UYKU APNESİ TEŞHİSİNDE GİYİLEBİLİR TEKNOLOJİLERDEKİ GELİŞMELER: DERLEME

Abstract

Bu makale, obstrüktif uyku apnesinin (OSA) tanısında klasik polisomnografiye (PSG) ait temel ölçütleri entegre eden giyilebilir cihazların kullanımına ilişkin literatürün kapsamlı bir değerlendirmesini sunmaktadır. PSG altın standart olmakla birlikte; yüksek maliyet, uzun süren işlemler, sınırlı erişilebilirlik ve hasta konforsuzluğu gibi pek çok dezavantaja sahiptir. Giyilebilir ve taşınabilir cihazlardaki son gelişmeler; kalp atım hızı, kan oksijen satürasyonu (SpO₂), solunum hızı, vücut hareketleri ve uyku pozisyonu gibi biyofizyolojik parametrelerin sürekli izlenmesini mümkün kılarak daha erişilebilir, kullanıcı dostu ve maliyet etkin alternatifler sunmaktadır. WatchPAT, Belun Ring ve NightOwl gibi ticari cihazlar; apne-hipopne indeksi (AHI) ve oksijen desatürasyon indeksi (ODI) gibi kritik ölçümlerde polisomnografi ile orta ila yüksek düzeyde uyum göstermektedir. Mevcut sınırlılıklar arasında hava akımının doğru şekilde ölçülememesi, sınırlı sayıda EEG/EMG kanalı, uyku evrelerinin sınıflandırılmasında düşük doğruluk, değişken algoritma performansı ve yetersiz klinik doğrulama yer almaktadır. Bu nedenle söz konusu cihazlar çoğunlukla ön tanı amaçlı tarama araçları olarak kullanılmaktadır. Özellikle derin öğrenme analizleri olmak üzere yapay zekânın entegrasyonu, tanısal doğruluğun ve klinik güvenilirliğin artırılması açısından önemli fırsatlar sunmaktadır. Uyku evrelerini değerlendiren, sensörlerin hatalı yerleştirilmesini tespit eden ve çok modlu biyobelirteçleri analiz eden yapay zekâ destekli modeller; veri kalitesini ve hasta uyumunu artırmaktadır. Ayrıca temassız ölçüm teknolojileri, mobil sağlık uygulamaları ve çok modlu veri füzyonu; PSG ile ev tipi uyku apne testi (HSAT) sistemlerinin entegrasyonunu kolaylaştırmaktadır. Gelecekte, daha küçük sensörler, otomatik yapay zekâ destekli analizler ve etkileşimli kullanıcı arayüzleri içeren hibrit modellerin klinik uygulamada daha önemli bir rol üstlenmesi ve geleneksel PSG’ye ölçeklenebilir ve verimli alternatifler sunması beklenmektedir.

Keywords

• Obstrüktif uyku apnesi
• Polisomnografi
• Giyilebilir teknoloji
• Yapay zeka

Project Number

FDK-2022-8789

ADVANCED TECHNOLOGIES IN WEARABLE FOR SLEEP APNEA DIAGNOSIS: A REVIEW

Abstract

This article provides a comprehensive assessment of the literature regarding the application of wearable devices that incorporate essential metrics of traditional polysomnography (PSG) for the diagnosis of obstructive sleep apnea (OSA). While PSG serves as a standard, it entails several disadvantages, including exorbitant expenses, lengthy processes, limited accessibility, and patient discomfort. Recent innovations in wearable and portable devices enable continuous monitoring of biophysiological parameters such as heart rate, blood oxygen saturation (SpO₂), respiratory rate, body movements, and sleep position, offering accessible, user-friendly, and economical alternatives. Commercial devices like the WatchPAT, Belun Ring, and NightOwl demonstrate moderate to strong correlations with polysomnography in key metrics, including the apnea-hypopnea index (AHI) and oxygen desaturation index (ODI). The existing limitations include the inability to precisely measure airflow, a limited number of EEG/EMG channels, low accuracy in sleep stage classification, inconsistent algorithmic performance, and insufficient clinical validation. As a result, these devices are primarily utilized as pre-diagnostic screening tools. The integration of artificial intelligence, particularly deep learning analysis, presents significant opportunities for enhancing diagnostic precision and clinical reliability. AI-driven models that evaluate sleep stages, detect improper sensor installation, and analyze multimodal biomarkers are improving data quality and patient compliance. Moreover, contactless measurement technologies, mobile health applications, and multimodal data fusion are enhancing the integration of polysomnography (PSG) and home sleep apnea testing (HSAT). In the future, hybrid models incorporating smaller sensors, automated AI-driven analytics, and interactive user interfaces are expected to play a more prominent role in clinical practice, providing scalable and efficient alternatives to traditional PSG.

Keywords

• Obstructive Sleep Apnea
• Polysomnography
• Wearable Technology
• Artificial Intelligence

Supporting Institution

Suleyman Demirel University Scientific Research Projects Coordination Unit

Project Number

FDK-2022-8789

Thanks

This study is supported by the Scientific Research Projects Coordination Unit of Süleyman Demirel University under project number FDK-2022-8789.

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