Araştırma Makalesi

Vision Transformer Based Classification of Neurological Disorders from Human Speech

Cilt: 3 Sayı: 2 12 Haziran 2024
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Vision Transformer Based Classification of Neurological Disorders from Human Speech

Öz

In this study, we introduce a transformative approach to achieve high-accuracy classification of distinct health categories, including Parkinson's disease, Multiple Sclerosis (MS), healthy individuals, and other categories, utilizing a transformer-based neural network. The cornerstone of this approach lies in the innovative conversion of human speech into spectrograms, which are subsequently transformed into visual images. This transformation process enables our network to capture intricate vocal patterns and subtle nuances that are indicative of various health conditions. The experimental validation of our approach underscores its remarkable performance, achieving exceptional accuracy in differentiating Parkinson's disease, MS, healthy subjects, and other categories. This breakthrough opens doors to potential clinical applications, offering an innovative, non-invasive diagnostic tool that rests on the fusion of spectrogram analysis and transformer-based models.

Anahtar Kelimeler

Etik Beyan

The study protocol for this dataset was approved by the Ondokuz Mayıs University Clinical Research Ethics Committee (2022-545/2023). Written informed consent form was obtained from the address in the working environment and patient contents were extracted to ensure anonymity.

Teşekkür

This study attracted great attention at the 58th National Neurology Congress in Turkey and was awarded the second prize in the oral presentation category.

Kaynakça

  1. B. Karasulu, “Çoklu ortam sistemleri için siber güvenlik kapsamında derin öğrenme kullanarak ses sahne ve olaylarının tespiti,” Acta INFOLOGICA, vol. 3, no. 2, pp. 60–82, 2019.
  2. A. Tursunov, J. Y. Choeh, and S. Kwon, “Age and gender recognition using a convolutional neural network with a specially designed multi-attention module through speech spectrograms,” Sensors, vol. 21, no. 17, p. 5892, 2021.
  3. M. Vacher, J.-F. Serignat, and S. Chaillol, “Sound classification in a smart room environment: an approach using GMM and HMM methods,” in The 4th IEEE Conference on Speech Technology and Human-Computer Dialogue (SpeD 2007), Publishing House of the Romanian Academy (Bucharest), 2007, vol. 1, pp. 135–146.
  4. J. Acharya and A. Basu, “Deep neural network for respiratory sound classification in wearable devices enabled by patient specific model tuning,” IEEE Trans. Biomed. Circuits Syst., vol. 14, no. 3, pp. 535–544, 2020.
  5. G. Woodson, “Management of neurologic disorders of the larynx,” Ann. Otol. Rhinol. \& Laryngol., vol. 117, no. 5, pp. 317–326, 2008.
  6. A. Abushakra and M. Faezipour, “Acoustic signal classification of breathing movements to virtually aid breath regulation,” IEEE J. Biomed. Heal. informatics, vol. 17, no. 2, pp. 493–500, 2013.
  7. E. Soares, P. Angelov, and X. Gu, “Autonomous learning multiple-model zero-order classifier for heart sound classification,” Appl. Soft Comput., vol. 94, p. 106449, 2020.
  8. Z. Dokur and T. Ölmez, “Heart sound classification using wavelet transform and incremental self-organizing map,” Digit. Signal Process., vol. 18, no. 6, pp. 951–959, 2008.

Ayrıntılar

Birincil Dil

İngilizce

Konular

Bilgisayar Yazılımı, Biyomedikal Tanı

Bölüm

Araştırma Makalesi

Yayımlanma Tarihi

12 Haziran 2024

Gönderilme Tarihi

17 Mart 2024

Kabul Tarihi

16 Nisan 2024

Yayımlandığı Sayı

Yıl 2024 Cilt: 3 Sayı: 2

Kaynak Göster

APA
Soylu, E., Gül, S., Aslan, K., Türkoğlu, M., & Terzi, M. (2024). Vision Transformer Based Classification of Neurological Disorders from Human Speech. Firat University Journal of Experimental and Computational Engineering, 3(2), 160-174. https://doi.org/10.62520/fujece.1454309
AMA
1.Soylu E, Gül S, Aslan K, Türkoğlu M, Terzi M. Vision Transformer Based Classification of Neurological Disorders from Human Speech. Firat University Journal of Experimental and Computational Engineering. 2024;3(2):160-174. doi:10.62520/fujece.1454309
Chicago
Soylu, Emel, Sema Gül, Kübra Aslan, Muammer Türkoğlu, ve Murat Terzi. 2024. “Vision Transformer Based Classification of Neurological Disorders from Human Speech”. Firat University Journal of Experimental and Computational Engineering 3 (2): 160-74. https://doi.org/10.62520/fujece.1454309.
EndNote
Soylu E, Gül S, Aslan K, Türkoğlu M, Terzi M (01 Haziran 2024) Vision Transformer Based Classification of Neurological Disorders from Human Speech. Firat University Journal of Experimental and Computational Engineering 3 2 160–174.
IEEE
[1]E. Soylu, S. Gül, K. Aslan, M. Türkoğlu, ve M. Terzi, “Vision Transformer Based Classification of Neurological Disorders from Human Speech”, Firat University Journal of Experimental and Computational Engineering, c. 3, sy 2, ss. 160–174, Haz. 2024, doi: 10.62520/fujece.1454309.
ISNAD
Soylu, Emel - Gül, Sema - Aslan, Kübra - Türkoğlu, Muammer - Terzi, Murat. “Vision Transformer Based Classification of Neurological Disorders from Human Speech”. Firat University Journal of Experimental and Computational Engineering 3/2 (01 Haziran 2024): 160-174. https://doi.org/10.62520/fujece.1454309.
JAMA
1.Soylu E, Gül S, Aslan K, Türkoğlu M, Terzi M. Vision Transformer Based Classification of Neurological Disorders from Human Speech. Firat University Journal of Experimental and Computational Engineering. 2024;3:160–174.
MLA
Soylu, Emel, vd. “Vision Transformer Based Classification of Neurological Disorders from Human Speech”. Firat University Journal of Experimental and Computational Engineering, c. 3, sy 2, Haziran 2024, ss. 160-74, doi:10.62520/fujece.1454309.
Vancouver
1.Emel Soylu, Sema Gül, Kübra Aslan, Muammer Türkoğlu, Murat Terzi. Vision Transformer Based Classification of Neurological Disorders from Human Speech. Firat University Journal of Experimental and Computational Engineering. 01 Haziran 2024;3(2):160-74. doi:10.62520/fujece.1454309

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