Evidence map›Paper›PMID 36501787›Full record

ArticleSensors (Basel, Switzerland)2022

Optimized Acoustic Phantom Design for Characterizing Body Sound Sensors.

Valerie Rennoll, Ian McLane, Mounya Elhilali, James E West

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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

4 authors.

Valerie RennollDepartment of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.ORCID 0000-0003-2354-3377
Ian McLaneDepartment of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.ORCID 0000-0002-1777-8105
Mounya ElhilaliDepartment of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.ORCID 0000-0003-2597-738X
James E WestDepartment of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Many commercial and prototype devices are available for capturing body sounds that provide important information on the health of the lungs and heart; however, a standardized method to characterize and compare these devices is not agreed upon. Acoustic phantoms are commonly used because they generate repeatable sounds that couple to devices using a material layer that mimics the characteristics of skin. While multiple acoustic phantoms have been presented in literature, it is unclear how design elements, such as the driver type and coupling layer, impact the acoustical characteristics of the phantom and, therefore, the device being measured. Here, a design of experiments approach is used to compare the frequency responses of various phantom constructions. An acoustic phantom that uses a loudspeaker to generate sound and excite a gelatin layer supported by a grid is determined to have a flatter and more uniform frequency response than other possible designs with a sound exciter and plate support. When measured on an optimal acoustic phantom, three devices are shown to have more consistent measurements with added weight and differing positions compared to a non-optimal phantom. Overall, the statistical models developed here provide greater insight into acoustic phantom design for improved device characterization.

Indexed as

AcousticsSoundEquipment DesignGelatinPhantoms, ImagingGelatinacoustic phantomfrequency responsesensor characterizationstethoscope

Identifiers

PMID36501787
PMCPMC9735779

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Registered trials

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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.