✦ LIBER ✦

A new method for sleep apnea classification using wavelets and feedforward neural networks

✍ Scribed by Oscar Fontenla-Romero; Bertha Guijarro-Berdiñas; Amparo Alonso-Betanzos; Vicente Moret-Bonillo

Book ID: 113469432
Publisher: Elsevier Science
Year: 2005
Tongue: English
Weight: 477 KB
Volume: 34
Category: Article
ISSN: 0933-3657
DOI: 10.1016/j.artmed.2004.07.014

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Classification of Sleep Apnea through Su

Classification of Sleep Apnea through Sub-band Energy of Abdominal Effort Signal Using Wavelets + Neural Networks

✍ M. Emin Tagluk; Necmettin Sezgin 📂 Article 📅 2009 🏛 Springer US 🌐 English ⚖ 243 KB

A New QRS Detection Method Using Wavelet

A New QRS Detection Method Using Wavelets and Artificial Neural Networks

✍ Berdakh Abibullaev; Hee Don Seo 📂 Article 📅 2010 🏛 Springer US 🌐 English ⚖ 706 KB

Corrigendum to “Classification of sleep

Corrigendum to “Classification of sleep apnea by using wavelet transform and artificial neural networks” [Expert Systems with Applications 37 (2) (2010) 1600–1607]

✍ Mehmet Akin; Necmettin Sezgin 📂 Article 📅 2012 🏛 Elsevier Science 🌐 English ⚖ 132 KB

A new method for solving MSDE based on w

A new method for solving MSDE based on wavelet neural networks

✍ Penglang Shui; Zheng Bao; Licheng Jiao 📂 Article 📅 1998 🏛 SP Science Press 🌐 English ⚖ 255 KB

A new method in determining initial weig

A new method in determining initial weights of feedforward neural networks for training enhancement

✍ Y.F. Yam; Tommy W.S. Chow; C.T. Leung 📂 Article 📅 1997 🏛 Elsevier Science 🌐 English ⚖ 651 KB

Using feedforward neural networks and fo

Using feedforward neural networks and forward selection of input variables for an ergonomics data classification problem

✍ Chuen-Lung Chen; David B. Kaber; Patrick G. Dempsey 📂 Article 📅 2003 🏛 John Wiley and Sons 🌐 English ⚖ 829 KB

## Abstract A method was developed to accurately predict the risk of injuries in industrial jobs based on datasets not meeting the assumptions of parametric statistical tools, or being incomplete. Previous research used a backward‐elimination process for feedforward neural network (FNN) input varia