Evidence map›Paper›PMID 34775553›Full record

ArticleMedical & biological engineering & computing2022

Identification of effective features of LFP signal for making closed-loop deep brain stimulation in parkinsonian rats.

Sana Amoozegar, Mohammad Pooyan, Mehrdad Roghani

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In one paragraph

Article in Medical & biological engineering & computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. State identification of Parkinson's disease based on transfer learning.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024
    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.

Sana AmoozegarDepartment of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, Iran.
Mohammad PooyanDepartment of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, Iran. pooyan@shahed.ac.ir.
Mehrdad RoghaniNeurophysiology Research Center, Shahed University, Tehran, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Traditional deep brain stimulation (DBS) is one of the acceptable methods to relieve the clinical symptoms of Parkinson's disease in its advanced stages. Today, the use of closed-loop DBS to increase stimulation efficiency and patient satisfaction is one of the most important issues under investigation. The present study was aimed to find local field potential (LFP) features of parkinsonian rats, which can determine the timing of stimulation with high accuracy. The LFP signals from rats were recorded in three groups of parkinsonian rat models receiving stimulation (stimulation), without getting stimulation (off-stimulation), and sham-controlled group. The frequency domain and chaotic features of signals were extracted for classifying three classes by support vector machine (SVM) and neural networks. The best combination of features was selected using the genetic algorithm (GA). Finally, the effective features were introduced to determine the on/off stimulation time, and the optimal stimulation parameters were identified. It was found that a combination of frequency domain and chaotic features with an accuracy of about 99% was able to determine the time the DBS must switch on. In about 80.67% of the 1861 different stimulation parameters, the brain was able to maintain its state for about 3 min after stimulation discontinuation.

Indexed as

Deep Brain StimulationParkinson DiseaseAnimalsBrainRatsSupport Vector MachineChaotic biomarkerClosed-loop deep brain stimulationLocal field potentialParkinson’s diseaseSupport vector machine

Identifiers

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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.