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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.

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Due to copyright restrictions, the access to the full text of this article is only available via subscription.Biomedical signals (BSs), which give information about the normal condition and also the inherent irregularities of our body, are expected to have non-stationary character due to the time-varying behavior of physiological systems. The Fourier transform and the short time Fourier transform are the widely used frequency and time-frequency analysis methods for extracting information from BSs with fixed frequency and time-frequency resolution respectively. However, in order to derive relevant information from non-stationary BSs, an appropriate analysis method which exhibits adjustable time-frequency resolution is needed. The wavelet transform (WT) can be used as a mathematical microscope in which the time-frequency resolution can be adjusted according to the different parts of the signal. The discrete wavelet transform (DWT) is a fast and discretized implementation for classical WT. Due to the aliasing, lack of directionality and shift-variance disadvantages, the DWT exhibits limited performance in the process of BSs. In literature, an improved version of the DWT, which is named as Dual Tree Complex Wavelet Transform (DTCWT), is employed in the analysis of BSs with great success. In this study, considering the improvements in embedded system technology and the needs for wavelet based real-time feature extraction or de-noising systems in portable medical devices, the DTCWT is implemented as a sub-system in field programmable gate arrays. In proposed hardware architecture, for every data input-channel, DTCWT is implemented by using only one adder and one multiplier. Additionally, considering the multi-channel outputs of biomedical data acquisition systems, this architecture is designed with the capability of running in parallel for N channels

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