Real-time threshold determination of auditory brainstem responses by cross-correlation analysis

Wang, Haoyu; Li, Bei; Lu, Yan; Han, Kun; Sheng, Haibin; Zhou, Jialei; Qi, Yumeng; Wang, Xueling; Huang, Zhiwu; Song, Lei; Hua, Yunfeng

doi:10.1016/j.isci.2021.103285

Cited by 11 publications

(10 citation statements)

References 37 publications

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“…The determination of hearing thresholds involves interpretation of the ABR waveform ( Wang et al, 2021 ). ABR thresholds are generally determined as the lowest sound intensity at which a waveform is manually observed ( Sato et al, 2010 ; Muniak et al, 2018 ; Buran et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

Bats experience age-related hearing loss (presbycusis)

et al. 2023

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Hearing loss is a hallmark of aging, typically initially affecting the higher frequencies. In echolocating bats, the ability to discern high frequencies is essential. However, nothing is known about age-related hearing loss in bats, and they are often assumed to be immune to it. We tested the hearing of 47 wild Egyptian fruit bats by recording their auditory brainstem response and cochlear microphonics, and we also assessed the cochlear histology in four of these bats. We used the bats’ DNA methylation profile to evaluate their age and found that bats exhibit age-related hearing loss, with more prominent deterioration at the higher frequencies. The rate of the deterioration was ∼1 dB per year, comparable to the hearing loss observed in humans. Assessing the noise in the fruit bat roost revealed that these bats are exposed to continuous immense noise—mostly of social vocalizations—supporting the assumption that bats might be partially resistant to loud noise. Thus, in contrast to previous assumptions, our results suggest that bats constitute a model animal for the study of age-related hearing loss.

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Section: Methodsmentioning

confidence: 99%

Bats experience age-related hearing loss (presbycusis)

et al. 2023

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“…ABR measurement were conducted as previously described (Wang et al, 2021; Zhang et al, 2022). In brief, animals were anesthetized, and the body temperature was maintained near 37°C using a regulated heating pad with a thermal probe placed under the abdomen (Homeothermic Monitoring System, Harvard Apparatus, US).…”

Section: Methodsmentioning

confidence: 99%

Noise-induced synaptopathy impacts the long and short sensory hair cells differently in the mammalian cochlea

Wang

et al. 2023

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In mammals, neural encoding of sounds is mediated by ribbon-type synapses between inner ear hair cells (IHCs) and postsynaptic spiral ganglion neurons. These specialized connections are arranged along the IHC basolateral surface with gradient changes in morphology and electrophysiological properties, which are essential for the level coding of sound intensity. Large synaptic ribbons, which predominantly reside on the modiolar IHC face, are responsive to loud sounds and more susceptible to acoustic insults. Selective loss of them is the major cause for reduced cochlear audible range. However, other than fewer ribbons after noise exposure, the consequential presynaptic reorganization in the IHCs remains less characterized. Here, by means of volume electron microscopy and artificial intelligence-assisted image analysis, we investigated noise exposure-related changes in the mouse IHCs. Apart from morphological changes of the ribbon-type active zones, our quantifications reveal differential synaptic reorganization and mitochondrial remodeling in an IHC subtype-specific manner. Together, these results argue for putative presynaptic mechanisms underlying activity-dependent structural changes of synaptic ribbons and the associated mitochondrial network (which powers the neurotransmission) in the sensory epithelial cells.

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“…Our correlation algorithm is based on the method of Wang et al which explores the correlation of ABR signals ( 26 ). The correlation procedure relies on the repeatability of the similarity of two waveforms.…”

Section: Methodsmentioning

confidence: 99%

“…For other electrophysiological signals, objectified analyses have become established in clinical practice. For example, for auditory brainstem responses (ABR), correlation analysis is used ( 25 , 26 ). In the evaluation of cortical auditory evoked potentials (CAEP), Hotelling's T 2 test has yielded a sensitivity at least comparable to that of visual inspection ( 27 – 29 ).…”

Section: Introductionmentioning

confidence: 99%

Objectification of intracochlear electrocochleography using machine learning

et al. 2022

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IntroductionElectrocochleography (ECochG) measures inner ear potentials in response to acoustic stimulation. In patients with cochlear implant (CI), the technique is increasingly used to monitor residual inner ear function. So far, when analyzing ECochG potentials, the visual assessment has been the gold standard. However, visual assessment requires a high level of experience to interpret the signals. Furthermore, expert-dependent assessment leads to inconsistency and a lack of reproducibility. The aim of this study was to automate and objectify the analysis of cochlear microphonic (CM) signals in ECochG recordings.MethodsProspective cohort study including 41 implanted ears with residual hearing. We measured ECochG potentials at four different electrodes and only at stable electrode positions (after full insertion or postoperatively). When stimulating acoustically, depending on the individual residual hearing, we used three different intensity levels of pure tones (i.e., supra-, near-, and sub-threshold stimulation; 250–2,000 Hz). Our aim was to obtain ECochG potentials with differing SNRs. To objectify the detection of CM signals, we compared three different methods: correlation analysis, Hotelling's T2 test, and deep learning. We benchmarked these methods against the visual analysis of three ECochG experts.ResultsFor the visual analysis of ECochG recordings, the Fleiss' kappa value demonstrated a substantial to almost perfect agreement among the three examiners. We used the labels as ground truth to train our objectification methods. Thereby, the deep learning algorithm performed best (area under curve = 0.97, accuracy = 0.92), closely followed by Hotelling's T2 test. The correlation method slightly underperformed due to its susceptibility to noise interference.ConclusionsObjectification of ECochG signals is possible with the presented methods. Deep learning and Hotelling's T2 methods achieved excellent discrimination performance. Objective automatic analysis of CM signals enables standardized, fast, accurate, and examiner-independent evaluation of ECochG measurements.

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Real-time threshold determination of auditory brainstem responses by cross-correlation analysis

Cited by 11 publications

References 37 publications

Bats experience age-related hearing loss (presbycusis)

Bats experience age-related hearing loss (presbycusis)

Noise-induced synaptopathy impacts the long and short sensory hair cells differently in the mammalian cochlea

Objectification of intracochlear electrocochleography using machine learning

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