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Towards Desynchronization Detection in Biosignals

Supratak, Akara, Schneider, Steffen, Dong, Hao, Li, Ling, Guo, Yike (2017) Towards Desynchronization Detection in Biosignals. In: Towards Desynchronization Detection in Biosignals. NIPS Time Series Workshop 2017. . (In press) (doi:https://sites.google.com/site/nipsts2017/NIPS_2017_TSW_paper_14.pdf)

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Abstract

This study presents a novel data-driven approach to detect desynchronization among biosignals from two modalities. We propose to train a deep neural network to learn synchronized patterns between biosignals from two modalities by transcribing signals from one modality into their expected, simultaneous or synchronized signal in another modality. Thus, instead of measuring the degree of synchrony between signals from different modalities using traditional linear and non-linear measures, we simplify this problem into the problem of measuring the degree of synchrony between the real and the synthesized signals from the same modality using the traditional measures. Desynchronization detection is then achieved by applying a threshold function to the estimated degree of synchrony. We demonstrate the approach with the detection of eye-movement artifacts in a public sleep dataset and compare the detection performance with traditional approaches.

Item Type: Conference or workshop item (Speech)
DOI/Identification number: https://sites.google.com/site/nipsts2017/NIPS_2017_TSW_paper_14.pdf
Uncontrolled keywords: generative adversarial networks, domain adaptation, time series, EEG
Subjects: Q Science
Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Faculties > Sciences > School of Computing > Data Science
Depositing User: Caroline Li
Date Deposited: 23 Oct 2018 17:29 UTC
Last Modified: 29 May 2019 21:20 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/69757 (The current URI for this page, for reference purposes)
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