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Low-power high-sensitivity spike detectors for implantable VLSI neural recording microsystems

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Abstract

A spike detector has become a necessity of a contemporary multichannel neural recording microsystem for data-compression. This paper proposes two spike detection algorithms, frequency-enhanced nonlinear energy operator (fNEO) and energy-of-derivative (ED), to solve the sensitivity degradation suffered by the conventional nonlinear energy operator (NEO) at the presence of large-amplitude baseline interferences. The efficiency of NEO, fNEO and ED algorithms are evaluated with Simulink programs firstly and then implemented into three low-power spike detectors with a standard 0.13-\(\mu m\) CMOS process. To achieve a low-power design, subthreshold CMOS analog multipliers, derivatives and adders are developed to work with a low supply voltage, 0.5 V. The power dissipation of the proposed fNEO spike detector and ED spike detector are only 258.7 and 129.4 nW, respectively. The quantitative investigation shown in the paper indicates that both fNEO and ED spike detectors achieves superior performance than the conventional NEO spike detector. Considering its lowest power dissipation, the ED spike detector is selected for our application. Further statistical evaluations based on the true positive and false positive detection rate proves that the ED spike detectors achieves higher detection rate than that of the conventional NEO spike detector but dissipates 48 % less power.

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References

  1. Barati, S., & Sodagar, A. (2011). Discrete-time automatic spike detection circuit for neural recording implants. Electronics Letters, 47(5), 306–307. doi:10.1049/el.2010.3040.

    Article  Google Scholar 

  2. Chae, M. S., Yang, Z., Yuce, M., Hoang, L., & Liu, W. (2009). A 128-channel 6 mw wireless neural recording ic with spike feature extraction and uwb transmitter. Neural Systems and Rehabilitation Engineering, IEEE Transactions On, 17(4), 312–321. doi:10.1109/TNSRE.2009.2021607.

    Article  Google Scholar 

  3. Gibson, S., Judy, J., & Markovic, D. (2010). Technology-aware algorithm design for neural spike detection, feature extraction, and dimensionality reduction. Neural Systems and Rehabilitation Engineering, IEEE Transactions On, 18(5), 469–478. doi:10.1109/TNSRE.2010.2051683.

    Article  Google Scholar 

  4. Gosselin, B., & Sawan, M. (2009). An ultra low-power cmos automatic action potential detector. Neural Systems and Rehabilitation Engineering, IEEE Transactions On, 17(4), 346–353. doi:10.1109/TNSRE.2009.2018103.

    Article  Google Scholar 

  5. Harrison, R. R., Watkins, P. T., Kier, R. J., Lovejoy, R. O., Black, D. J., Greger, B., et al. (2007). A low-power integrated circuit for a wireless 100-electrode neural recording system. Solid-State Circuits, IEEE Journal of, 42(1), 123–133.

    Article  Google Scholar 

  6. Hiseni, S., Sawigun, C., Ngamkham, W., & Serdijn, W. (2009). A compact, nano-power cmos action potential detector. In: Biomedical Circuits and Systems Conference, 2009. BioCAS 2009. IEEE, pp. 97–100 (2009). doi:10.1109/BIOCAS.2009.5372074.

  7. Hiseni, S., Sawigun, C., & Serdijn, W. (2009). Dynamic translinear nonlinear energy operator. In: Circuit Theory and Design, 2009. ECCTD 2009. European Conference on, pp. 153–156. doi:10.1109/ECCTD.2009.5274959.

  8. Holleman, J., Mishra, A., Diorio, C., & Otis, B. (2008). A micro-power neural spike detector and feature extractor in.13 um cmos. In: Custom Integrated Circuits Conference, 2008. CICC 2008. IEEE. doi:10.1109/CICC.2008.4672089.

  9. Kaiser, J. (1990). On a simple algorithm to calculate the ‘energy’ of a signal. In: Acoustics, Speech, and Signal Processing, 1990. ICASSP-90, 1990 International Conference on, pp. 381–384 vol. 1. doi:10.1109/ICASSP.1990.115702.

  10. Kamboh, A., & Mason, A. (2010). On-chip feature extraction for spike sorting in high density implantable neural recording systems. In: Biomedical Circuits and Systems Conference (BioCAS), 2010 IEEE. doi:10.1109/BIOCAS.2010.5709559.

  11. Li, H., & Xu, Q. (2011). Sub-threshold-based ultra-low-power neural spike detector. Electronics Letters, 47(6), 367–368. doi:10.1049/el.2010.3711.

    Article  Google Scholar 

  12. Li, Y.G., Ma, Q., Haider, M. & Massoud, Y. (2013). Ultra-low-power high sensitivity spike detectors based on modified nonlinear energy operator. In: Circuits and Systems (ISCAS), 2013 IEEE International Symposium on, pp. 137–140. doi:10.1109/ISCAS.2013.6571801.

  13. Liu, W., & Liu, S.I. (2010). Design of a cmos low-power and low-voltage four-quadrant analog multiplier. Analog Integrated Circuits and Signal Processing, 63(2), 307–312. doi:10.1007/s10470-009-9382-y.

  14. Liu, X., Hao, H., Yang, L., Li, L., Zhang, J., Yang, A. & Ma, Y. (2011). Epileptic seizure detection with the local field potential of anterior thalamic of rats aiming at real time application. In: Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE. doi:10.1109/IEMBS.2011.6091672.

  15. Mollazadeh, M., Murari, K., Cauwenberghs, G., & Thakor, N. (2009). Micropower cmos integrated low-noise amplification, filtering, and digitization of multimodal neuropotentials. Biomedical Circuits and Systems, IEEE Transactions On, 3(1), 1–10. doi:10.1109/TBCAS.2008.2005297.

    Article  Google Scholar 

  16. Rodriguez-Perez, A., Ruiz-Amaya, J., Delgado-Restituto, M., & Rodriguez-Vazquez, A. (2012). A low-power programmable neural spike detection channel with embedded calibration and data compression. Biomedical Circuits and Systems, IEEE Transactions On, 6(2), 87–100. doi:10.1109/TBCAS.2012.2187352.

    Article  Google Scholar 

  17. Sarje, A. & Abshire, P. (2011). Low power cmos circuit for spike detection. In: Sensors, 2011 IEEE, pp. 928–931. doi:10.1109/ICSENS.2011.6127271.

  18. Semmaoui, H., Drolet, J., Lakhssassi, A. & Sawan, M. (2010) A new approach for higher data reduction capacity based on spike detection technique in wireless multichannel neural recordings. In: Biomedical Engineering Conference (CIBEC), 2010 5th Cairo, International. doi:10.1109/CIBEC.2010.5716056.

  19. Semmaoui, H., Drolet, J., Lakhssassi, A., & Sawan, M. (2012). Setting adaptive spike detection threshold for smoothed teo based on robust statistics theory. Biomedical Engineering, IEEE Transactions On, 59(2), 474–482. doi:10.1109/TBME.2011.2174992.

    Article  Google Scholar 

  20. Singireddy, A., McMillan, K., & Graham, D. (2011). Compact and low-power continuous-time derivative circuit. Electronics Letters, 47(17), 956–957. doi:10.1049/el.2011.1991.

    Article  Google Scholar 

  21. Sundaresan, K., Allen, P., & Ayazi, F. (2006). Process and temperature compensation in a 7-mhz cmos clock oscillator. Solid-State Circuits, IEEE Journal of, 41(2), 433–442. doi:10.1109/JSSC.2005.863149.

    Article  Google Scholar 

  22. Yang, Z., Zhao, Q., & Liu, W. (2009). Improving spike separation using waveform derivatives. Journal of Neurol Engineering, 6(4), 046006.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, The University of Alabama at Birmingham, Birmingham, AL, 35294-4551, USA

    Yang-Guo Li & Mohammad Rafiqul Haider

  2. Department of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA, 01609, USA

    Yehia Massoud

Authors
  1. Yang-Guo Li
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  2. Yehia Massoud
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  3. Mohammad Rafiqul Haider
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Correspondence to Mohammad Rafiqul Haider.

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Li, YG., Massoud, Y. & Haider, M.R. Low-power high-sensitivity spike detectors for implantable VLSI neural recording microsystems. Analog Integr Circ Sig Process 80, 449–457 (2014). https://doi.org/10.1007/s10470-014-0311-3

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  • DOI: https://doi.org/10.1007/s10470-014-0311-3

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