中国神经再生研究(英文版) ›› 2020, Vol. 15 ›› Issue (2): 222-231.doi: 10.4103/1673-5374.265542

• 综述:脑损伤修复保护与再生 • 上一篇    下一篇

先进信号处理和机器学习在新生儿缺氧缺血性脑电图中的应用

  

  • 出版日期:2020-02-15 发布日期:2020-05-23

Applications of advanced signal processing and machine learning in the neonatal hypoxic-ischemic electroencephalography

Hamid Abbasi, Charles P. Unsworth   

  1. Department of Engineering Science, The University of Auckland, Auckland, New Zealand
  • Online:2020-02-15 Published:2020-05-23
  • Contact: Charles P. Unsworth, PhD,c.unsworth@auckland.ac.nz.
  • Supported by:
    This work was supported by the Auckland Medical Research Foundation, No. 1117017 (to CPU).

摘要: orcid: 0000-0002-9153-5232 (Charles P. Unsworth)

Abstract: Perinatal hypoxic-ischemic-encephalopathy significantly contributes to neonatal death and life-long disability such as cerebral palsy. Advances in signal processing and machine learning have provided the research community with an opportunity to develop automated real-time identification techniques to detect the signs of hypoxic-ischemic-encephalopathy in larger electroencephalography/amplitude-integrated electroencephalography data sets more easily. This review details the recent achievements, performed by a number of prominent research groups across the world, in the automatic identification and classification of hypoxic-ischemic epileptiform neonatal seizures using advanced signal processing and machine learning techniques. This review also addresses the clinical challenges that current automated techniques face in order to be fully utilized by clinicians, and highlights the importance of upgrading the current clinical bedside sampling frequencies to higher sampling rates in order to provide better hypoxic-ischemic biomarker detection frameworks. Additionally, the article highlights that current clinical automated epileptiform detection strategies for human neonates have been only concerned with seizure detection after the therapeutic latent phase of injury. Whereas recent animal studies have demonstrated that the latent phase of opportunity is critically important for early diagnosis of hypoxic-ischemic-encephalopathy electroencephalography biomarkers and although difficult, detection strategies could utilize biomarkers in the latent phase to also predict the onset of future seizures.

Key words: advanced signal processing, aEEG, automatic detection, classification, clinical, EEG, fetal, HIE, hypoxic-ischemic encephalopathy, machine learning, neonatal seizure, real-time identification, review