Automated classification of acoustic startle reflex waveforms in young CBA/CaJ mice using machine learning

Fawcett, Timothy J.; Cooper, Chad S.; Longenecker, Ryan J.; Walton, Joseph P.

doi:10.1016/j.jneumeth.2020.108853

Cited by 7 publications

(4 citation statements)

References 52 publications

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“…The disadvantage of this test is that it is subjective, reflects only the better ear, and is insensitive to less severe hearing loss ( Jero et al, 2001 ). Recently, to overcome the subjectivity of this test, a team developed an automated classification of acoustic startle reflex waveforms using a machine learning method ( Fawcett, Cooper, Longenecker, & Walton, 2020 ).…”

Section: Methods Used To Study Hearing Lossmentioning

confidence: 99%

Congenital Deafness and Recent Advances Towards Restoring Hearing Loss

Renauld

Basch

2021

Current Protocols

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Congenital hearing loss is the most common birth defect, estimated to affect 2‐3 in every 1000 births. Currently there is no cure for hearing loss. Treatment options are limited to hearing aids for mild and moderate cases, and cochlear implants for severe and profound hearing loss. Here we provide a literature overview of the environmental and genetic causes of congenital hearing loss, common animal models and methods used for hearing research, as well as recent advances towards developing therapies to treat congenital deafness. © 2021 The Authors.

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Section: Methods Used To Study Hearing Lossmentioning

confidence: 99%

Congenital Deafness and Recent Advances Towards Restoring Hearing Loss

Renauld

Basch

2021

Current Protocols

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“…The details of the startle waveform acquisition, sampling and procedure are identical to those in Fawcett et al. [4] . Briefly, individual mice were placed in wire cages which rested on platforms with embedded piezoelectric transducers.…”

Section: Methodsmentioning

confidence: 99%

“…In an attempt to employ algorithms to find optimal feature combinations, various techniques from diverse algorithm families including Linear classification (logistic regression and linear discriminant analysis), Bagged classification (bagged tree and random forests), Boosted classification (extreme gradient boosted and C5.0), and Discriminative classification via kernels (support vector machine) were employed as reported in Fawcett et al. [4] . All machine learning algorithms were implemented in the caret package [11] in the R programming language.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

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Machine learning, waveform preprocessing and feature extraction methods for classification of acoustic startle waveforms

et al. 2021

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The acoustic startle response (ASR) is an involuntary muscle reflex that occurs in response to a transient loud sound and is a highly-utilized method of assessing hearing status in animal models. Currently, a high level of variability exists in the recording and interpretation of ASRs due to the lack of standardization for collecting and analyzing these measures. An ensembled machine learning model was trained to predict whether an ASR waveform is a startle or non-startle using highly-predictive features extracted from normalized ASR waveforms collected from young adult CBA/CaJ mice. Features were extracted from the normalized waveform as well as the power spectral density estimates and continuous wavelet transforms of the normalized waveform. Machine learning models utilizing methods from different families of algorithms were individually trained and then ensembled together, resulting in an extremely robust model. ASR waveforms were normalized using the mean and standard deviation computed before the startle elicitor was presented 9 machine learning algorithms from 4 different families of algorithms were individually trained using features extracted from the normalized ASR waveforms Trained machine learning models were ensembled to produce an extremely robust classifier

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Implementation of Ensemble Deep Learning Coupled with Remote Sensing for the Quantitative Analysis of Changes in Arable Land Use in a Mining Area

Luo

2021

J Indian Soc Remote Sens

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Automated classification of acoustic startle reflex waveforms in young CBA/CaJ mice using machine learning

Cited by 7 publications

References 52 publications

Congenital Deafness and Recent Advances Towards Restoring Hearing Loss

Congenital Deafness and Recent Advances Towards Restoring Hearing Loss

Machine learning, waveform preprocessing and feature extraction methods for classification of acoustic startle waveforms

Implementation of Ensemble Deep Learning Coupled with Remote Sensing for the Quantitative Analysis of Changes in Arable Land Use in a Mining Area

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