Abstract
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, κ = 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.
Highlights
IntroductionThe electroencephalogram (EEG) is capable of passively monitoring neonatal cortical function
Hypoxic-ischaemic encephalopathy (HIE) is a major cause of neonatal neurological morbidity in the developed world with an incidence of 2.5/1000 births.[12,16] HIE results from a lack of oxygen and impairment to the blood supply in the neonatal brain around the time of birth andThe electroencephalogram (EEG) is capable of passively monitoring neonatal cortical function
These neonates were not treated with therapeutic hypothermia as they were recorded before 2006 when the treatment was adopted by our neonatal intensive care unit (NICU)
Summary
The electroencephalogram (EEG) is capable of passively monitoring neonatal cortical function It is portable, provides minimal disturbance to the neonate, has a high time resolution and is capable of long duration recording in excess of 48 h. The aim of visual interpretation is to grade a period of background EEG, typically an hour, as normal or abnormal and to grade the degree of abnormality This visual interpretation incorporates EEG characteristics such as amplitude, continuity, frequency content, symmetry, synchrony, sleep state cycling and clinical information such as the gestational age of the neonate, suspected diagnosis and any administered medications.[6,22,39] There are several grading or classification systems based on slightly different interpretations of these EEG and clinical characteristics and most interpretations correlate with neonatal outcome.[44]
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