VLSI Wavelet Denoising of Neural Signals - Critical Appraisal of Different Algorithmic Solutions for Threshold Estimation

Nicola Carta, Danilo Pani, Luigi Raffo

2014

Abstract

Wavelet denoising represents a common preprocessing step for several biomedical applications exposing low SNR. When the real-time requirements are joined to the fulfilment of area and power minimization for wearable/ implantable applications, such as for neuroprosthetic devices, only custom VLSI implementations can be adopted. In this case, every part of the algorithm should be carefully tuned. The usually overlooked part related to threshold estimation is deeply analysed in this paper, in terms of required hardware resources and functionality, exploiting Xilinx System Generator for the design of the architecture and the co-simulation. The analysis reveals how the widely used Median Absolute Deviation (MAD) could lead to hardware implementations highly inefficient compared to other dispersion estimators demonstrating better scalability, relatively to the specific application.

References

  1. Bahoura, M. and Ezzaidi, H. (2012). FPGAimplementation of discrete wavelet transform with application to signal denoising. Circuits, Systems, and Signal Processing, 31(3):987-1015.
  2. Baig, M. M., Gholamhosseini, H., and Connolly, M. J. (2013). A comprehensive survey of wearable and wireless ECG monitoring systems for older adults. Medical & Biological Engineering & Computing, 51(5):485-495.
  3. Casson, A., Yates, D., Smith, S., Duncan, J., and RodriguezVillegas, E. (2010). Wearable electroencephalography. Engineering in Medicine and Biology Magazine, IEEE, 29(3):44-56.
  4. Citi, L., Carpaneto, J., Yoshida, K., Hoffmann, K.-P., Koch, K. P., Dario, P., and Micera, S. (2008). On the use of wavelet denoising and spike sorting techniques to process electroneurographic signals recorded using intraneural electrodes. Journal of Neuroscience Methods, 172:294-302.
  5. Cohen, A. and Kovacevic, J. (1996). Wavelets: the mathematical background. Proceedings of the IEEE, 84(4):514-522.
  6. Diedrich, A., Charoensuk, W., Brychta, R., Ertl, A., and Shiavi, R. (2003). Analysis of raw microneurographic recordings based on wavelet de-noising technique and classification algorithm: wavelet analysis in microneurography. IEEE Trans Biomed Eng, 50(1):41- 50.
  7. Holschneider, M., Kronland-Martinet, R., Morlet, J., and Tchamitchian, P. (1990). A real-time algorithm for signal analysis with the help of the wavelet transform. In Wavelets, pages 286-297. Springer Berlin Heidelberg.
  8. Kuzume, K., Niijima, K., and Takano, S. (2004). FPGAbased lifting wavelet processor for real-time signal detection. Signal Processing, 84(10):1931-1940.
  9. Mahmoud, M. I., Dessouky, M. I. M., Deyab, S., and Elfouly, F. H. (2008). Signal denoising by wavelet packet transform on FPGA technology. Special Issue of Ubiquitous Computing and Communication Journal Bioinformatics and Image.
  10. Martinez, J., Cumplido, R., and Feregrino, C. (2005). An FPGA-based parallel sorting architecture for the Burrows Wheeler transform. In International Conference on Reconfigurable Computing and FPGAs, ReConFig 2005.
  11. Medina, C., Alcaim, A., and Jr., J. A. (2003). Wavelet denoising of speech using neural networks for threshold selection. Electronics Letters, 39(25):1869-1871.
  12. Montani, M., Marchi, L. D., Marcianesi, A., and Speciale, N. (2003). Comparison of a programmable DSP and FPGA implementation for a wavelet-based denoising algorithm. In Proc. IEEE 46th Midwest Symposium on Circuits and Systems, volume 2, pages 602-605.
  13. Nezan, J., Siret, N., Wipliez, M., Palumbo, F., and Raffo, L. (2012). Multi-purpose systems: A novel dataflowbased generation and mapping strategy. In Proc. IEEE International Symposium on Circuits and Systems (ISCAS), pages 3073-3076.
  14. Palumbo, F., Carta, N., Pani, D., Meloni, P., and Raffo, L. (2012). The multi-dataflow composer tool: generation of on-the-fly reconfigurable platforms. Journal of Real-Time Image Processing, pages 1-17.
  15. Pani, D., Usai, F., Citi, L., and Raffo, L. (2011). Impact of the approximated on-line centering and whitening in OL-JADE on the quality of the estimated fetal ecg. In Proc. of the 5th International IEEE/EMBS Conference on Neural Engineering (NER), pages 44-47.
  16. Quiroga, R. Q., Nadasdy, Z., and Ben-Shaul, Y. (2004). Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering. Neural Comput., 16(8):1661-1687.
  17. Radovan, S., Sas?a, K., Dejan, K., and Goran, D. (2013). Optimization and implementation of the wavelet based algorithms for embedded biomedical signal processing. Computer Science and Information Systems, 10:502-523.
  18. Zhang, M., Deng, R., Ma, Z., and Zhang, M. (2011). A FPGA-based low-cost real-time wavelet packet denoising system. In Proc. of 2011 Int. Conf. on Electronics and Optoelectronics (ICEOE), volume 2, pages V2-350-V2-353.
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Paper Citation


in Harvard Style

Carta N., Pani D. and Raffo L. (2014). VLSI Wavelet Denoising of Neural Signals - Critical Appraisal of Different Algorithmic Solutions for Threshold Estimation . In Proceedings of the International Conference on Biomedical Electronics and Devices - Volume 1: BIODEVICES, (BIOSTEC 2014) ISBN 978-989-758-013-0, pages 45-52. DOI: 10.5220/0004865700450052


in Bibtex Style

@conference{biodevices14,
author={Nicola Carta and Danilo Pani and Luigi Raffo},
title={VLSI Wavelet Denoising of Neural Signals - Critical Appraisal of Different Algorithmic Solutions for Threshold Estimation},
booktitle={Proceedings of the International Conference on Biomedical Electronics and Devices - Volume 1: BIODEVICES, (BIOSTEC 2014)},
year={2014},
pages={45-52},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004865700450052},
isbn={978-989-758-013-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Biomedical Electronics and Devices - Volume 1: BIODEVICES, (BIOSTEC 2014)
TI - VLSI Wavelet Denoising of Neural Signals - Critical Appraisal of Different Algorithmic Solutions for Threshold Estimation
SN - 978-989-758-013-0
AU - Carta N.
AU - Pani D.
AU - Raffo L.
PY - 2014
SP - 45
EP - 52
DO - 10.5220/0004865700450052