Epileptic Seizure Detection using Bidimensional Empirical Mode Decomposition and Distance Metric Learning on Scalogram

Sheoran, Poonam; Rathee, Neeru; Saini, J.S.

doi:10.1109/spin48934.2020.9070962

Cited by 5 publications

(1 citation statement)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A Random Forest classifier was used in the study, and a success rate of 94.1% was achieved. Sheoran et al [11] obtained scalogram images by transitioning EEG signals from the time domain to frequency domain with CWT (Continuous Wavelet Transform). Feature extraction was performed by calculating the potential feature values of the instantaneous frequency components, LBP (Local Binary Patterns), and HOG (Oriented Gradient Histograms) from the obtained scalogram images.…”

Section: Introductionmentioning

confidence: 99%

A new dataset for EEG abnormality detection MTOUH

Taşçı

Doğan

et al. 2022

Turkish Journal of Science and Technology

View full text Add to dashboard Cite

Elektroensefalogram (EEG), beyindeki elektriksel aktivitenin izlenmesi için yaygın olarak kullanılmaktadır. EEG sinyallerinin hekimler tarafından incelenmesi yorucu ve zaman alıcıdır. Bu nedenle, algılama doğruluğunu artırmak için makine öğrenme teknikleri kullanılabilir. Bu çalışmada 35 kanal, 10575x15 saniye normal ve 11240x15 saniye anormal EEG sinyalinden oluşan 2 sınıflı veri seti oluşturulmuştur. Bu very seti Turgut Özal Üniversitesi Malatya Eğitim Araştırma Hastanesi’ ne 2021 yılında başvuran hastaların EEG sinyalleri incelenerek elde edilmiştir. Çalışmada istatistiksel özellik çıkarımı tabanlı bir model önerilmiştir. Önerilen modele komşu bileşen analizi kullanılarak öznitelik vektörü indirgemesi yapıldıktan sonra destek vektör makineleri kullanılarak sınıflandırma yapılmıştır. 35 kanaldan en yüksek doğruluk P4O2 kanalında elde edilmiştir. P4O2 kanalı için doğruluk, duyarlılık, özgüllük, kesinlik ve f-skoru sırasıyla %81.3,%78.9, %83.7, %82.0 ve %80.4 olarak elde edilmiştir.

show abstract

Section: Introductionmentioning

confidence: 99%

A new dataset for EEG abnormality detection MTOUH

Taşçı

Doğan

et al. 2022

Turkish Journal of Science and Technology

View full text Add to dashboard Cite

show abstract

Multiple classification of EEG signals and epileptic seizure diagnosis with combined deep learning

Varli

Yılmaz

2023

Journal of Computational Science

View full text Add to dashboard Cite

Bispectrum-Based Channel Selection for Motor Imagery Based Brain-Computer Interfacing

Jin

Chang

Daly

et al. 2020

IEEE Trans. Neural Syst. Rehabil. Eng.

108

View full text Add to dashboard Cite

The performance of motor imagery (MI) based Brain-computer interfacing (BCI) is easily affected by noise and redundant information that exists in the multi-channel electroencephalogram (EEG). To solve this problem, many temporal and spatial feature based channel selection methods have been proposed. However, temporal and spatial features do not accurately reflect changes in the power of the oscillatory EEG. Thus, spectral features of MI-related EEG signals may be useful for channel selection. Bispectrum analysis is a technique developed for extracting non-linear and non-Gaussian information from non-linear and non-Gaussian signals. The features extracted from bispectrum analysis can provide frequency domain information about the EEG. Therefore, in this study, we propose a bispectrumbased channel selection (BCS) method for MI-based BCI. The proposed method uses the sum of logarithmic amplitudes (SLA) and the first order spectral moment (FOSM) features extracted from bispectrum analysis to select EEG channels without redundant information. Three public BCI competition datasets (BCI competition IV dataset 1, BCI competition III dataset IVa and BCI competition III dataset IIIa) were used to validate the effectiveness of our proposed method. The results indicate that our BCS method outperforms use of all channels (83.8% vs 69.4%, 86.3% vs 82.9% and 77.8% vs 68.2%, respectively). Furthermore, compared to the other state-of-the-art methods, our BCS method also

show abstract

Epileptic Seizure Detection using Bidimensional Empirical Mode Decomposition and Distance Metric Learning on Scalogram

Cited by 5 publications

References 37 publications

A new dataset for EEG abnormality detection MTOUH

A new dataset for EEG abnormality detection MTOUH

Multiple classification of EEG signals and epileptic seizure diagnosis with combined deep learning

Bispectrum-Based Channel Selection for Motor Imagery Based Brain-Computer Interfacing

Contact Info

Product

Resources

About