Objective auditory brainstem response classification using machine learning
| Autor: | Robert C. MacKinnon, Richard M. McKearney |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Adult
Male Linguistics and Language medicine.medical_specialty Databases Factual genetic structures Computer science Audiology Machine learning computer.software_genre Language and Linguistics Young Adult Speech and Hearing Deep Learning Evoked Potentials Auditory Brain Stem otorhinolaryngologic diseases medicine Humans Artificial neural network business.industry Hearing Tests Auditory Threshold Signal Processing Computer-Assisted Auditory brainstem response Acoustic Stimulation Female Artificial intelligence business computer Brain Stem |
| Zdroj: | International Journal of Audiology. 58:224-230 |
| ISSN: | 1708-8186 1499-2027 |
| DOI: | 10.1080/14992027.2018.1551633 |
| Popis: | The objective of this study was to use machine learning in the form of a deep neural network to objectively classify paired auditory brainstem response waveforms into either: ‘clear response’, ‘inconclusive’ or ‘response absent’. A deep convolutional neural network was constructed and fine-tuned using stratified 10-fold cross-validation on 190 paired ABR waveforms. The final model was evaluated on a test set of 42 paired waveforms. The full dataset comprised 232 paired ABR waveforms recorded from eight normal-hearing individuals. The dataset was obtained from the PhysioBank database. The paired waveforms were independently labelled by two audiological scientists in order to train the network and evaluate its performance. The trained neural network was able to classify paired ABR waveforms with 92.9% accuracy. The sensitivity and the specificity were 92.9% and 96.4%, respectively. This neural network may have clinical utility in assisting clinicians with waveform classification for the purpose of hearing threshold estimation. Further evaluation using a large clinically obtained dataset would provide further validation with regard to the clinical potential of the neural network in diagnostic adult testing, newborn testing and in automated newborn hearing screening. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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