Transformer Kodlayıcı ve Zaman-Frekans Görüntüleri Kullanarak Otomatik Uyku Evreleri Sınıflandırması

Year 2023, Volume: IDAP-2023 : International Artificial Intelligence and Data Processing Symposium Issue: IDAP-2023, 103 - 108, 18.10.2023

Göksu Zekiye Özen Yunus Özen

https://doi.org/10.53070/bbd.1370639

Abstract

Bu çalışmada Polisomnografi (PSG) kayıtlarından alınan tek kanallı EEG verileri kullanarak otomatik uyku evreleri sınıflandırması yapan bir derin öğrenme modeli önerilmektedir. Önerilen model, EEG sinyallerinin kısa süreli Fourier dönüşümü (STFT) ile elde edilen zaman-frekans görüntülerinden öznitelik çıkarmak için Transformer kodlayıcı kullanmaktadır. Transformer kodlayıcının çok başlı dikkat mekanizması, zaman-frekans görüntülerindeki zaman bağımlılıklarını yakalayarak modelin uykunun sıralı doğasını anlama performansını artırmaktadır. Önerilen modelin performansı, SleepEDF Expanded adlı açık erişim veri seti üzerinde değerlendirilmiştir ve 0.84 F1 skoru ile yüksek doğruluk değerine sahip sonuç elde edilmiştir. Modelin zaman-frekans görüntüleri kullanması, EEG sinyallerinin temel zaman alanı ve frekans alanı özelliklerini yakalayarak doğru uyku evreleri sınıflandırmasına katkı sağlamaktadır. Gelecek çalışmalarda, diğer PSG kanalları da dâhil edilerek uygulamada kullanımı mümkün olabilecek bir model geliştirilebileceği değerlendirilmektedir.

Keywords

Uyku evreleri sınıflandırması, Derin öğrenme, Transformer kodlayıcılar, EEG sinyalleri, Polisomnografi, Zaman-frekans görüntüleri

Automated Sleep Stage Classification Using Transformer Encoders and Time-Frequency Images

Abstract

This study proposes a deep-learning model for automatic sleep stage classification using single-channel EEG data from polysomnography (PSG) recordings. The model employs transformer encoders to extract features from time-frequency images obtained through short-time Fourier transform (STFT) of the EEG signals. The transformer encoder's multi-head attention mechanism captures temporal dependencies within the time-frequency images, improving the model's ability to understand the sequential nature of sleep. We evaluated the model's performance on the publicly available SleepEDF Expanded dataset, and a high accuracy of 0.84 F1 score was obtained. The model's use of time-frequency images enables it to capture essential time-domain and frequency-domain features of EEG signals, contributing to accurate sleep stage classification. In conclusion, our deep learning model based on transformer encoders provides an efficient and reliable solution for sleep stage classification from single-channel EEG data. Future research may explore extending the model to incorporate additional PSG channels and expanding its utility to broader sleep studies.
Keywords

Keywords

Sleep stage classification, Deep learning, Transformer encoders, EEG signals, Polysomnography, Time-frequency images

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