Author
Canbay, F., Levent, Vecdi Emre, Serbes, G., Goren, S., Aydin, N.
Publication Date
2015
Publication Place
-
IEEE
Subject
Adders, Data acquisition, Discrete wavelet transforms, Embedded systems, Feature extraction, Field programmable gate arrays, Medical signal processing, Multiplying circuits, Signal denoising, Signal resolution, Time-frequency analysis, Trees (mathematics)
Type
Document
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
1557-170X
Record ID
b25d3109-b1d0-419f-b7d3-5630e829e146
Date
2015
Notes
Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Sample Text
Due to the inherent time-varying characteristics of physiological systems, most biomedical signals (BSs) are expected to have non-stationary character. Therefore, any appropriate analysis method for dealing with BSs should exhibit adjustable time-frequency (TF) resolution. The wavelet transform (WT) provides a TF representation of signals, which has good frequency resolution at low frequencies and good time resolution at high frequencies, resulting in an optimized TF resolution. Discrete wavelet transform (DWT), which is used in various medical signal processing applications such as denoising and feature extraction, is a fast and discretized algorithm for classical WT. However, the DWT has some very important drawbacks such as aliasing, lack of directionality, and shift-variance. To overcome these drawbacks, a new improved discrete transform named as Dual Tree Complex Wavelet Transform (DTCWT) can be used. Nowadays, with the improvements in embedded system technology, portable real-time medical devices are frequently used for rapid diagnosis in patients. In this study, in order to implement DTCWT algorithm in FPGAs, which can be used as real-time feature extraction or denoising operator for biomedical signals, a novel hardware architecture is proposed. In proposed architecture, DTCWT is implemented with only one adder and one multiplier. Additionally, considering the multi-channel outputs of biomedical data acquisition systems, this architecture is capable of running N channels in parallel.
DOI
10.1109/EMBC.2015.7319765
