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Quantitative analysis of EEG slow wave activity based on MinPeakProminence method

Traitement Du Signal · Haziran 2021

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YÖKSİS Kayıtları
Quantitative Analysis of EEG Slow Wave Activity Based on MinPeakProminence Method
Traitement du Signal · 2021 SCI-Expanded
Prof. Dr. HASAN ERDİNÇ KOÇER →
YÖKSİS ISSN Eşleşmesi

Bu dergide (ISSN eşleşmesi) kurumun 8 kaydı bulundu.

YÖKSİS Kayıtları — ISSN Eşleşmesi
GKP Signal Processing Using Deep CNN and SVM for Tongue-Machine Interface
2019 ISSN: 0765-0019 SCI-Expanded
Prof. Dr. MUHAMMET SERDAR BAŞÇIL →
Classification of Medical Thermograms Belonging Neonates by Using Segmentation, Feature Engineering and Machine Learning Algorithms
2020 ISSN: 0765-0019 SCI-Expanded
Doç. Dr. MURAT KONAK →
Comparison of the Effects of Mel Coefficients and Spectrogram Images via Deep Learning in Emotion Classification
2020 ISSN: 0765-0019 SCI-Expanded Q3
Prof. Dr. HUMAR KAHRAMANLI ÖRNEK →
Quantitative Analysis of EEG Slow Wave Activity Based on MinPeakProminence Method
2021 ISSN: 0765-0019 SCI-Expanded
Öğr. Gör. SEMA YILDIRIM →
Quantitative Analysis of EEG Slow Wave Activity Based on MinPeakProminence Method
2021 ISSN: 0765-0019 SCI-Expanded Q2
Prof. Dr. HASAN ERDİNÇ KOÇER →
Using a Deep Learning System That Classifies Hypertensive Retinopathy Based on the Fundus Images of Patients of Wide Age
2021 ISSN: 0765-0019 SCI-Expanded Q2
Prof. Dr. ŞAKİR TAŞDEMİR →
Diagnosing Epilepsy from EEG Using Machine Learning and Welch Spectral Analysis
2024 ISSN: 0765-0019 SCI-Expanded Q3
Prof. Dr. HUMAR KAHRAMANLI ÖRNEK →
Prediction of Epileptic Seizures Using Deep Learning: A Brief Review of Current Methods and Emerging Trends
2024 ISSN: 0765-0019 SCI-Expanded
Prof. Dr. HUMAR KAHRAMANLI ÖRNEK →

Makale Bilgileri

Dergi Traitement Du Signal
ISSN07650019
Yayın TarihiHaziran 2021
Cilt / Sayfa38 · 757-773
DOI10.18280/ts.380323
Scopus ID2-s2.0-85111776693
Erişim🔓 Açık Erişim
Özet Persistent, unchanging, and non-reactive focal or generalized abnormal Slow Wave (SW) activities in an awake adult patient are examined pathologically. Although these waves in Electroencephalogram (EEG) are much less prominent than transient activities in some areas, it is not possible to understand them easily by looking at the EEG. For this reason, reliable computer programs that can sort out Slow Waves (SWs) correctly are needed. In this study, a new method based on MinPeakProminence that can detect abnormal SW activities was developed. To test the performance of the study, the data collected from Selcuk University Hospital (22 subjects - epilepsy and various neurological diseases) and Bonn Hospital (only normal A dataset) were used. Various statistical performance measurement methods were used to search the results. The results of this analysis revealed that the classification success, sensitivity and specificity values obtained with the SUH dataset were 96.5%, 93.3% and 96.1%, respectively. In the results of the experiments made with the Bonn dataset, 100% classification success was achieved. Besides, according to the analyses, it was found that SWs are frequently seen in the posterior regions of the brain, especially in the parietal and occipital regions in the SUH dataset.

Yazarlar (3)

1
Sema Yildirim
2
Hasan Erdinc Kocer
3
Ahmet Hakan Ekmekci

Anahtar Kelimeler

Electroencephalogram Epilepsy Minpeakprominence Neurologic disorder Peak Slow wave

Kurumlar

Konya Technical University
Konya Turkey
Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Traitement du Signal (discontinued)
-
H-Index31
YayıncıInternational Information and Engineering Technology Association
ÜlkeFrance
Electrical and Electronic Engineering
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Metrikler

2
Atıf
3
Yazar
6
Anahtar Kelime

Sistemimizdeki Yazarlar

SY
SEMA YILDIRIM
Öğr. Gör.

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