Adaptive EEG channel selection for nonconvulsive seizure analysis

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Abstract

A preliminary work of the nonconvulsive seizure detection system is presented here. The system aims at detecting nonconvulsive seizures for epilepsy patients, targeting a 24/7 monitoring based on continuous electroencephalography (EEG) signals. It has been observed that the interesting seizure-related brain activities in some of the multi-channel EEG signals were weak, often with a noisy background or artifacts, and this might also be patient-dependent. Therefore, using the 'best' channels with a good signal quality is expected to enhance the seizure detection performance. This paper describes a method to select the 'best' EEG channels adaptively from the data of nonconvulsive seizure patients. A signal quality index (SQI) was proposed, where a higher SQI of a channel (signal) indicates a stronger brain activity associated with the ictals of nonconvulsive seizures and less artifacts. The validity of the SQI for adaptive channel selection is demonstrated in this paper. Advantages and limitations of our proposed method were discussed.

Original languageEnglish
Title of host publication2018 IEEE 23rd International Conference on Digital Signal Processing, DSP 2018
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Number of pages5
ISBN (Electronic)9781538668115
DOIs
Publication statusPublished - 31 Jan 2019
Event23rd IEEE International Conference on Digital Signal Processing, DSP 2018 - Shanghai, China
Duration: 19 Nov 201821 Nov 2018

Conference

Conference23rd IEEE International Conference on Digital Signal Processing, DSP 2018
CountryChina
CityShanghai
Period19/11/1821/11/18

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Keywords

  • Channel selection
  • EEG
  • nonconvulsive seizure

Cite this

Wang, Y., Long, X., van Dijk, H., Aarts, R., & Arends, J. (2019). Adaptive EEG channel selection for nonconvulsive seizure analysis. In 2018 IEEE 23rd International Conference on Digital Signal Processing, DSP 2018 [8631844] Piscataway: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICDSP.2018.8631844