Enhancing Obstructive Apnea Disease Detection Using Dual‑Tree Complex Wavelet Transform‑Based Features and the Hybrid “K‑Means, Recursive Least‑Squares” Learning for the Radial Basis Function Network
عنوان مقاله: Enhancing Obstructive Apnea Disease Detection Using Dual‑Tree Complex Wavelet Transform‑Based Features and the Hybrid “K‑Means, Recursive Least‑Squares” Learning for the Radial Basis Function Network
شناسه ملی مقاله: JR_JMSI-10-4_001
منتشر شده در در سال 1399
شناسه ملی مقاله: JR_JMSI-10-4_001
منتشر شده در در سال 1399
مشخصات نویسندگان مقاله:
Javad Ostadieh - Departments of Electrical Engineering
Mehdi Chehel Amirani - Departments of Electrical Engineering
Morteza Valizadeh - Electrical and Computer Engineering, Urmia University, Urmia, Iran
خلاصه مقاله:
Javad Ostadieh - Departments of Electrical Engineering
Mehdi Chehel Amirani - Departments of Electrical Engineering
Morteza Valizadeh - Electrical and Computer Engineering, Urmia University, Urmia, Iran
Background: The obstructive sleep apnea (OSA) detection has become a hot research topic because
of the high risk of this disease. In this paper, we tested some powerful and low computational signal
processing techniques for this task and compared their results with the recent achievements in OSA
detection. Methods: The Dual-tree complex wavelet transform (DT-CWT) is used in this paper to
extract feature coefficients. From these coefficients, eight non-linear features are extracted and then
reduced by the Multi-cluster feature selection (MCFS) algorithm. The remaining features are applied
to the hybrid “K-means, RLS” RBF network which is a low computational rival for the Support
vector machine (SVM) networks family. Results and Conclusion: The results showed suitable OSA
detection percentage near ۹۶% with a reduced complexity of nearly one third of the previously
presented SVM based methods.
کلمات کلیدی: Classification, feature reduction, hybrid K‑means recursive least‑squares, multi‑cluster feature selection, obstructive sleep apnea, single‑lead electrocardiogram
صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/1700107/