A discriminative approach to automatic seizure detection in multichannel EEG signals

A discriminative approach to automatic seizure detection in multichannel EEG signals. James, D., Xie, X., & Eslambolchilar, P. In 2014 22nd European Signal Processing Conference (EUSIPCO), pages 2010-2014, Sep., 2014.

Paper abstract bibtex

The aim of this paper is to introduce the application of Random Forests to the automated analysis of epileptic EEG data. Feature extraction is performed using a discrete wavelet transform to give time-frequency representations, from which statistical features based on the wavelet decompositions are formed and used for training and classification. We show that Random Forests can be used for the classification of ictal, inter-ictal and healthy EEG with a high level of accuracy, with 99% sensitivity and 93.5% specificity for classifying ictal and inter-ictal EEG, 90.6% sensitivity and 95.7% specificity for the windowed data and 93.9% sensitivity for seizure onset classification.

@InProceedings{6952742,
  author = {D. James and X. Xie and P. Eslambolchilar},
  booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},
  title = {A discriminative approach to automatic seizure detection in multichannel EEG signals},
  year = {2014},
  pages = {2010-2014},
  abstract = {The aim of this paper is to introduce the application of Random Forests to the automated analysis of epileptic EEG data. Feature extraction is performed using a discrete wavelet transform to give time-frequency representations, from which statistical features based on the wavelet decompositions are formed and used for training and classification. We show that Random Forests can be used for the classification of ictal, inter-ictal and healthy EEG with a high level of accuracy, with 99% sensitivity and 93.5% specificity for classifying ictal and inter-ictal EEG, 90.6% sensitivity and 95.7% specificity for the windowed data and 93.9% sensitivity for seizure onset classification.},
  keywords = {electroencephalography;feature extraction;medical signal processing;seizure onset classification;inter-ictal EEG;wavelet decompositions;statistical features;time frequency representations;discrete wavelet transform;feature extraction;epileptic EEG data;automated analysis;random forests;multichannel EEG signals;automatic seizure detection;Electroencephalography;Feature extraction;Sensitivity;Vectors;Accuracy;Radio frequency;Training},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569925083.pdf},
}

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