Denoising click-evoked otoacoustic emission signals by optimal shrinkage.


Journal

The Journal of the Acoustical Society of America
ISSN: 1520-8524
Titre abrégé: J Acoust Soc Am
Pays: United States
ID NLM: 7503051

Informations de publication

Date de publication:
04 2021
Historique:
entrez: 4 5 2021
pubmed: 5 5 2021
medline: 6 7 2021
Statut: ppublish

Résumé

Click-evoked otoacoustic emissions (CEOAEs) are clinically used as an objective way to infer whether cochlear functions are normal. However, because the sound pressure level of CEOAEs is typically much lower than the background noise, it usually takes hundreds, if not thousands, of repetitions to estimate the signal with sufficient accuracy. In this paper, we propose to improve the signal-to-noise ratio (SNR) of CEOAE signals within limited measurement time by optimal shrinkage (OS) in two different settings: covariance-based optimal shrinkage (cOS) and singular value decomposition-based optimal shrinkage (sOS). By simulation, the cOS consistently enhanced the SNR by 1-2 dB from a baseline method that is based on calculating the median. In real data, however, the cOS cannot enhance the SNR over 1 dB. The sOS achieved a SNR enhancement of 2-3 dB in simulation and demonstrated capability to enhance the SNR in real recordings. In addition, the level of enhancement increases as the baseline SNR decreases. An appealing property of OS is that it produces an estimate of all single trials. This property makes it possible to investigate CEOAE dynamics across a longer period of time when the cochlear conditions are not strictly stationary.

Identifiants

pubmed: 33940909
doi: 10.1121/10.0004264
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

2659

Auteurs

Tzu-Chi Liu (TC)

Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan.

Yi-Wen Liu (YW)

Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan.

Hau-Tieng Wu (HT)

Department of Mathematics and Department of Statistical Science, Duke University, Durham, North Carolina 27708, USA.

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Classifications MeSH