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EEG Sinyallerini Kullanarak Şizofreninin Ayırıcı Tanısı için Derin Konvolüsyonel Sinir Ağı Modeli

Yıl 2024, Cilt: 6 Sayı: 3, 257 - 265, 14.10.2024
https://doi.org/10.52827/hititmedj.1440548

Öz

Amaç: İnsanların gerçekliği anormal bir durumda yorumladığı ciddi zihinsel bozukluklardan biri de şizofrenidir. Şizofreni nedeniyle aşırı derecede düzensiz düşünce, sanrı ve halüsinasyonların birleşimi ortaya çıkmakta ve bu hastalık nedeniyle kişinin günlük işlevleri ciddi şekilde bozulmaktadır. Genel bilişsel aktivite analizi için elektroensefalografi sinyalleri, düşük çözünürlüklü bir teşhis aracı olarak yaygın olarak kullanılmaktadır. Bu çalışmaya şizofreni tanısı almış 73 hasta ile sağlıklı grubuna ait 67 hastanın EEG’si dahil edilerek transfer öğrenme metodu ile şizofreni teşhisi gerçekleştirmek amaçlanmıştır.
Gereç ve Yöntem: Çalışmanın ilk adımında sayısal elektroensefalografi sinyal verilerini kullanılabilir hale getirmek amacıyla spektogramlara dönüştürme işlemi gerçekleştirilmiştir. Sınıflandırma aşamasında FastAI ile Convolutional Neural Network (CNN) tabanlı derin öğrenme modelleri olan ResNet18, ResNet50 ve EfficientNet modelleri kullanılmıştır.
Bulgular: Elektroensefalografi verilerinin karmaşıklığına rağmen çalışmada CNN tabanlı modeller, nörofizyolojik aktivitenin farklı yönlerini yakalamada başarılı olmuştur. En iyi performans %97 doğruluk oranı ile ResNet-50 modelinden elde edilmiştir. Sonrasında sırasıyla %95 ResNet-18 ve %83 EfficientNet modelleri ile sınıflandırma işlemi sonuçlandırılmıştır.
Sonuç: Uygulamada ulaşılan sonucun sınıflandırma performansının umut verici olduğu ve bundan sonraki yapılacak çalışmalar için yol gösterici nitelikte olabileceği düşünülmektedir.

Etik Beyan

Evrak Tarih ve Sayısı: 17.10.2022-207987 T.C. ERZİNCAN BİNALİ YILDIRIM ÜNİVERSİTESİ REKTÖRLÜĞÜ Klinik Araştırmaları Etik Kurulu Sayı :E-26447783-050.01.04-207987 Konu :Dr. Filiz DEMİRDÖĞEN Klinik Araştırmalar Etik Kurul Kararı(2022-03) Sayın; Dr. Öğr. Üyesi Filiz DEMİRDÖĞEN 17.10.2022 Üniversitemiz Etik Kurul Başkanlığının 13.10.2022 tarih ve 03 sayılı oturumunda alınan 03/9 sayılı kararı aşağıya çıkarılmıştır. Bilgilerini ve gereğini rica ederim. KARAR: 03/9 Dr. Öğr. Üyesi Filiz DEMİRDÖĞEN'e ait "Şizofren Hastalarında Ayırıcı Tanı için Çekilen EEG' nin İlaç Direnci, Klinik ve Demografik Özelliklerin Yapay Zeka ile İncelenmesi" konulu Bilimsel Araştırma Projesi çalışmasının görüşülmesi, Yapılan görüşmelerden sonra; adı geçen öğretim üyesinin değerlendirilmek üzere Etik Kurula sunduğu bilimsel çalışmasının; Bilimsel Araştırma ve Yayın Etiği ile ilgili mevzuat hükümleri bakımından uygun olduğuna mevcut oy çokluğuyla karar verilmiştir. Dr.Öğr.Üyesi Ertuğrul ERHAN Klinik Araştırmaları Etik Kurulu Başkanı

Destekleyen Kurum

ERİNCAN BİNALİ YILDIRIM ÜNİVERSİTESİ

Kaynakça

  • Van Os J, Kapur S. Schizophrenia. Lancet. 2009;374:635-645.
  • American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), Arlington: American Psychiatric Publishers 2013:1-947
  • Andreas S, Theisen P, Mestel R, Koch U, Schulz H. Validity of routine clinical DSM-IV diagnoses (Axis I/II) in inpatients with mental disorders. Psychiatry Res. 2009;170:252-255.
  • Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014;511:421-427.
  • Sarpal DK, Lencz T, Malhotra AK. In Support of Neuroimaging Biomarkers of Treatment Response in First-Episode Schizophrenia. Am J Psychiatry. 2016;173:732-733.
  • Gong Q, Lui S, Sweeney JA. A Selective Review of Cerebral Abnormalities in Patients With First-Episode Schizophrenia Before and After Treatment. Am J Psychiatry. 2016;173:232-243.
  • Shepherd AM, Laurens KR, Matheson SL, Carr VJ, Green MJ. Systematic meta-review and quality assessment of the structural brain alterations in schizophrenia. Neurosci Biobehav Rev. 2012;36:1342-1356.
  • Bleich CM, Jamshy S, Sharon H, et al. Machine learning fMRI classifier delineates subgroups of schizophrenia patients. Schizophr Res. 2014;160:196-200.
  • Uhlhaas PJ, Singer W. Abnormal neural oscillations and synchrony in schizophrenia. Nat Rev Neurosci. 2010;11:100-113.
  • Sand T, Bjørk MH, Vaaler AE. Is EEG a useful test in adult psychiatry?. Tidsskr Nor Laegeforen. 2013;133:1200-1204.
  • Boutros NN, Arfken C, Galderisi S, Warrick J, Pratt G, Iacono W. The status of spectral EEG abnormality as a diagnostic test for schizophrenia. Schizophr Res. 2008;99:225-237.
  • Niedermeyer E, Schomer DL, da Silva FHL. Niedermeyer's Electroencephalography: Basic Principles, Clinical Applications, and Related Fields: Wolters Kluwer/Lippincott Williams & Wilkins Health. 4th ed., 2011.
  • Abrams R, Taylor MA. Differential EEG patterns in affective disorder and schizophrenia. Arch Gen Psychiatry. 1979;36:1355-1358.
  • Ergüzen A, Haltaş K, Erdal E, Lüy M. Yardımcı Sistem Olarak BCI ve EEG Sinyallerinin BCI Sistemlerde Kullanım Şekilleri. International Journal of Engineering Research and Development. 2018; 10: 72-79.
  • Shahid KA, Ahmad Z, Ahmad F. A Spectrogram Image-Based Network Anomaly Detection System Using Deep Convolutional Neural Network. IEEE Access.2021; 1-1.
  • Kandel I, Castelli M. Transfer Learning with Convolutional Neural Networks for Diabetic Retinopathy Image Classification. A Review. Applied Sciences. 2020; 10:2021.
  • Al-jumaili S, Duru A, Ucan O. Covid-19 Ultrasound image classification using SVM based on kernels deduced from Convolutional neural network.2021; 429-433.
  • Shamila Ebenezer A, Deepa Kanmani S, Sivakumar M, Jeba Priya S. Effect of image transformation on EfficientNet model for COVID-19 CT image classification. Mater Today Proc. 2022;51:2512-2519.
  • Atila U, Ucar M, Akyol K, Uçar E. Plant leaf disease classification using EfficientNet deep learning model. Ecological Informatic. 2021; 61: 101182.
  • Chakraborty A, Kumer D, Deeba KV. Plant Leaf Disease Recognition Using Fastai Image Classification. 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), 2021; 1624-1630.
  • Aslan Z, Akin M. A deep learning approach in automated detection of schizophrenia using scalogram images of EEG signals. Phys Eng Sci Med. 2022;45:83-96.
  • Shalbaf A, Bagherzadeh S, Maghsoudi A. Transfer learning with deep convolutional neural network for automated detection of schizophrenia from EEG signals. Phys Eng Sci Med. 2020;43:1229-1239.
  • Khare SK, Bajaj V. A hybrid decision support system for automatic detection of Schizophrenia using EEG signals. Comput Biol Med. 2022;141:105028.
  • Mahato S, Kumari Pathak L, Kumari K Detection of Schizophrenia Using EEG Signals. 2021; In pp. 359-390.
  • Zülfikar A, Mehmet A. Empirical mode decomposition and convolutional neural network-based approach for diagnosing psychotic disorders from eeg signals. Applied Intelligence. 2022; 52: 12103-12115.
  • WeiKoh J, Rajinikanth V, Vicnesh J. Application of local configuration pattern for automated detection of schizophrenia with electroencephalogram signals. 2022, Expert Systems. 12957.
  • Das K, Pachori RB. Schizophrenia detection technique using multivariate iterative filtering and multichannel EEG signals. Biomedical Signal Processing and Control. 2021; 67: 102525.
  • Bagherzadeh, S., & Shalbaf, A. EEG-based schizophrenia detection using fusion of effective connectivity maps and convolutional neural networks with transfer learning. 2024; Cognitive Neurodynamics, 1-12.

Deep Convolutional Neural Network Model for the Differential Diagnosis of Schizophrenia Using EEG Signals

Yıl 2024, Cilt: 6 Sayı: 3, 257 - 265, 14.10.2024
https://doi.org/10.52827/hititmedj.1440548

Öz

Objective: One of the serious mental disorders in which people interpret reality in an abnormal situation is schizophrenia. A combination of extremely disordered thoughts, delusions, and hallucinations occurs due to schizophrenia, and the person's daily functions are seriously impaired due to this disease. For general cognitive activity analysis, electroencephalography signals are widely used as a low-resolution diagnostic tool. This study aimed to diagnose schizophrenia using the transfer learning method by including the EEGs of 73 patients diagnosed with schizophrenia, and 67 patients from the healthy group.
Material and Method: In the first step of the study, digital electroencephalography signal data was converted into spectrograms to make them usable. In the classification phase, ResNet18, ResNet50 and EfficientNet models, which are FastAI, and Convolutional Neural Network (CNN) based deep learning models, were used.
Results: Despite the complexity of electroencephalography data, CNN-based models in the study were successful in capturing different aspects of neurophysiological activity. The best performance was obtained from the ResNet-50 model with an accuracy rate of 97%. Afterwards, the classification process was finalized with 95% ResNet-18, and 83% EfficientNet models, respectively.
Conclusion: It is thought that the classification performance of the result obtained in the application is promising, and may be a guide for future studies.

Kaynakça

  • Van Os J, Kapur S. Schizophrenia. Lancet. 2009;374:635-645.
  • American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), Arlington: American Psychiatric Publishers 2013:1-947
  • Andreas S, Theisen P, Mestel R, Koch U, Schulz H. Validity of routine clinical DSM-IV diagnoses (Axis I/II) in inpatients with mental disorders. Psychiatry Res. 2009;170:252-255.
  • Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014;511:421-427.
  • Sarpal DK, Lencz T, Malhotra AK. In Support of Neuroimaging Biomarkers of Treatment Response in First-Episode Schizophrenia. Am J Psychiatry. 2016;173:732-733.
  • Gong Q, Lui S, Sweeney JA. A Selective Review of Cerebral Abnormalities in Patients With First-Episode Schizophrenia Before and After Treatment. Am J Psychiatry. 2016;173:232-243.
  • Shepherd AM, Laurens KR, Matheson SL, Carr VJ, Green MJ. Systematic meta-review and quality assessment of the structural brain alterations in schizophrenia. Neurosci Biobehav Rev. 2012;36:1342-1356.
  • Bleich CM, Jamshy S, Sharon H, et al. Machine learning fMRI classifier delineates subgroups of schizophrenia patients. Schizophr Res. 2014;160:196-200.
  • Uhlhaas PJ, Singer W. Abnormal neural oscillations and synchrony in schizophrenia. Nat Rev Neurosci. 2010;11:100-113.
  • Sand T, Bjørk MH, Vaaler AE. Is EEG a useful test in adult psychiatry?. Tidsskr Nor Laegeforen. 2013;133:1200-1204.
  • Boutros NN, Arfken C, Galderisi S, Warrick J, Pratt G, Iacono W. The status of spectral EEG abnormality as a diagnostic test for schizophrenia. Schizophr Res. 2008;99:225-237.
  • Niedermeyer E, Schomer DL, da Silva FHL. Niedermeyer's Electroencephalography: Basic Principles, Clinical Applications, and Related Fields: Wolters Kluwer/Lippincott Williams & Wilkins Health. 4th ed., 2011.
  • Abrams R, Taylor MA. Differential EEG patterns in affective disorder and schizophrenia. Arch Gen Psychiatry. 1979;36:1355-1358.
  • Ergüzen A, Haltaş K, Erdal E, Lüy M. Yardımcı Sistem Olarak BCI ve EEG Sinyallerinin BCI Sistemlerde Kullanım Şekilleri. International Journal of Engineering Research and Development. 2018; 10: 72-79.
  • Shahid KA, Ahmad Z, Ahmad F. A Spectrogram Image-Based Network Anomaly Detection System Using Deep Convolutional Neural Network. IEEE Access.2021; 1-1.
  • Kandel I, Castelli M. Transfer Learning with Convolutional Neural Networks for Diabetic Retinopathy Image Classification. A Review. Applied Sciences. 2020; 10:2021.
  • Al-jumaili S, Duru A, Ucan O. Covid-19 Ultrasound image classification using SVM based on kernels deduced from Convolutional neural network.2021; 429-433.
  • Shamila Ebenezer A, Deepa Kanmani S, Sivakumar M, Jeba Priya S. Effect of image transformation on EfficientNet model for COVID-19 CT image classification. Mater Today Proc. 2022;51:2512-2519.
  • Atila U, Ucar M, Akyol K, Uçar E. Plant leaf disease classification using EfficientNet deep learning model. Ecological Informatic. 2021; 61: 101182.
  • Chakraborty A, Kumer D, Deeba KV. Plant Leaf Disease Recognition Using Fastai Image Classification. 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), 2021; 1624-1630.
  • Aslan Z, Akin M. A deep learning approach in automated detection of schizophrenia using scalogram images of EEG signals. Phys Eng Sci Med. 2022;45:83-96.
  • Shalbaf A, Bagherzadeh S, Maghsoudi A. Transfer learning with deep convolutional neural network for automated detection of schizophrenia from EEG signals. Phys Eng Sci Med. 2020;43:1229-1239.
  • Khare SK, Bajaj V. A hybrid decision support system for automatic detection of Schizophrenia using EEG signals. Comput Biol Med. 2022;141:105028.
  • Mahato S, Kumari Pathak L, Kumari K Detection of Schizophrenia Using EEG Signals. 2021; In pp. 359-390.
  • Zülfikar A, Mehmet A. Empirical mode decomposition and convolutional neural network-based approach for diagnosing psychotic disorders from eeg signals. Applied Intelligence. 2022; 52: 12103-12115.
  • WeiKoh J, Rajinikanth V, Vicnesh J. Application of local configuration pattern for automated detection of schizophrenia with electroencephalogram signals. 2022, Expert Systems. 12957.
  • Das K, Pachori RB. Schizophrenia detection technique using multivariate iterative filtering and multichannel EEG signals. Biomedical Signal Processing and Control. 2021; 67: 102525.
  • Bagherzadeh, S., & Shalbaf, A. EEG-based schizophrenia detection using fusion of effective connectivity maps and convolutional neural networks with transfer learning. 2024; Cognitive Neurodynamics, 1-12.
Toplam 28 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Anesteziyoloji
Bölüm Araştırma Makaleleri
Yazarlar

Filiz Demirdöğen 0000-0003-2973-916X

Çağla Danacı 0000-0003-2414-1310

Seda Arslan Tuncer 0000-0001-6472-8306

Mustafa Akkuş 0000-0002-5674-6632

Sevler Yıldız 0000-0002-9951-9093

Yayımlanma Tarihi 14 Ekim 2024
Gönderilme Tarihi 20 Şubat 2024
Kabul Tarihi 22 Ağustos 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 6 Sayı: 3

Kaynak Göster

AMA Demirdöğen F, Danacı Ç, Arslan Tuncer S, Akkuş M, Yıldız S. Deep Convolutional Neural Network Model for the Differential Diagnosis of Schizophrenia Using EEG Signals. Hitit Medical Journal. Ekim 2024;6(3):257-265. doi:10.52827/hititmedj.1440548