Detection of high frequency oscillations in epilepsy with k-means clustering method

Su Liu; Nuri F. Ince; Akin Sabanci; Aydin Aydoseli; Yavuz Aras; Altay Sencer; Nerses Bebek; Zhiyi Sha; Candan Gurses

doi:10.1109/NER.2015.7146779

Detection of high frequency oscillations in epilepsy with k-means clustering method

Su Liu, Nuri F. Ince, Akin Sabanci, Aydin Aydoseli, Yavuz Aras, Altay Sencer, Nerses Bebek, Zhiyi Sha, Candan Gurses

Neurology

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

12 Scopus citations

Abstract

High frequency oscillations (HFOs) have been considered as a promising clinical biomarker of epileptogenic regions in brain. Due to their low amplitude, short duration, and variability in patterns, the visual identification of HFOs in long-term continuous intracranial EEG (iEEG) is cumbersome. The aim of our study is to improve and automatize the detection of HFO patterns by developing analysis tools based on an unsupervised k-means clustering method exploring the time-frequency content of iEEG. The clustering approach successfully isolated HFOs from noise, artifacts, and arbitrary spikes. We tested this technique on three subjects. Using this algorithm we were able to localize the seizure onset area in all of the subjects. The channel with maximum number of HFOs was associated with the seizure onset.

Original language	English (US)
Title of host publication	2015 7th International IEEE/EMBS Conference on Neural Engineering, NER 2015
Publisher	IEEE Computer Society
Pages	934-937
Number of pages	4
ISBN (Electronic)	9781467363891
DOIs	https://doi.org/10.1109/NER.2015.7146779
State	Published - Jul 1 2015
Event	7th International IEEE/EMBS Conference on Neural Engineering, NER 2015 - Montpellier, France Duration: Apr 22 2015 → Apr 24 2015

Publication series

Name	International IEEE/EMBS Conference on Neural Engineering, NER
Volume	2015-July
ISSN (Print)	1948-3546
ISSN (Electronic)	1948-3554

Other

Other	7th International IEEE/EMBS Conference on Neural Engineering, NER 2015
Country/Territory	France
City	Montpellier
Period	4/22/15 → 4/24/15

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

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10.1109/NER.2015.7146779

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Liu, S., Ince, N. F., Sabanci, A., Aydoseli, A., Aras, Y., Sencer, A., Bebek, N., Sha, Z., & Gurses, C. (2015). Detection of high frequency oscillations in epilepsy with k-means clustering method. In 2015 7th International IEEE/EMBS Conference on Neural Engineering, NER 2015 (pp. 934-937). Article 7146779 (International IEEE/EMBS Conference on Neural Engineering, NER; Vol. 2015-July). IEEE Computer Society. https://doi.org/10.1109/NER.2015.7146779

Detection of high frequency oscillations in epilepsy with k-means clustering method. / Liu, Su; Ince, Nuri F.; Sabanci, Akin et al.
2015 7th International IEEE/EMBS Conference on Neural Engineering, NER 2015. IEEE Computer Society, 2015. p. 934-937 7146779 (International IEEE/EMBS Conference on Neural Engineering, NER; Vol. 2015-July).

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

Liu, S, Ince, NF, Sabanci, A, Aydoseli, A, Aras, Y, Sencer, A, Bebek, N, Sha, Z & Gurses, C 2015, Detection of high frequency oscillations in epilepsy with k-means clustering method. in 2015 7th International IEEE/EMBS Conference on Neural Engineering, NER 2015., 7146779, International IEEE/EMBS Conference on Neural Engineering, NER, vol. 2015-July, IEEE Computer Society, pp. 934-937, 7th International IEEE/EMBS Conference on Neural Engineering, NER 2015, Montpellier, France, 4/22/15. https://doi.org/10.1109/NER.2015.7146779

Liu S, Ince NF, Sabanci A, Aydoseli A, Aras Y, Sencer A et al. Detection of high frequency oscillations in epilepsy with k-means clustering method. In 2015 7th International IEEE/EMBS Conference on Neural Engineering, NER 2015. IEEE Computer Society. 2015. p. 934-937. 7146779. (International IEEE/EMBS Conference on Neural Engineering, NER). doi: 10.1109/NER.2015.7146779

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abstract = "High frequency oscillations (HFOs) have been considered as a promising clinical biomarker of epileptogenic regions in brain. Due to their low amplitude, short duration, and variability in patterns, the visual identification of HFOs in long-term continuous intracranial EEG (iEEG) is cumbersome. The aim of our study is to improve and automatize the detection of HFO patterns by developing analysis tools based on an unsupervised k-means clustering method exploring the time-frequency content of iEEG. The clustering approach successfully isolated HFOs from noise, artifacts, and arbitrary spikes. We tested this technique on three subjects. Using this algorithm we were able to localize the seizure onset area in all of the subjects. The channel with maximum number of HFOs was associated with the seizure onset.",

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Detection of high frequency oscillations in epilepsy with k-means clustering method

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