Evidence map›Paper›PMID 30271224›Full record

ArticleMedical devices (Auckland, N.Z.)2018

Tensometric tremorography in high-precision medical diagnostic systems.

Zoya Aleksanyan, Olga Bureneva, Nikolay Safyannikov

Open access · goldAbstract read
In one paragraph

Article in Medical devices (Auckland, N.Z.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 43% 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

1 citing paper in PubMed, 7 citations in OpenAlex.

  1. 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

3 authors at 2 institutions in 1 country.

Zoya AleksanyanInstitute of the Human Brain, Russian Academy of Sciences, Saint Petersburg, Russia.
Olga BurenevaDepartment of Computer Science and Engineering, Saint-Petersburg State Electrotechnical University "LETI", Saint Peterburg, Russia, oibureneva@etu.ru.
Nikolay SafyannikovDepartment of Computer Science and Engineering, Saint-Petersburg State Electrotechnical University "LETI", Saint Peterburg, Russia, oibureneva@etu.ru.
Saint Petersburg State Electrotechnical University · RUInstitute of the Human Brain · RU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe objective of the study was to develop a system for the precision diagnostics of pathologies of motor brain regions based on tensometric measurement and to explore its diagnostic capabilities. MATERIALS AND

methodsTremor is a syndrome that indicates the abnormal state of the central nervous system, primarily in the motor brain regions. Analysis of tremor parameters provides significant information about the changes in the body motion control and can be used as an objective index of the central nervous system state. Existing methods are aimed at the analysis of visible tremor based on the use of different sensors. We suggest an alternative approach based on the use of a tensometric system performing tremor measurements when the tremor appears on the background of voluntary isometric efforts. The key advantage of our approach is that it allows to determine the tremor before its visible manifestation. In the article, we describe hardware implementation of our tremor analysis system.

resultsIn the article, we represent the new methodology and the original equipment based on the control of isometric effort. Isometric effort formed by a patient is controlled with the use of a feedback system on the patient's monitor. We evaluated the performance of our equipment with more than 400 healthy volunteers and patients with various pathologies of the central nervous system motor regions, and the results of the investigations, allowing to identify tremor parameters typical for parkinsonism, are represented in our article.

conclusionTesting of the system confirmed its high diagnostic validity and reliability, high sensitivity, simplicity and high speed of information processing. The approach based on tensometric measurements is very promising for the diagnostics of Parkinson disease and dysfunctions of a central nervous system.

Indexed as

biological feedbackhands oscillationsisometric methodprecision diagnosticstremor

Identifiers

PMID30271224
PMCPMC6145354
OpenAlexW2891592822

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.