Levent ÖZTÜRK, İlhan UMUT, Erdem UÇAR, Tevfik GÜLYAŞAR, Nejdet SÜT

Can obstructive apnea and hypopnea during sleep be diff erentiated by using electroencephalographic frequency bands? Statistical analysis of receiver-operator curve characteristics

Amaç: Elektroensefalografik (EEG) frekans bandlarının uyku apneli hastalarda obstrüktif apne ve hipopne gibi anormal solunum olaylarını ayırt etmede kullanılıp kullanılamayacağını belirlemek üzere bu çalışmayı planladık. Yöntem ve gereç: 20 hastanın polisomnografik kayıtları retrospektif olarak incelendi. EEG kayıtları C4-A1 ve C3-A2 kanallarından alınarak dijital sinyal işleme yöntemlerini kullanan ve çalışma ekibi tarafından geliştirilen bir yazılım ile incelendi. Delta, teta, alfa ve beta frekans bandlarının yüzde değerleri apne ve hipopneleri ayırt edebilmek amacıyla diskriminant ve ROC analizleri kullanılarak değerlendirildi. Bulgular: C4-A1 delta (%) frekans düzeyi en yüksek diskriminatif değeri sağladı (AUC = 0,563; P < 0,001), ancak C4-A1 alfa (%) düzeyi en düşük diskriminatif değeri verdi (AUC = 0,519; P = 0,041). Benzer şekilde, C4-A2 delta (%) frekans düzeyi en yüksek diskriminatif değeri sağlarken (AUC = 0,565; P < 0,001), C4-A2 alfa (%) düzeyi en düşük diskriminatif değeri verdi (AUC = 0,501; P = 0,943). Sonuç: Diskriminant analiz sonucunda, hipopnelerin doğru sınıfl andırılma oranı % 44,8 ve obstrüktif olguların doğru sınıflandırılma oranı % 63,5 oldu. Dört farklı frekans bandı içinde en anlamlı frekans delta idi. Ancak, prediktif değerler anlamlı derecede yüksek değildi.

Obstrüktif apne ve hipopne uyku esnasında elektroensefalografik frekans bandları kullanılarak birbirinden ayırt edilebilir mi? Karakteristik işlem eğrisi (receiveroperator curve, ROC) analizi

Aim: To investigate whether electroencephalographic (EEG) frequency bands are applicable in distinguishing abnormal respiratory events such as obstructive apnea and hypopnea in patients with sleep apnea. Materials and methods: Th e polysomnographic recordings of 20 patients were examined retrospectively. EEG record segments were taken from C4-A1 and C3-A2 channels and were analyzed with soft ware that uses digital signal processing methods, developed by the study team. Percentage values of delta, theta, alpha, and beta frequency bands were evaluated through discriminant and receiver-operator curve (ROC) analysis to distinguish between apneas and hypopneas. Results: For the C4-A1 channel, delta (%) provided the highest discriminative value (AUC = 0.563; P < 0.001); on the other hand, alpha (%) gave the lowest discriminative value (AUC = 0.519; P = 0.041). Likewise, whereas for the C3-A2 channel delta (%) gave the highest discriminative value (AUC = 0.565; P < 0.001), alpha produced the lowest discriminative value (AUC = 0.501; P = 0.943). Conclusion: As a result of discriminant analysis, the accurate classifi cation rate of hypopneas was 44.8% and the accurate classifi cation of obstructive apneas was 63.5%. Of the 4 frequency bands, the most signifi cant was delta. Th e predictive values were not at signifi cance level.

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