Evidence map›Paper›PMID 36421496›Full record

ArticleEntropy (Basel, Switzerland)2022

Similarity-Based Adaptive Window for Improving Classification of Epileptic Seizures with Imbalance EEG Data Stream.

Hayder K Fatlawi, Attila Kiss

Abstract read
In one paragraph

Article in Entropy (Basel, Switzerland), 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. 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

2 authors.

Hayder K FatlawiDepartment of Information Systems, ELTE Eötvös Loránd University, 1117 Budapest, Hungary.ORCID 0000-0001-7243-8181
Attila KissDepartment of Information Systems, ELTE Eötvös Loránd University, 1117 Budapest, Hungary.ORCID 0000-0001-8174-6194

Funding

Application Domain Specific Highly Reliable IT Solutions' project, National Research, Development and Innovation Fund of Hungary, the Thematic Excellence Programme TKP2020-NKA-06 TKP2020-NKA-06
6 · The paper itself

Abstract

Data stream mining techniques have recently received increasing research interest, especially in medical data classification. An unbalanced representation of the classification's targets in these data is a common challenge because classification techniques are biased toward the major class. Many methods have attempted to address this problem but have been exaggeratedly biased toward the minor class. In this work, we propose a method for balancing the presence of the minor class within the current window of the data stream while preserving the data's original majority as much as possible. The proposed method utilized similarity analysis for selecting specific instances from the previous window. This group of minor-class was then added to the current window's instances. Implementing the proposed method using the Siena dataset showed promising results compared to the Skew ensemble method and some other research methods.

Indexed as

EEGimbalanced datamachine learningsimilarity analysis

Identifiers

PMID36421496
PMCPMC9689083

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.