Adaptive EEG Channel Selection for Nonconvulsive Seizure Analysis

Authors: Wang, Y. and Long, X. and v. Dijk, H. and Aarts, R. and Arends, J.

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.

[BibTeX] [ DOI]
@inproceedings{Wang2019,
  author = {{Wang}, Y. and {Long}, X. and v. {Dijk}, H. and {Aarts}, R. and {Arends}, J.},
  booktitle = {IEEE 23rd International Conference on Digital Signal Processing (DSP)},
  title = {Adaptive EEG Channel Selection for Nonconvulsive Seizure Analysis},
  year = {2018},
  volume = {},
  number = {},
  pages = {1-5},
  keywords = {bioelectric potentials;electroencephalography;medical disorders;medical signal processing;neurophysiology;patient monitoring;signal classification;epilepsy patients;continuous electroencephalography signals;multichannel EEG signals;seizure detection performance;nonconvulsive seizure patients;signal quality index;adaptive channel selection;adaptive EEG channel selection;nonconvulsive seizure detection system;seizure-related brain activity;Electroencephalography;Time-frequency analysis;Brain;Discharges (electric);Electrodes;Noise measurement;Feature extraction;nonconvulsive seizure;EEG;Channel selection},
  doi = {10.1109/ICDSP.2018.8631844},
  issn = {2165-3577},
  url = {https://research.tue.nl/files/121932638/08631844.pdf},
  month = nov
}