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Parallel real time seizure detection in large EEG data
KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsvetenskap och beräkningsteknik (CST).ORCID-id: 0000-0001-6877-3702
KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsvetenskap och beräkningsteknik (CST).
KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsvetenskap och beräkningsteknik (CST).ORCID-id: 0000-0002-9901-9857
2016 (engelsk)Inngår i: IoTBD 2016 - Proceedings of the International Conference on Internet of Things and Big Data, SciTePress, 2016, s. 214-222Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
SciTePress, 2016. s. 214-222
Emneord [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
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Identifikatorer
URN: urn:nbn:se:kth:diva-195466DOI: 10.5220/0005875502140222ISI: 000391100400023Scopus ID: 2-s2.0-84979619793ISBN: 9789897581830 (tryckt)OAI: oai:DiVA.org:kth-195466DiVA, id: diva2:1045405
Konferanse
International Conference on Internet of Things and Big Data, IoTBD 2016, Rome, Italy, 23 April 2016 through 25 April 2016
Merknad

QC 20161109

Tilgjengelig fra: 2016-11-09 Laget: 2016-11-03 Sist oppdatert: 2018-01-13bibliografisk kontrollert

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