Abstract
Classification in large-scale data is a key problem in big data domain. The theory of compressive sensing enables the recovery of a sparse signal from a small set of linear, random projections which provides a compressive classification method operating directly on the compressed data without reconstructing for big data. In this paper, we collected the compressed vowel /a:/ and /i:/ voice signals using compressive sensing for throat polyp detection. The throat polyp prediction procedure based on wavelet packet transform and support vector machine intelligent algorithm was deduced. The experiments for throat polyp prediction with the proposed classification algorithm were carried out. The results showed that the correct rate of prediction was stable under different number of samples and different random measurement matrices.
Highlights
Throat polyps are small fleshy growths which form on the vocal cords, usually as a result of overuse
Because of the advantage of compressed classification in big data based on compressive sensing comparative with classification in original data [10-15], a throat polyp detection algorithm based on compressive sensing and support vector machine is proposed in this paper
The C-support vector machine (SVM) program proposed by Dr Lin was used for setting up the classification model and throat polyp prediction [23]
Summary
Throat polyps are small fleshy growths which form on the vocal cords, usually as a result of overuse. In [1], Zhong et al tried to detect throat polyps based on patient voices. We will use the compressive sensing and support vector machine (SVM) algorithm to detect the throat polyps with patient vowel voices /a:/ and /i:/ while reducing the burden of voice data collection and storage.
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