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Auditory brainstem response classification: A hybrid model using time and frequency features
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DOI：10.1016/j.artmed.2006.07.001.png)
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Objective: The auditory brainstem response (ABR) is an evoked response obtained from brain electrical activity when an auditory stimulus is applied to the ear. An audiologist can determine the threshold level of hearing by applying stimuli at reducing levels of intensity, and can also diagnose various otological, audiological, and neurological abnormalities by examining the morphology of the waveform and the latencies of the individual waves. This is a subjective process requiring considerable expertise. The aim of this research was to develop,software classification models to assist the audiologist with an automated detection of the ABR waveform and also to provide objectivity and consistency in this detection. Materials and methods: The dataset used in this study consisted of 550 waveforms derived from tests using a range of stimulus levels applied to 85 subjects ranging in hearing ability. Each waveform had been classified by a human expert as 'response = Yes' or 'response = No'. Individual software classification models were generated using time, frequency and cross-correlation measures. Classification employed both artificial neural networks (NNs) and the C5.0 decision tree algorithm. Accuracies were validated using sixfold cross-validation, and by randomising training, validation and test datasets. Results: The result was a two stage classification process whereby strong responses were classified to an accuracy of 95.6% in the first stage. This used a ratio of poststimulus to pre-stimulus power in the time domain, with power measures at 200, 500 and 900 Hz in the frequency domain. In the second stage, outputs from time, frequency and cross-correlation classifiers were combined using the Dempster-Shafer method to produce a hybrid model with an accuracy of 85% (126 repeat waveforms).
Keyword:
decision support
auditory brainstem response
feature selection
hybrid model
classification
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