Epileptik Nöbet Tespiti İçin Yüksek Çözünürlüklü Güç Spektral Yoğunluğu Yaklaşımları

Öz

Epilepsi, dünya çapında milyonlarca insanı etkileyen bir nörolojik hastalık olup, epileptik nöbetlerin hızlı ve doğru bir şekilde tespiti, hastaların yaşam kalitesini artırmada büyük öneme sahiptir. Bu çalışmada, EEG sinyallerinden epileptik nöbet tespiti için çeşitli güç spektral yoğunluğu yöntemleri ve sınıflandırıcılar kullanılarak farklı analizler yapılmıştır. Her bir yöntemin spektral çözünürlük ve frekans bileşenlerini ayırt etme yeteneklerinin gösterdiği değişikliği tespit etmek için Music, Lomb-Scargle, Multitaper, Welch, Periodogram, Burg gibi farklı yöntemler denenmiştir. Her sınıf için referans sinyaller oluşturulmuş, bu referans sinyallerle karşılaştırılarak spektral enerji, spektral entropi ve maksimum spektral sapma gibi ayırt edici özellikler çıkarılmıştır. Bu öznitelik vektörleri, sınıflandırıcı olarak seçilen Rastgele Orman ve k-En Yakın Komşu algoritmalarında kullanılarak sınıflandırma işlemi gerçekleştirilmiştir. Elde edilen sonuçlara göre, yüksek çözünürlüklü güç spektral yoğunluğu yöntemleri olan Music ve Lomb-Scargle ile Rastgele Orman sınıflandırıcısı en yüksek doğruluğa ulaşmıştır. Bu çalışma, epileptik nöbet tespiti alanında yüksek çözünürlüklü güç spektral yoğunluğu yöntemleri ve güçlü topluluk öğrenme tabanlı sınıflandırıcıların birlikte kullanımının, nöbet tespit doğruluğunu anlamlı şekilde artırabileceğini ortaya koyarak literatüre önemli bir katkı sunmaktadır.

Anahtar Kelimeler

• Epileptik nöbet tespiti
• Music yöntemi
• Lomb-Scargle yöntemi
• Rastgele Orman sınıflandırıcısı

High-Resolution Power Spectral Density Approaches For Epileptic Seizure Detection

Öz

Epilepsy is a neurological disorder that affects millions of people worldwide, and the rapid and accurate detection of epileptic seizures is crucial in improving patients' quality of life. This study performs various analyses using different power spectral density methods and classifiers for epileptic seizure detection from EEG signals. Methods such as Music, Lomb-Scargle, Multitaper, Welch, Periodogram, and Burg are tested to identify changes in their ability to distinguish spectral resolution and frequency components. Reference signals are created for each class, and discriminative features such as spectral energy, spectral entropy, and maximum spectral deviation are extracted by comparing these reference signals. These feature vectors are used in classification with Random Forest and k-Nearest Neighbor algorithms. The results indicate that the high-resolution spectral power density methods, Music and Lomb-Scargle, along with the Random Forest classifier, achieved the highest accuracy. This study makes a significant contribution to the literature by demonstrating that the combined use of high-resolution spectral power density methods and powerful ensemble learning-based classifiers can significantly improve seizure detection accuracy.

Anahtar Kelimeler

• Epileptic seizure detection
• Music method
• Lomb-Scargle method
• Random Forest classifier

Kaynakça

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