論文

査読有り 国際誌
2019年7月

Autoencoding of long-term scalp electroencephalogram to detect epileptic seizure for diagnosis support system.

Computers in biology and medicine
  • Ali Emami
  • ,
  • Naoto Kunii
  • ,
  • Takeshi Matsuo
  • ,
  • Takashi Shinozaki
  • ,
  • Kensuke Kawai
  • ,
  • Hirokazu Takahashi

110
開始ページ
227
終了ページ
233
記述言語
英語
掲載種別
DOI
10.1016/j.compbiomed.2019.05.025

INTRODUCTION: Epileptologists could benefit from a diagnosis support system that automatically detects seizures because visual inspection of long-term electroencephalograms (EEGs) is extremely time-consuming. However, the diversity of seizures among patients makes it difficult to develop universal features that are applicable for automatic seizure detection in all cases, and the rarity of seizures results in a lack of sufficient training data for classifiers. METHODS: To overcome these problems, we utilized an autoencoder (AE), which is often used for anomaly detection in the field of machine learning, to perform seizure detection. We hypothesized that multichannel EEG signals are compressible by AE owing to their spatio-temporal coupling and that the AE should be able to detect seizures as anomalous events from an interictal EEG. RESULTS: Through experiments, we found that the AE error was able to classify seizure and nonseizure states with a sensitivity of 100% in 22 out of 24 available test subjects and that the AE was better than the commercially available software BESA and Persyst for half of the test subjects. CONCLUSIONS: These results suggest that the AE error is a feasible candidate for a universal seizure detection feature.

リンク情報
DOI
https://doi.org/10.1016/j.compbiomed.2019.05.025
PubMed
https://www.ncbi.nlm.nih.gov/pubmed/31202153
ID情報
  • DOI : 10.1016/j.compbiomed.2019.05.025
  • PubMed ID : 31202153

エクスポート
BibTeX RIS