TY  - JOUR
  - Stevenson NJ, Korotchikova I, Temko A, Lightbody G, Marnane WP, Boylan GB
  - 2013
  - April
  - Annals of Biomedical Engineering
  - An automated system for grading EEG abnormality in term neonates with hypoxic-ischaemic encephalopathy.
  - Validated
  - ()
  - 41
  - 4
  - 775
  - 785
  - Automated analysis of the neonatal EEG has the potential to assist clinical decision making for neonates with hypoxic-ischaemic encephalopathy. This paper proposes a method of automatically grading the degree of abnormality in an hour long epoch of neonatal EEG. The automated grading system (AGS) was based on a multi-class linear classifier grading of short-term epochs of EEG which were converted into a long-term grading of EEG using a majority vote operation. The features used in the AGS were summary measurements of two sub-signals extracted from a quadratic time-frequency distribution: the amplitude modulation and instantaneous frequency. These sub-signals were based on a model of EEG as a multiplication of a coloured random process with a slowly varying pseudo-periodic waveform and may be related to macroscopic neurophysiological function. The 4 grade AGS had a classification accuracy of 83% compared to human annotation of the EEG (level of agreement, ¿ = 0.76). Features estimated on the developed sub-signals proved more effective at grading the EEG than measures based solely on the EEG and the incorporation of additional sub-grades based on EEG states into the AGS also improved performance.
  - 10.1007/s10439-012-0710-5
DA  - 2013/04
ER  - 

@article{V241502647,
   = {Stevenson NJ,  Korotchikova I and  Temko A,  Lightbody G and  Marnane WP,  Boylan GB },
   = {2013},
   = {April},
   = {Annals of Biomedical Engineering},
   = {An automated system for grading EEG abnormality in term neonates with hypoxic-ischaemic encephalopathy.},
   = {Validated},
   = {()},
   = {41},
   = {4},
  pages = {775--785},
   = {{Automated analysis of the neonatal EEG has the potential to assist clinical decision making for neonates with hypoxic-ischaemic encephalopathy. This paper proposes a method of automatically grading the degree of abnormality in an hour long epoch of neonatal EEG. The automated grading system (AGS) was based on a multi-class linear classifier grading of short-term epochs of EEG which were converted into a long-term grading of EEG using a majority vote operation. The features used in the AGS were summary measurements of two sub-signals extracted from a quadratic time-frequency distribution: the amplitude modulation and instantaneous frequency. These sub-signals were based on a model of EEG as a multiplication of a coloured random process with a slowly varying pseudo-periodic waveform and may be related to macroscopic neurophysiological function. The 4 grade AGS had a classification accuracy of 83% compared to human annotation of the EEG (level of agreement, ¿ = 0.76). Features estimated on the developed sub-signals proved more effective at grading the EEG than measures based solely on the EEG and the incorporation of additional sub-grades based on EEG states into the AGS also improved performance.}},
   = {10.1007/s10439-012-0710-5},
  source = {IRIS}
}