Araştırma Makalesi
BibTex RIS Kaynak Göster

A new dataset for EEG abnormality detection MTOUH

Yıl 2022, Cilt: 17 Sayı: 1, 135 - 141, 20.03.2022

İrem Taşcı Burak Tasci Sengul Dogan Türker Tuncer

https://doi.org/10.55525/tjst.1074540
Cited By: 3

Öz

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.

Anahtar Kelimeler

Electroencephalography signals classification SVM Machine Learning

Kaynakça

  • [1] A. Sibel KOCAASLAN, B. Onur, and K. Mehmet Cemal, "Elektroensefalografinin Biyofiziksel Temelleri," Turkiye Klinikleri J Neurol, vol. 10(2), pp. 110-114, 2017.
  • [2] A. Biasiucci, B. Franceschiello, and M. M. Murray, "Electroencephalography," Current Biology, vol. 29, no. 3, pp. R80-R85, 2019.
  • [3] D. Millett, "Hans Berger: From psychic energy to the EEG," Perspectives in biology and medicine, vol. 44, no. 4, pp. 522-542, 2001.
  • [4] A. Galip and T. Sabiha, "Elektroensefalografinin Tarihçesi," Turkiye Klinikleri J Neurol, vol. 10(2), pp. 105-109, 2017.
  • [5] K. Süleyman and Ş. Nihat, "Rutin Elektroensefalografi Kayıtlaması ve Aktivasyon Yöntemleri," Turkiye Klinikleri J Neurol, vol. 10(2), pp. 115-119, 2017.
  • [6] W. Zhao et al., "A novel deep neural network for robust detection of seizures using EEG signals," Computational and mathematical methods in medicine, vol. 2020, 2020.
  • [7] P. Khan, Y. Khan, S. Kumar, M. S. Khan, and A. H. Gandomi, "HVD-LSTM based recognition of epileptic seizures and normal human activity," Computers in Biology and Medicine, vol. 136, p. 104684, 2021.
  • [8] Y. Wang et al., "Computer-Aided Intracranial EEG Signal Identification Method Based on a Multi-Branch Deep Learning Fusion Model and Clinical Validation," Brain Sciences, vol. 11, no. 5, p. 615, 2021. [Online]. Available: https://www.mdpi.com/2076-3425/11/5/615.
  • [9] M. Rashid et al., "The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN," PeerJ Computer Science, vol. 7, p. e374, 2021.
  • [10] M. Ravi Kumar and Y. Srinivasa Rao, "Epileptic seizures classification in EEG signal based on semantic features and variational mode decomposition," Cluster Computing, vol. 22, no. 6, pp. 13521-13531, 2019.
  • [11] P. Sheoran, N. Rathee, and J. Saini, "Epileptic seizure detection using bidimensional empirical mode decomposition and distance metric learning on scalogram," in 2020 7th International Conference on Signal Processing and Integrated Networks (SPIN), 2020: IEEE, pp. 675-680.
  • [12] S. Bera, R. Roy, D. Sikdar, A. Kar, R. Mukhopadhyay, and M. Mahadevappal, "A randomised ensemble learning approach for multiclass motor imagery classification using error correcting output coding," in 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018: IEEE, pp. 5081-5084.
  • [13] K.-W. Ha and J.-W. Jeong, "Motor imagery EEG classification using capsule networks," Sensors, vol. 19, no. 13, p. 2854, 2019.
  • [14] J. Goldberger, S. Roweis, G. Hinton, and R. Salakhutdinov, "Neighbourhood components analysis," presented at the Proceedings of the 17th International Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, 2004.
  • [15] L. E. Peterson, "K-nearest neighbor," Scholarpedia, vol. 4, no. 2, p. 1883, 2009.
  • [16] V. Vapnik, "The Support Vector Method of Function Estimation," in Nonlinear Modeling: Advanced Black-Box Techniques, J. A. K. Suykens and J. Vandewalle Eds. Boston, MA: Springer US, 1998, pp. 55-85.
  • [17] V. Vapnik, The nature of statistical learning theory. Springer science & business media, 1999.
  • [18] M. J. Warrens, "On the equivalence of Cohen’s kappa and the Hubert-Arabie adjusted Rand index," Journal of classification, vol. 25, no. 2, pp. 177-183, 2008.
  • [19] D. Chicco and G. Jurman, "The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation," BMC genomics, vol. 21, no. 1, p. 6, 2020.

Yıl 2022, Cilt: 17 Sayı: 1, 135 - 141, 20.03.2022

İrem Taşcı Burak Tasci Sengul Dogan Türker Tuncer

https://doi.org/10.55525/tjst.1074540
Cited By: 3

Öz

Kaynakça

  • [1] A. Sibel KOCAASLAN, B. Onur, and K. Mehmet Cemal, "Elektroensefalografinin Biyofiziksel Temelleri," Turkiye Klinikleri J Neurol, vol. 10(2), pp. 110-114, 2017.
  • [2] A. Biasiucci, B. Franceschiello, and M. M. Murray, "Electroencephalography," Current Biology, vol. 29, no. 3, pp. R80-R85, 2019.
  • [3] D. Millett, "Hans Berger: From psychic energy to the EEG," Perspectives in biology and medicine, vol. 44, no. 4, pp. 522-542, 2001.
  • [4] A. Galip and T. Sabiha, "Elektroensefalografinin Tarihçesi," Turkiye Klinikleri J Neurol, vol. 10(2), pp. 105-109, 2017.
  • [5] K. Süleyman and Ş. Nihat, "Rutin Elektroensefalografi Kayıtlaması ve Aktivasyon Yöntemleri," Turkiye Klinikleri J Neurol, vol. 10(2), pp. 115-119, 2017.
  • [6] W. Zhao et al., "A novel deep neural network for robust detection of seizures using EEG signals," Computational and mathematical methods in medicine, vol. 2020, 2020.
  • [7] P. Khan, Y. Khan, S. Kumar, M. S. Khan, and A. H. Gandomi, "HVD-LSTM based recognition of epileptic seizures and normal human activity," Computers in Biology and Medicine, vol. 136, p. 104684, 2021.
  • [8] Y. Wang et al., "Computer-Aided Intracranial EEG Signal Identification Method Based on a Multi-Branch Deep Learning Fusion Model and Clinical Validation," Brain Sciences, vol. 11, no. 5, p. 615, 2021. [Online]. Available: https://www.mdpi.com/2076-3425/11/5/615.
  • [9] M. Rashid et al., "The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN," PeerJ Computer Science, vol. 7, p. e374, 2021.
  • [10] M. Ravi Kumar and Y. Srinivasa Rao, "Epileptic seizures classification in EEG signal based on semantic features and variational mode decomposition," Cluster Computing, vol. 22, no. 6, pp. 13521-13531, 2019.
  • [11] P. Sheoran, N. Rathee, and J. Saini, "Epileptic seizure detection using bidimensional empirical mode decomposition and distance metric learning on scalogram," in 2020 7th International Conference on Signal Processing and Integrated Networks (SPIN), 2020: IEEE, pp. 675-680.
  • [12] S. Bera, R. Roy, D. Sikdar, A. Kar, R. Mukhopadhyay, and M. Mahadevappal, "A randomised ensemble learning approach for multiclass motor imagery classification using error correcting output coding," in 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018: IEEE, pp. 5081-5084.
  • [13] K.-W. Ha and J.-W. Jeong, "Motor imagery EEG classification using capsule networks," Sensors, vol. 19, no. 13, p. 2854, 2019.
  • [14] J. Goldberger, S. Roweis, G. Hinton, and R. Salakhutdinov, "Neighbourhood components analysis," presented at the Proceedings of the 17th International Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, 2004.
  • [15] L. E. Peterson, "K-nearest neighbor," Scholarpedia, vol. 4, no. 2, p. 1883, 2009.
  • [16] V. Vapnik, "The Support Vector Method of Function Estimation," in Nonlinear Modeling: Advanced Black-Box Techniques, J. A. K. Suykens and J. Vandewalle Eds. Boston, MA: Springer US, 1998, pp. 55-85.
  • [17] V. Vapnik, The nature of statistical learning theory. Springer science & business media, 1999.
  • [18] M. J. Warrens, "On the equivalence of Cohen’s kappa and the Hubert-Arabie adjusted Rand index," Journal of classification, vol. 25, no. 2, pp. 177-183, 2008.
  • [19] D. Chicco and G. Jurman, "The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation," BMC genomics, vol. 21, no. 1, p. 6, 2020.
Toplam 19 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm TJST
Yazarlar

İrem Taşcı Bu kişi benim 0000-0001-7069-769X

Burak Tasci 0000-0002-4490-0946

Sengul Dogan 0000-0001-9677-5684

Türker Tuncer 0000-0002-5126-6445

Yayımlanma Tarihi 20 Mart 2022
Gönderilme Tarihi 16 Şubat 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 17 Sayı: 1

Kaynak Göster

APA Taşcı, İ., Tasci, B., Dogan, S., Tuncer, T. (2022). A new dataset for EEG abnormality detection MTOUH. Turkish Journal of Science and Technology, 17(1), 135-141. https://doi.org/10.55525/tjst.1074540
AMA Taşcı İ, Tasci B, Dogan S, Tuncer T. A new dataset for EEG abnormality detection MTOUH. TJST. Mart 2022;17(1):135-141. doi:10.55525/tjst.1074540
Chicago Taşcı, İrem, Burak Tasci, Sengul Dogan, ve Türker Tuncer. “A New Dataset for EEG Abnormality Detection MTOUH”. Turkish Journal of Science and Technology 17, sy. 1 (Mart 2022): 135-41. https://doi.org/10.55525/tjst.1074540.
EndNote Taşcı İ, Tasci B, Dogan S, Tuncer T (01 Mart 2022) A new dataset for EEG abnormality detection MTOUH. Turkish Journal of Science and Technology 17 1 135–141.
IEEE İ. Taşcı, B. Tasci, S. Dogan, ve T. Tuncer, “A new dataset for EEG abnormality detection MTOUH”, TJST, c. 17, sy. 1, ss. 135–141, 2022, doi: 10.55525/tjst.1074540.
ISNAD Taşcı, İrem vd. “A New Dataset for EEG Abnormality Detection MTOUH”. Turkish Journal of Science and Technology 17/1 (Mart 2022), 135-141. https://doi.org/10.55525/tjst.1074540.
JAMA Taşcı İ, Tasci B, Dogan S, Tuncer T. A new dataset for EEG abnormality detection MTOUH. TJST. 2022;17:135–141.
MLA Taşcı, İrem vd. “A New Dataset for EEG Abnormality Detection MTOUH”. Turkish Journal of Science and Technology, c. 17, sy. 1, 2022, ss. 135-41, doi:10.55525/tjst.1074540.
Vancouver Taşcı İ, Tasci B, Dogan S, Tuncer T. A new dataset for EEG abnormality detection MTOUH. TJST. 2022;17(1):135-41.

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