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Journals / Uludağ Üniversitesi Mühendislik Fakültesi Dergisi / 2026 / Cilt: 26 Sayı: 2

ESTIMATION OF PAIN THRESHOLD FROM EEG SIGNALS OF SUBJECTS IN PHYSICAL THERAPY USING LONG-SHORT-TERM MEMORY DEEP LEARNING MODEL

Fizik Tedavide Hastaların EEG Sinyallerinden Ağrı Eşiğinin Uzun Kısa Süreli Hafıza Derin Öğrenme Modeliyle Kestirimi

Kutay Güneç(DUMLUPINAR ÜNİVERSİTESİ, SİMAV TEKNOLOJİ FAKÜLTESİ)0000-0002-9801-9222
Ömer Kasım(DUMLUPINAR ÜNİVERSİTESİ, SİMAV TEKNOLOJİ FAKÜLTESİ)0000-0003-4021-5412
Mustafa Tosun(DUMLUPINAR ÜNİVERSİTESİ, SİMAV TEKNOLOJİ FAKÜLTESİ)0000-0001-7167-4561
Emine Büyükköroğlu(KÜTAHYA DOÇ.DR.İSMAİL KARAKUYU SİMAV DEVLET HASTANESİ)0000-0002-3246-4964
Pages
447–460
Publication Date
2021-08-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This success is a quality that can support specialists in diagnosis and treatment by determining the threshold where pain occurs during the first physical therapy examination from the electroencephalogram signals.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Pain is a natural stimulation to protect the whole body.
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Key Findings & Data:Validation of the model was performed with records obtained from 43 volunteer subjects with a 14-channel wireless Emotive brand electroencephalogram device. 96.1% success in binary classification as with pain or without pain and 89.6% success in multi-class classification as with high pain, low pain and without pain was achieved.
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Conclusion & Impact:This success is a quality that can support specialists in diagnosis and treatment by determining the threshold where pain occurs during the first physical therapy examination from the electroencephalogram signals.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Ağrı Eşiği · EEG Sinyal İşleme · Güç Spektrumu

Abstract

Pain is a natural stimulation to protect the whole body. An overreaction to pain can damage the tissues. Therefore, it is important to know the angle at which pain is felt when routinely measuring joint range of motion during the first examination. Detection of pain with the change in characteristics of electroencephalogram signals at the moments when pain occurs is the novelty of this study. The characteristics of the signal with power band changes were obtained by frequency analysis of the electroencephalogram signals. Pain was detected by classifying these characteristics with the Long Short Term Memory deep learning model. Validation of the model was performed with records obtained from 43 volunteer subjects with a 14-channel wireless Emotive brand electroencephalogram device. 96.1% success in binary classification as with pain or without pain and 89.6% success in multi-class classification as with high pain, low pain and without pain was achieved. This success is a quality that can support specialists in diagnosis and treatment by determining the threshold where pain occurs during the first physical therapy examination from the electroencephalogram signals.

Özet

Ağrı, tüm vücudu korumak için doğal bir uyarıdır. Bu uyarıya karşı gösterilecek aşırı reaksiyon, dokuda hasarlara neden olmaktadır. İlk muayenede rutin olarak eklem hareket açıklığı (EHA) ölçümünde ağrının hissedildiği açının bilinmesi önemlidir. Ağrının oluştuğu anlardaki EEG sinyallerindeki güç değişimi ile ağrının tespiti bu çalışmanın yeniliğidir. EEG sinyallerinin frekans analizi ile güç bandı değişimleri ile sinyale ait özellikler elde edilmiştir. Bu özellikler LSTM derin öğrenme modeli ile sınıflandırılarak ağrı tespit edilmiştir. Modelin doğrulanması bu çalışma kapsamında 43 gönüllü hastadan, 14 kanallı kablosuz Emotive marka EEG cihazıyla alınan kayıtlar ile yapılmıştır. İkili sınıflandırmada %96,1 çoklu sınıflandırmada ise %89,6’lik başarı elde edilmiştir. Bu başarı, ilk fizik tedavi muayenesi sırasında ağrının oluştuğu eşiğin EEG sinyallerinden belirlemesiyle uzmanları tanı ve tedavide destekleyebilecek bir niteliktir.

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Cite This Article

Güneç, K., Kasım, Ö., Tosun, M., Büyükköroğlu, E. (2021). ESTIMATION OF PAIN THRESHOLD FROM EEG SIGNALS OF SUBJECTS IN PHYSICAL THERAPY USING LONG-SHORT-TERM MEMORY DEEP LEARNING MODEL. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, Cilt: 26 Sayı: 2, 447–460. https://doi.org/10.17482/uumfd.883100

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