Changes in functional connectivity in people with HIV switching antiretroviral therapy

Journal of NeuroVirology - We assessed changes in functional connectivity by fMRI (functional magnetic resonance imaging) and cognitive measures in otherwise neurologically asymptomatic people with...     

Transient-evoked otoacoustic emission signals predicting outcomes of acute sensorineural hearing loss in patients with Ménière’s disease

Abstract

Background: Fluctuating hearing loss is characteristic of Ménière’s disease (MD) during acute episodes. However, no reliable audiometric hallmarks are available for counselling the hearing recovery possibility.

Aims/objectives: To find parameters for predicting MD hearing outcomes.

Material and methods: We applied machine learning techniques to analyse transient-evoked otoacoustic emission (TEOAE) signals recorded from patients with MD. Thirty unilateral MD patients were recruited prospectively after onset of acute cochleo-vestibular symptoms. Serial TEOAE and pure-tone audiogram (PTA) data were recorded longitudinally. Denoised TEOAE signals were projected onto the three most prominent principal directions through a linear transformation. Binary classification was performed using a support vector machine (SVM). TEOAE signal parameters, including signal energy and group delay, were compared between improved (PTA improvement: ≥15?dB) and nonimproved groups using Welch’s t-test.

Results: Signal energy did not differ (p?=?.64) but a significant difference in 1-kHz (p?=?.045) group delay was recorded between improved and nonimproved groups. The SVM achieved a cross-validated accuracy of >80% in predicting hearing outcomes.

Conclusions and significance: This study revealed that baseline TEOAE parameters obtained during acute MD episodes, when processed through machine learning technology, may provide information on outer hair cell function to predict hearing recovery.