Evidence map›Paper›PMID 36290973›Full record

ReviewBiosensors2022

Sensing Devices for Detecting and Processing Acoustic Signals in Healthcare.

Norma Mallegni, Giovanna Molinari, Claudio Ricci, Andrea Lazzeri, Davide La Rosa, Antonino Crivello, Mario Milazzo

Abstract readReview
In one paragraph

Review in Biosensors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Review
  5. Review
  6. [Improving adaptive noise reduction performance of body sound auscultation through linear preprocessing].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024
    Article
  7. Review
  8. Article
  9. 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

7 authors.

Norma MallegniDepartment of Civil and Industrial Engineering, University of Pisa, 56122 Pisa, Italy.ORCID 0000-0003-4179-4962
Giovanna MolinariDepartment of Civil and Industrial Engineering, University of Pisa, 56122 Pisa, Italy.ORCID 0000-0002-6142-9922
Claudio RicciDepartment of Civil and Industrial Engineering, University of Pisa, 56122 Pisa, Italy.
Andrea LazzeriDepartment of Civil and Industrial Engineering, University of Pisa, 56122 Pisa, Italy.ORCID 0000-0002-9463-1502
Davide La RosaISTI-CNR, Institute of Information Science and Technologies, 56124 Pisa, Italy.ORCID 0000-0001-8424-0762
Antonino CrivelloISTI-CNR, Institute of Information Science and Technologies, 56124 Pisa, Italy.ORCID 0000-0001-7238-2181
Mario MilazzoDepartment of Civil and Industrial Engineering, University of Pisa, 56122 Pisa, Italy.ORCID 0000-0001-8429-5544

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Acoustic signals are important markers to monitor physiological and pathological conditions, e.g., heart and respiratory sounds. The employment of traditional devices, such as stethoscopes, has been progressively superseded by new miniaturized devices, usually identified as microelectromechanical systems (MEMS). These tools are able to better detect the vibrational content of acoustic signals in order to provide a more reliable description of their features (e.g., amplitude, frequency bandwidth). Starting from the description of the structure and working principles of MEMS, we provide a review of their emerging applications in the healthcare field, discussing the advantages and limitations of each framework. Finally, we deliver a discussion on the lessons learned from the literature, and the open questions and challenges in the field that the scientific community must address in the near future.

Indexed as

AcousticsMicro-Electrical-Mechanical SystemsDelivery of Health CareMonitoring, Physiologicaccelerometersacoustic signalshealthcareMEMSmonitoring

Identifiers

PMID36290973
PMCPMC9599683

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.