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
سال انتشار: 1399
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 83
فایل این مقاله در 9 صفحه با فرمت PDF قابل دریافت می باشد
- صدور گواهی نمایه سازی
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_JMSI-10-4_001
تاریخ نمایه سازی: 28 تیر 1402
چکیده مقاله:
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.
کلیدواژه ها:
Classification ، feature reduction ، hybrid K‑means recursive least‑squares ، multi‑cluster feature selection ، obstructive sleep apnea ، single‑lead electrocardiogram
نویسندگان
Javad Ostadieh
Departments of Electrical Engineering
Mehdi Chehel Amirani
Departments of Electrical Engineering
Morteza Valizadeh
Electrical and Computer Engineering, Urmia University, Urmia, Iran