intensive and compute-intensive. Analysing EEG data for detecting seizures in real time has many applications, e.g., in automatic seizure detection or in allowing a timely alarm signal to be presented to the patient. In real time seizure detection, seizures have to be detected with negligible delay, thus requiring lightweight algorithms. MapReduce and its variations have been effectively used for data analysis in large dataset problems on general-purpose machines. In this study, we propose a parallel lightweight algorithm for epileptic seizure detection using Spark Streaming. Our algorithm not only classifies seizures in real time, it also learns an epileptic threshold in real time. We furthermore present "top-k amplitude measure" as a feature for classifying seizures in the EEG, that additionally assists in reducing data size. In a benchmark experiment we show that our algorithm can detect seizures in real time with low latency, while maintaining a good seizure detection rate. In short, our algorithm provides new possibilities in using private cloud infrastructures for real time epileptic seizure detection in EEG data.

Place, publisher, year, edition, pages
SciTePress, 2016. p. 214-222
Keywords [en]
BigData, Epilepsy, MapReduce, Real Time, Seizure Detection, Spark, Algorithms, Electric sparks, Electroencephalography, Electrophysiology, Internet, Internet of things, Neurodegenerative diseases, Neurophysiology, Automatic seizure detections, Benchmark experiments, Clinical monitoring, Epileptic seizure detection, Map-reduce, Big data
National Category
Computer and Information Sciences Neurosciences
Identifiers
URN: urn:nbn:se:kth:diva-195466DOI: 10.5220/0005875502140222ISI: 000391100400023Scopus ID: 2-s2.0-84979619793ISBN: 9789897581830 (print)OAI: oai:DiVA.org:kth-195466DiVA, id: diva2:1045405
Conference
International Conference on Internet of Things and Big Data, IoTBD 2016, Rome, Italy, 23 April 2016 through 25 April 2016
Note

QC 20161109

Available from: 2016-11-09 Created: 2016-11-03 Last updated: 2018-01-13Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Ahmed, LaeeqEdlund, ÅkeLaure, Erwin
By organisation
Computational Science and Technology (CST)
Computer and Information SciencesNeurosciences

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 546 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf