Evidence map›Paper›PMID 39120777›Full record

ArticleNeurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology2024

MFCC-CNN: A patient-independent seizure prediction model.

Fan Zhang, Boyan Zhang, Siyuan Guo, Xinhong Zhang

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Article in Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology, 2024. 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
–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

1 citing paper in PubMed.

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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.

Fan ZhangRadiology department, Huaihe Hospital of Henan University, Kaifeng, 475004, China.ORCID http://orcid.org/0000-0003-2176-3835
Boyan ZhangSchool of Software, Henan University, Kaifeng, 475004, China.ORCID http://orcid.org/0000-0001-5170-281X
Siyuan GuoSchool of Computer and Information Engineering, Henan University, Kaifeng, 475004, China.ORCID http://orcid.org/0000-0002-8223-119X
Xinhong ZhangSchool of Software, Henan University, Kaifeng, 475004, China. zxh@henu.edu.cn.ORCID http://orcid.org/0000-0003-2403-4777

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAutomatic prediction of seizures is a major goal in the field of epilepsy. However, the high variability of Electroencephalogram (EEG) signals in different patients limits the use of prediction models in clinical applications.

methodsThis paper proposes a patient-independent seizure prediction model, named MFCC-CNN, to improve the generalization ability. MFCC-CNN model introduces Mel-Frequency Cepstrum Coefficients (MFCC) features and Linear Predictive Cepstral Coefficients (LPCC) features concentrated in the low frequency region, which contains more detailed information. Convolutional neural network (CNN) is used to construct a seizure prediction model.

resultsExperimental results showed that the proposed model obtained accuracy of 96

conclusionMFCC-CNN model does not need to be specifically customized for different patients. As a patient-independent seizure prediction model, it has good generalization ability.

Indexed as

ElectroencephalographyNeural Networks, ComputerSeizuresHumansSignal Processing, Computer-AssistedConvolutional neural networkElectroencephalogramMeir frequency cepstrum coefficientSeizure prediction

Identifiers

PMID39120777

What OpenQuestion holds

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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.