Analysis of amplitude-integrated EEG in the newborn based on approximate entropy

Amplitude-integrated electroencephalographic (aEEG), a cerebral-function-monitoring method, is widely used in response to the clinical needs for continuous EEG monitoring. In this paper, we present an approach to analyze aEEG in newborns based on approximate entropy (ApEn). Unlike the traditional aEEG signal processing and diagnosing methods, the Box-Cox transformation is substituted for semilogarithmic amplitude compression to keep the continuity of the signal, reduce the excessive compression of chaotic information in high amplitudes, and use ApEn, rather than the amplitudes of the borders, to estimate the degree of chaos in the signal. Experiments with aEEGs of 120 cases (32 normal and 88 abnormal of full-term infants, and 57 cases of preterm infants) were conducted to validate the effectiveness of the proposed method. The results show an aEEG signal analyzed based on the proposed algorithm always belongs to an abnormal case and needs to be examined by physicians if the corresponding indicator is considered abnormal. The novel description of aEEG could be helpful in detecting brain disorders in the newborn as a new clinical target.