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Convmixer ve SDD Kullanılarak DEHB Hastalığının EEG Sinyalleri ile Otomatik Olarak Tespit Edilmesi

Yıl 2024, , 19 - 25, 26.03.2024
https://doi.org/10.46810/tdfd.1388893

Öz

DEHB, çocuklarda dikkat eksikliği, davranış problemleri, eğitimle ilgili sorunlar ve düşük özgüven gibi problemler oluşturabilir. Bu çalışma, Dikkat Eksikliği Hiperaktivite Bozukluğu (DEHB) teşhisini elektroensefalografi (EEG) sinyalleriyle değerlendirmeyi hedefleyen bir araştırmayı özetlemektedir. Araştırma, 30 DEHB tanısı almış çocuk ve 30 sağlıklı kontrol grubunun EEG verilerini kullanmıştır. EEG verileri öncelikle gürültü azaltma amacıyla işlenmiş ve ardından ConvMixer, ResNet50 ve ResNet18 gibi derin öğrenme modelleri kullanılarak sınıflandırılmıştır. Bulgular, ConvMixer'in düşük hesaplama kaynaklarına ihtiyaç duyarak yüksek sınıflandırma başarısı elde ettiğini göstermektedir. Ayrıca, EEG sinyallerinin DEHB teşhisinde kullanılabilirliği konusunda farklı kanalların etkileri incelenmiş ve T8 kanalının özellikle etkili olduğu tespit edilmiştir. Bu çalışma, EEG tabanlı DEHB teşhisi için daha hafif modellerin kullanılabilirliğini ve EEG kanallarının önemini vurgulamaktadır.

Kaynakça

  • Willcutt, E. G. . The prevalence of DSM-IV attention-deficit/hyperactivity disorder: a meta-analytic review. Neurotherapeutics, 2012; 9(3), 490-499.
  • Tosun, M. Effects of spectral features of EEG signals recorded with different channels and recording statuses on ADHD classification with deep learning. Physical and Engineering Sciences in Medicine, 2021 44(3), 693-702.
  • Lee, W., Lee, D., Lee, S., Jun, K., & Kim, M. S. . Deep-Learning-Based ADHD Classification Using Children’s Skeleton Data Acquired through the ADHD Screening Game. Sensors, 2022; 23(1), 246.
  • Wang, D., Hong, D., & Wu, Q.. Attention deficit hyperactivity disorder classification based on deep learning. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2022; 20(2), 1581-1586.
  • Chen, H., Song, Y., & Li, X. . Use of deep learning to detect personalized spatial-frequency abnormalities in EEGs of children with ADHD. Journal of neural engineering, 2019; 16(6), 066046.
  • Lee, W., Lee, S., Lee, D., Jun, K., Ahn, D. H., & Kim, M. S. . Deep Learning-Based ADHD and ADHD-RISK Classification Technology through the Recognition of Children’s Abnormal Behaviors during the Robot-Led ADHD Screening Game. Sensors, 2023; 23(1), 278.
  • Saurabh, S., & Gupta, P. K.. Deep Learning-Based Modified Bidirectional LSTM Network for Classification of ADHD Disorder. Arabian Journal for Science and Engineering, 2023; 1-18.
  • Tang, Y., Sun, J., Wang, C., Zhong, Y., Jiang, A., Liu, G., & Liu, X. . ADHD classification using auto-encoding neural network and binary hypothesis testing. Artificial Intelligence in Medicine, 2022; 123.
  • Ahmadi, A., Kashefi, M., Shahrokhi, H., & Nazari, M. A. Computer aided diagnosis system using deep convolutional neural networks for ADHD subtypes. Biomedical Signal Processing and Control, 2021; 63, 102227.
  • Ali Motie Nasrabadi, Armin Allahverdy, Mehdi Samavati, Mohammad Reza Mohammadi, June 10, 2020, "EEG data for ADHD / Control children", IEEE Dataport, doi: https://dx.doi.org/10.21227/rzfh-zn36.
  • Maniruzzaman, M., Hasan, M. A. M., Asai, N., & Shin, J. . Optimal Channels and Features Selection Based ADHD Detection From EEG Signal Using Statistical and Machine Learning Techniques. IEEE Access, 2023;11, 33570-33583.
  • Park, C., Rouzi, M. D., Atique, M. M. U., Finco, M. G., Mishra, R. K., Barba-Villalobos, G., ... & Najafi, B. Machine Learning-Based Aggression Detection in Children with ADHD Using Sensor-Based Physical Activity Monitoring. Sensors, 2023; 23(10), 4949.
  • Ghasemi, E., Ebrahimi, M., & Ebrahimie, E. Machine learning models effectively distinguish attention-deficit/hyperactivity disorder using event-related potentials. Cognitive Neurodynamics, 2022; 16(6), 1335-1349.
  • Mikolas, P., Vahid, A., Bernardoni, F., Süß, M., Martini, J., Beste, C., & Bluschke, A. Training a machine learning classifier to identify ADHD based on real-world clinical data from medical records. Scientific Reports, 2022; 12(1), 12934.
  • Rioul, O., & Duhamel, P. Fast algorithms for discrete and continuous wavelet transforms. IEEE transactions on information theory, 1992; 38(2), 569-586.
  • Uyanık, H., Ozcelik, S. T. A., Duranay, Z. B., Sengur, A., & Acharya, U. R. Use of differential entropy for automated emotion recognition in a virtual reality environment with EEG signals. Diagnostics, 2022; 12(10), 2508.
  • Trockman, A., & Kolter, J. Z. Patches are all you need?. arXiv preprint arXiv:2201.09792; 2022.
  • Tolstikhin, I. O., Houlsby, N., Kolesnikov, A., Beyer, L., Zhai, X., Unterthiner, T., ... & Dosovitskiy, A. Mlp-mixer: An all-mlp architecture for vision. Advances in neural information processing systems, 2021; 34, 24261-24272.
  • He, K., Zhang, X., Ren, S., & Sun, J. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition 2016; (pp. 770-778).

Advanced EEG-Based Analysis for ADHD Identification Utilizing Convmixer and Continuous Wavelet Transform

Yıl 2024, , 19 - 25, 26.03.2024
https://doi.org/10.46810/tdfd.1388893

Öz

Children with ADHD may experience challenges such as attention deficits, behavioral problems, educational problems, and low self-confidence. This study summarizes research aiming to evaluate the diagnosis of attention deficit hyperactivity disorder (ADHD) with electroencephalography (EEG) signals. The research used EEG data from 30 children diagnosed with ADHD and 30 healthy control groups. EEG data was first processed for noise reduction purposes and then classified using deep learning models such as ConvMixer, ResNet50, and ResNet18. The findings show that ConvMixer demonstrates high accuracy in classification. while requiring low computational resources. Additionally, the effects of different channels on the usability of EEG signals in the diagnosis of ADHD were examined, and the T8 channel was found to be particularly effective. In conclusion, the study emphasizes the effectiveness of lightweight models and underscores the significance of specific EEG channels in diagnosing ADHD using EEG signals.

Kaynakça

  • Willcutt, E. G. . The prevalence of DSM-IV attention-deficit/hyperactivity disorder: a meta-analytic review. Neurotherapeutics, 2012; 9(3), 490-499.
  • Tosun, M. Effects of spectral features of EEG signals recorded with different channels and recording statuses on ADHD classification with deep learning. Physical and Engineering Sciences in Medicine, 2021 44(3), 693-702.
  • Lee, W., Lee, D., Lee, S., Jun, K., & Kim, M. S. . Deep-Learning-Based ADHD Classification Using Children’s Skeleton Data Acquired through the ADHD Screening Game. Sensors, 2022; 23(1), 246.
  • Wang, D., Hong, D., & Wu, Q.. Attention deficit hyperactivity disorder classification based on deep learning. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2022; 20(2), 1581-1586.
  • Chen, H., Song, Y., & Li, X. . Use of deep learning to detect personalized spatial-frequency abnormalities in EEGs of children with ADHD. Journal of neural engineering, 2019; 16(6), 066046.
  • Lee, W., Lee, S., Lee, D., Jun, K., Ahn, D. H., & Kim, M. S. . Deep Learning-Based ADHD and ADHD-RISK Classification Technology through the Recognition of Children’s Abnormal Behaviors during the Robot-Led ADHD Screening Game. Sensors, 2023; 23(1), 278.
  • Saurabh, S., & Gupta, P. K.. Deep Learning-Based Modified Bidirectional LSTM Network for Classification of ADHD Disorder. Arabian Journal for Science and Engineering, 2023; 1-18.
  • Tang, Y., Sun, J., Wang, C., Zhong, Y., Jiang, A., Liu, G., & Liu, X. . ADHD classification using auto-encoding neural network and binary hypothesis testing. Artificial Intelligence in Medicine, 2022; 123.
  • Ahmadi, A., Kashefi, M., Shahrokhi, H., & Nazari, M. A. Computer aided diagnosis system using deep convolutional neural networks for ADHD subtypes. Biomedical Signal Processing and Control, 2021; 63, 102227.
  • Ali Motie Nasrabadi, Armin Allahverdy, Mehdi Samavati, Mohammad Reza Mohammadi, June 10, 2020, "EEG data for ADHD / Control children", IEEE Dataport, doi: https://dx.doi.org/10.21227/rzfh-zn36.
  • Maniruzzaman, M., Hasan, M. A. M., Asai, N., & Shin, J. . Optimal Channels and Features Selection Based ADHD Detection From EEG Signal Using Statistical and Machine Learning Techniques. IEEE Access, 2023;11, 33570-33583.
  • Park, C., Rouzi, M. D., Atique, M. M. U., Finco, M. G., Mishra, R. K., Barba-Villalobos, G., ... & Najafi, B. Machine Learning-Based Aggression Detection in Children with ADHD Using Sensor-Based Physical Activity Monitoring. Sensors, 2023; 23(10), 4949.
  • Ghasemi, E., Ebrahimi, M., & Ebrahimie, E. Machine learning models effectively distinguish attention-deficit/hyperactivity disorder using event-related potentials. Cognitive Neurodynamics, 2022; 16(6), 1335-1349.
  • Mikolas, P., Vahid, A., Bernardoni, F., Süß, M., Martini, J., Beste, C., & Bluschke, A. Training a machine learning classifier to identify ADHD based on real-world clinical data from medical records. Scientific Reports, 2022; 12(1), 12934.
  • Rioul, O., & Duhamel, P. Fast algorithms for discrete and continuous wavelet transforms. IEEE transactions on information theory, 1992; 38(2), 569-586.
  • Uyanık, H., Ozcelik, S. T. A., Duranay, Z. B., Sengur, A., & Acharya, U. R. Use of differential entropy for automated emotion recognition in a virtual reality environment with EEG signals. Diagnostics, 2022; 12(10), 2508.
  • Trockman, A., & Kolter, J. Z. Patches are all you need?. arXiv preprint arXiv:2201.09792; 2022.
  • Tolstikhin, I. O., Houlsby, N., Kolesnikov, A., Beyer, L., Zhai, X., Unterthiner, T., ... & Dosovitskiy, A. Mlp-mixer: An all-mlp architecture for vision. Advances in neural information processing systems, 2021; 34, 24261-24272.
  • He, K., Zhang, X., Ren, S., & Sun, J. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition 2016; (pp. 770-778).
Toplam 19 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Biyomedikal Görüntüleme
Bölüm Makaleler
Yazarlar

Buğra Karakaş 0000-0002-8319-7480

Salih Taha Alperen Özçelik 0000-0002-7929-7542

Hakan Uyanık 0000-0002-6870-7569

Hüseyin Üzen 0000-0002-0998-2130

Abdülkadir Şengür 0000-0003-1614-2639

Erken Görünüm Tarihi 26 Mart 2024
Yayımlanma Tarihi 26 Mart 2024
Gönderilme Tarihi 10 Kasım 2023
Kabul Tarihi 7 Ocak 2024
Yayımlandığı Sayı Yıl 2024

Kaynak Göster

APA Karakaş, B., Özçelik, S. T. A., Uyanık, H., Üzen, H., vd. (2024). Convmixer ve SDD Kullanılarak DEHB Hastalığının EEG Sinyalleri ile Otomatik Olarak Tespit Edilmesi. Türk Doğa Ve Fen Dergisi, 13(1), 19-25. https://doi.org/10.46810/tdfd.1388893
AMA Karakaş B, Özçelik STA, Uyanık H, Üzen H, Şengür A. Convmixer ve SDD Kullanılarak DEHB Hastalığının EEG Sinyalleri ile Otomatik Olarak Tespit Edilmesi. TDFD. Mart 2024;13(1):19-25. doi:10.46810/tdfd.1388893
Chicago Karakaş, Buğra, Salih Taha Alperen Özçelik, Hakan Uyanık, Hüseyin Üzen, ve Abdülkadir Şengür. “Convmixer Ve SDD Kullanılarak DEHB Hastalığının EEG Sinyalleri Ile Otomatik Olarak Tespit Edilmesi”. Türk Doğa Ve Fen Dergisi 13, sy. 1 (Mart 2024): 19-25. https://doi.org/10.46810/tdfd.1388893.
EndNote Karakaş B, Özçelik STA, Uyanık H, Üzen H, Şengür A (01 Mart 2024) Convmixer ve SDD Kullanılarak DEHB Hastalığının EEG Sinyalleri ile Otomatik Olarak Tespit Edilmesi. Türk Doğa ve Fen Dergisi 13 1 19–25.
IEEE B. Karakaş, S. T. A. Özçelik, H. Uyanık, H. Üzen, ve A. Şengür, “Convmixer ve SDD Kullanılarak DEHB Hastalığının EEG Sinyalleri ile Otomatik Olarak Tespit Edilmesi”, TDFD, c. 13, sy. 1, ss. 19–25, 2024, doi: 10.46810/tdfd.1388893.
ISNAD Karakaş, Buğra vd. “Convmixer Ve SDD Kullanılarak DEHB Hastalığının EEG Sinyalleri Ile Otomatik Olarak Tespit Edilmesi”. Türk Doğa ve Fen Dergisi 13/1 (Mart 2024), 19-25. https://doi.org/10.46810/tdfd.1388893.
JAMA Karakaş B, Özçelik STA, Uyanık H, Üzen H, Şengür A. Convmixer ve SDD Kullanılarak DEHB Hastalığının EEG Sinyalleri ile Otomatik Olarak Tespit Edilmesi. TDFD. 2024;13:19–25.
MLA Karakaş, Buğra vd. “Convmixer Ve SDD Kullanılarak DEHB Hastalığının EEG Sinyalleri Ile Otomatik Olarak Tespit Edilmesi”. Türk Doğa Ve Fen Dergisi, c. 13, sy. 1, 2024, ss. 19-25, doi:10.46810/tdfd.1388893.
Vancouver Karakaş B, Özçelik STA, Uyanık H, Üzen H, Şengür A. Convmixer ve SDD Kullanılarak DEHB Hastalığının EEG Sinyalleri ile Otomatik Olarak Tespit Edilmesi. TDFD. 2024;13(1):19-25.