Evidence map›Paper›PMID 34705633›Full record

ArticleIEEE transactions on bio-medical engineering2022

CCi-MOBILE: A Portable Real Time Speech Processing Platform for Cochlear Implant and Hearing Research.

Ria Ghosh, Hussnain Ali, John H L Hansen

Open access · greenAbstract read
In one paragraph

Article in IEEE transactions on bio-medical engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
2.8field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 36 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 1 country.

Ria Ghosh
Hussnain Ali
John H L Hansen
The University of Texas at Dallas · USHarman (United States) · US

Funding

CCI-Mobile: Signal Processing Advancements for Cochlear Implant Users in Naturalistic EnvironmentsR01DC016839 · NIDCD · UNIVERSITY OF TEXAS DALLAS · PI HANSEN, JOHN H.L., LITOVSKY, RUTH Y · 2018 to 2022
$3.6M
Leveraging Natural Language Processing for Reverberant Speech Enhancement in Cochlear ImplantsR56DC020267 · NIDCD · DUKE UNIVERSITY · PI COLLINS, LESLIE M., HANSEN, JOHN H.L. · 2023 to 2023
$174k
NIDCD NIH HHS R01 DC016839NIDCD NIH HHS R56 DC020267
6 · The paper itself

Abstract

Experimental hardware-research interfaces form a crucial role during developmental stages of any medical, signal-monitoring system as it allows researchers to test and optimize output results before perfecting the design for the actual FDA approved medical device and large-scale production. These testing platforms, intake raw signals through which performance of novel algorithms can be analyzed and modified to generate the desired data points for an optimized output, allowing the advancement of the medical device. With cochlear implants (CIs) and hearing aids (HAs) becoming a more common solution for varying degrees of hearing impairment, having modern signal processing strategies tested for such speech sensitive systems is a necessity. But the rigid design requirements of commercial CI and HA processors make it difficult to explore novel algorithms for research investigations and conducting longitudinal studies. This study presents the design, development, clinical evaluation, and applications of CCi-MOBILE, a computationally powerful signal processing testing platform built for researchers in the hearing-impaired field. The custom-made, portable research platform allows researchers to design and perform complex speech processing algorithm assessment offline and in real-time. It can be operated through user-friendly, open-source software and is compatible with implants manufactured by Cochlear Corporation. The FPGA design and hardware processing pipeline for CI stimulation is discussed followed by results from an acute study with implant users' speech intelligibility in quiet and noisy conditions. The results show a consistent level of performance compared with CI users' clinical processor, thus confirming the viability of the platform in chronic CI based studies.

Indexed as

Cochlear ImplantationCochlear ImplantsSpeech PerceptionHearingSpeech Intelligibility

Identifiers

PMID34705633
PMCPMC8918373
OpenAlexW3210307430

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.