Indian Journal of Science and Technology

The PDF file you selected should load here if your Web browser has a PDF reader plug-in installed (for example, a recent version of Adobe Acrobat Reader).

If you would like more information about how to print, save, and work with PDFs, Highwire Press provides a helpful Frequently Asked Questions about PDFs.

Alternatively, you can download the PDF file directly to your computer, from where it can be opened using a PDF reader. To download the PDF, click the Download link above.

Fullscreen Fullscreen Off


Sleep apnea is a dishevelment that causes interruption in breath or shoal of the respiration. The respiratory signal is classified into three states such as normal respiration, motion artifacts, and sleep apnea and it is obtained from a physionet. Firstly, using a second order auto regressive modeling, an algorithm is developed to attain the energy and frequency parameters of the signal and then the signal is classified with threshold based manual classification into any of the above taxonomy. In addition to this dataset, MLP is trained with a back propagation learning algorithm that results in reduced time, iterations and errors. Consequently, the training of SVM, a binary classifier used to solve multiple class problems is done with the same data set and classification is made to reduce overall errors. The overall efficiency of the above techniques is compared.

Keywords

Feature Extraction, Autoregressive Model, Burgs Method, Multilayer Perceptron, Back Propagation Learning Algorithm, Support Vector Machine
User