EEG based automated detection of auditory loss: A pilot study
Auditory dysfunction is one of the most common deficiencies present in the newborn. Recent studies show that significant bilateral hearing loss is present in ?1 to 3 per 1000 newborn infants in the well-baby nursery population and in ?2 to 4 per 1000 infants in the ICU population. The ignorance of hearing screening test at the initial stage will impede speech, language and cognitive development. It has been further noted that direct physician observation as well as parental recognition has not been significantly successful until today in identifying the hearing loss in the first year of life. To overcome such problems, early screening is essential. This paper presents a pilot study on detection of hearing loss by applying electroencephalography (EEG) signals as the key indicator. The effect of auditory evoked potential (AEP) is exploited on EEGs by introducing an external stimulus to the subjects auditory canal. Two time domain features, spike rhythmicity, autoregressive model using LevinsonDurbin algorithm and frequency domain features such as power spectral density estimation by Burgs and YuleWalker methods are applied. Feed forward and feedback neural network models are used to distinguish the stimuli and non-stimuli EEGs. The neural network models are configured optimally by varying the hidden neurons and learning algorithms and their performance are evaluated in terms of specificity, sensitivity and classification accuracy. It can be concluded from the experimental study that the proposed methodology can be applied for neonatal healthcare applications.
eeg hearing loss neural network neonatal classifier time-frequency domain features