Digital Fiat Currency (DFC): A Taxonomy for Automatic Sleep Stage Classification

Abstract

Deep learning is the latest phenomena, which is being used to get the results for automatic classification, segmentation, image processing in various medical fields. This technology basically helps in reducing processing time and to avoid manual classification and identification. In recent years, convolution neural network in deep learning has been used for getting automatic results from the raw data. [1], [2] This technology is quite popular in automatic sleep stage classification, these days. It is basically used for automatic sleep stage classification, as manual classification is very time consuming and complex. [3] In previous times, the classification of sleep stages, was done with the help of manual human vision inspection, which were very time consuming and complex. To fasten this process and to reduce complexity, deep learning neural network models are used for classification. These neural network models help to improve this process and give better results than manual scoring of sleep stages. [4], [5] In this proposed DFC taxonomy, these components are implemented to validate the sleep stage classification in deep convolution neural network. [6]. After validation, evaluation and verification of this Digital Fiat Currency (DFC) taxonomy, it can improve the results of classification to large extent, which involves the major components of deep learning to improve the accuracy. In addition, this proposed method, is simple and easy to adapt for other methods.