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Spike Detection Based on the Adaptive Time–Frequency Analysis

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Abstract

This paper presents a novel spike detection algorithm in nonstationary signals using a time–frequency (t–f) approach. The proposed algorithm exploits the direction of signal energy in the t–f domain to detect spikes in the presence of high-frequency nonstationary signals even at low signal-to-noise ratio. The performance of the proposed approach is evaluated using synthetic nonstationary signals, synthesized signals mimicking electroencephalogram (EEG) signals, manually selected segments of speech signals, and manually selected segments of real EEG signals. The statistical measures, such as hit rate and precision, are used to demonstrate that the proposed algorithm performs better than other widely used algorithms, such as the smoothed nonlinear energy detector.

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References

  1. R. Anvari, M. Mohammadi, A.R. Kahoo, N.A. Khan, A.I. Abdullah, Random noise attenuation of 2d seismic data based on sparse low-rank estimation of the seismic signal. Comput. Geosci. 135, 104376 (2020). https://doi.org/10.1016/j.cageo.2019.104376

    Article  Google Scholar 

  2. B. Boashash, P. Black, An efficient real-time implementation of the Wigner–Ville distribution. IEEE Trans. Acoust. Speech Signal Process. 35(11), 1611–1618 (1987). https://doi.org/10.1109/TASSP.1987.1165070

    Article  Google Scholar 

  3. B. Boashash, N.A. Khan, T. Ben-Jabeur, Time–frequency features for pattern recognition using high-resolution TFDS: a tutorial review. Digit. Signal Process. 40, 1–30 (2015)

    Article  MathSciNet  Google Scholar 

  4. B. Boashash, S. Ouelha, An improved design of high-resolution quadratic time frequency distributions for the analysis of nonstationary multicomponent signals using directional compact kernels. IEEE Trans. Signal Process. 65(10), 2701–2713 (2017). https://doi.org/10.1109/TSP.2017.2669899

    Article  MathSciNet  MATH  Google Scholar 

  5. T. Borghi, R. Gusmeroli, A. Spinelli, G. Baranauskas, A simple method for efficient spike detection in multiunit recordings. J. Neurosci. Methods 163(1), 176–180 (2007). https://doi.org/10.1016/j.jneumeth.2007.02.014

    Article  Google Scholar 

  6. R. Chandra, L.M. Optican, Detection, classification, and superposition resolution of action potentials in multiunit single-channel recordings by an on-line real-time neural network. IEEE Trans. Biomed. Eng. 44(5), 403–412 (1997). https://doi.org/10.1109/10.568916

    Article  Google Scholar 

  7. K. Das, D. Daschakladar, P.P. Roy, A. Chatterjee, S.P. Saha, Epileptic seizure prediction by the detection of seizure waveform from the pre-ictal phase of EEG signal. Biomed. Signal Process. Control 57, 101,720 (2020). https://doi.org/10.1016/j.bspc.2019.101720

    Article  Google Scholar 

  8. V. Filipovic, N. Nedic, V. Stojanovic, Robust identification of pneumatic servo actuators in the real situations. Forsch. Ing. 75(4), 183–196 (2011). https://doi.org/10.1007/s10010-011-0144-5

    Article  Google Scholar 

  9. F. Franke, M. Natora, C. Boucsein, M.H.J. Munk, K. Obermayer, An online spike detection and spike classification algorithm capable of instantaneous resolution of overlapping spikes. J. Comput. Neurosci. 29(1), 127–148 (2010). https://doi.org/10.1007/s10827-009-0163-5

    Article  Google Scholar 

  10. J. Garofolo, L. Lamel, W. Fisher, J. Fiscus, D. Pallett, N. Dahlgren, V. Zue, Timit Acoustic–Phonetic Continuous Speech Corpus (Linguistic Data Consortium, Philadelphia, 1992)

    Google Scholar 

  11. G. Gritsch, P. Ossenblok, F. Furbass, A.J. Colon, H. Perko, T. Kluge, F08 automatic spike detection in intracerebral depth electrode recordings. Clin. Neurophysiol. 129, e69 (2018). https://doi.org/10.1016/j.clinph.2018.04.171

    Article  Google Scholar 

  12. H. Hassanpour, M. Mesbah, Boashash, B. Eeg spike detection using time–frequency signal analysis, in 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 5 (2004), pp. V-421–V-424. https://doi.org/10.1109/ICASSP.2004.1327137

  13. D. Iatsenko, P.V. McClintock, A. Stefanovska, Linear and synchrosqueezed time–frequency representations revisited: overview, standards of use, resolution, reconstruction, concentration, and algorithms. Digit. Signal Process. 42, 1–26 (2015). https://doi.org/10.1016/j.dsp.2015.03.004

    Article  MathSciNet  Google Scholar 

  14. M. Ihle, H. Feldwisch-Drentrup, C.A. Teixeira, A. Witon, B. Schelter, J. Timmer, A. Schulze-Bonhage, Epilepsiae—a European epilepsy database. Comput. Methods Prog. Biomed. 106(3), 127–138 (2012). https://doi.org/10.1016/j.cmpb.2010.08.011

    Article  Google Scholar 

  15. T.Y. Jun, A.B. Jambek, U. Hashim, Performance comparison of automatic peak detection for portable signal analyser, in 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES) (2016), pp. 400–404. https://doi.org/10.1109/IECBES.2016.7843481

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

  17. N.A. Khan, S. Ali, A new feature for the classification of non-stationary signals based on the direction of signal energy in the time–frequency domain. Comput. Biol. Med. 100, 10–16 (2018). https://doi.org/10.1016/j.compbiomed.2018.06.018

    Article  Google Scholar 

  18. N.A. Khan, S. Ali, M. Mohammadi, J. Akram, Novel direction of arrival estimation using adaptive directional spatial time–frequency distribution. Signal Process (2020). https://doi.org/10.1016/j.sigpro.2019.107342

    Article  Google Scholar 

  19. N.A. Khan, F. Baig, S.J. Nawaz, N. Ur Rehman, S.K. Sharma, Analysis of power quality signals using an adaptive time–frequency distribution. Energies 9(11), 933 (2016)

    Article  Google Scholar 

  20. N.A. Khan, F. Baig, S.J. Nawaz, N. Ur-Rehman, S.K. Sharma, Analysis of power quality signals using an adaptive time–frequency distribution. Energies (2016). https://doi.org/10.3390/en9110933

    Article  Google Scholar 

  21. N.A. Khan, B. Boashash, Multi-component instantaneous frequency estimation using locally adaptive directional time frequency distributions. Int. J. Adapt. Control Signal Process. 30(3), 429–442 (2016)

    Article  MathSciNet  Google Scholar 

  22. N.A. Khan, M. Mohammadi, Reconstruction of non-stationary signals with missing samples using time-frequency filtering. CSSP 37(8), 3175–3190 (2018). https://doi.org/10.1007/s00034-018-0814-8

    Article  MathSciNet  Google Scholar 

  23. N.A. Khan, M. Mohammadi, I. Stankovic, Sparse reconstruction based on iterative TF domain filtering and viterbi based IF estimation algorithm. Signal Process (2020). https://doi.org/10.1016/j.sigpro.2019.107260

    Article  Google Scholar 

  24. S. Kim, J. McNames, Automatic spike detection based on adaptive template matching for extracellular neural recordings. J. Neurosci. Methods 165(2), 165–174 (2007). https://doi.org/10.1016/j.jneumeth.2007.05.033

    Article  Google Scholar 

  25. X. Liu, X. Yang, N. Zheng, Automatic extracellular spike detection with piecewise optimal morphological filter. Neurocomputing 79, 132–139 (2012). https://doi.org/10.1016/j.neucom.2011.10.016

    Article  Google Scholar 

  26. M. Mohammadi, N.A. Khan, A.A. Pouyan, Automatic seizure detection using a highly adaptive directional time–frequency distribution. Multidimens. Syst. Signal Process. 29(4), 1661–1678 (2018). https://doi.org/10.1007/s11045-017-0522-8

    Article  MATH  Google Scholar 

  27. M. Mohammadi, A.A. Pouyan, V. Abolghasemi, N.A. Khan, Enhancement of the spikes attributes in the time–frequency representations of real EEG signals, in 2017 IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI) (2017), pp. 0768–0772

  28. M. Mohammadi, A.A. Pouyan, N.A. Khan, A highly adaptive directional time–frequency distribution. SIViP 10(7), 1369–1376 (2016). https://doi.org/10.1007/s11760-016-0901-x

    Article  Google Scholar 

  29. M. Mohammadi, A.A. Pouyan, N.A. Khan, V. Abolghasemi, An improved design of adaptive directional time–frequency distributions based on the radon transform. Signal Process. 150, 85–89 (2018). https://doi.org/10.1016/j.sigpro.2018.04.004

    Article  Google Scholar 

  30. M. Mohammadi, A.A. Pouyan, N.A. Khan, V. Abolghasemi, Locally optimized adaptive directional time–frequency distributions. Circuits Syst. Signal Process. (2018). https://doi.org/10.1007/s00034-018-0802-z

    Article  MathSciNet  Google Scholar 

  31. S. Mukhopadhyay, G.C. Ray, A new interpretation of nonlinear energy operator and its efficacy in spike detection. IEEE Trans. Biomed. Eng. 45(2), 180–187 (1998). https://doi.org/10.1109/10.661266

    Article  Google Scholar 

  32. N. Nedic, D. Prsic, L. Dubonjic, V. Stojanovic, V. Djordjevic, Optimal cascade hydraulic control for a parallel robot platform by pso. Int. J. Adv. Manuf. Technol. 72(5), 1085–1098 (2014). https://doi.org/10.1007/s00170-014-5735-5

    Article  Google Scholar 

  33. N. Nedic, D. Prsic, C. Fragassa, V. Stojanovic, A. Pavlovic, Simulation of hydraulic check valve for forestry equipment. Int. J. Heavy Veh. Syst. 24, 260–276 (2017). https://doi.org/10.1504/IJHVS.2017.084875

    Article  Google Scholar 

  34. Z. Nenadic, J.W. Burdick, Spike detection using the continuous wavelet transform. IEEE Trans. Biomed. Eng. 52(1), 74–87 (2005). https://doi.org/10.1109/TBME.2004.839800

    Article  Google Scholar 

  35. K.C. Ray, A.S. Dhar, Cordic-based unified VLSI architecture for implementing window functions for real time spectral analysis. IEE Proceedings - Circuits, Devices and Systems 153(6), 539–544 (2006). https://doi.org/10.1049/ip-cds:20050280

    Article  Google Scholar 

  36. M.L. Scheuer, A. Bagic, S.B. Wilson, Spike detection: inter-reader agreement and a statistical turing test on a large data set. Clin. Neurophysiol. 128(1), 243–250 (2017). https://doi.org/10.1016/j.clinph.2016.11.005

    Article  Google Scholar 

  37. E.M. Schmidt, Computer separation of multi-unit neuroelectric data: a review. J. Neurosci. Methods 12(2), 95–111 (1984). https://doi.org/10.1016/0165-0270(84)90009-8

    Article  Google Scholar 

  38. S. Shahid, J. Walker, L.S. Smith, A new spike detection algorithm for extracellular neural recordings. IEEE Trans. Biomed. Eng. 57(4), 853–866 (2010). https://doi.org/10.1109/TBME.2009.2026734

    Article  Google Scholar 

  39. H.S. Shin, C. Lee, M. Lee, Adaptive threshold method for the peak detection of photoplethysmographic waveform. Comput. Biol. Med. 39(12), 1145–52 (2009)

    Article  Google Scholar 

  40. L.S. Smith, N. Mtetwa, A tool for synthesizing spike trains with realistic interference. J. Neurosci. Methods 159(1), 170–180 (2007). https://doi.org/10.1016/j.jneumeth.2006.06.019

    Article  Google Scholar 

  41. S. Stanković, L. Stanković, V. Ivanović, R. Stojanović, An architecture for the VLSI design of systems for time–frequency analysis and time-varying filtering. Ann. Des Télécommun. 57(9), 974–995 (2002). https://doi.org/10.1007/BF03005257

    Article  Google Scholar 

  42. V. Stojanovic, V. Filipovic, Adaptive input design for identification of output error model with constrained output. Circuits Syst. Signal Process. 33(1), 97–113 (2014). https://doi.org/10.1007/s00034-013-9633-0

    Article  MathSciNet  Google Scholar 

  43. V. Stojanovic, N. Nedic, Robust kalman filtering for nonlinear multivariable stochastic systems in the presence of non-gaussian noise. Int. J. Robust Nonlinear Control 26(3), 445–460 (2016). https://doi.org/10.1002/rnc.3319

    Article  MathSciNet  MATH  Google Scholar 

  44. V. Stojanovic, N. Nedic, D. Prsic, L. Dubonjic, Optimal experiment design for identification of ARX models with constrained output in non-gaussian noise. Appl. Math. Model. 40(13), 6676–6689 (2016). https://doi.org/10.1016/j.apm.2016.02.014

    Article  MathSciNet  MATH  Google Scholar 

  45. D. Ventzas, N. Petrellis, Peak searching algorithms and applications, in Proceedings of the IASTED International Conference on Signal andImage Processing and Applications, SIPA 2011 (2011). https://doi.org/10.2316/P.2011.738-049

  46. H. Wang, P. Jin, G. Liu, Automatic spikes detection in seismogram. Acta Seismol. Sin. 16(3), 348–355 (2003). https://doi.org/10.1007/s11589-003-0039-0

    Article  Google Scholar 

  47. G. Xu, J. Wang, Q. Zhang, S. Zhang, J. Zhu, A spike detection method in eeg based on improved morphological filter. Comput. Biol. Med. 37(11), 1647–1652 (2007). https://doi.org/10.1016/j.compbiomed.2007.03.005

    Article  Google Scholar 

  48. Y. Yang, Z. Peng, W. Zhang, G. Meng, Parameterised time–frequency analysis methods and their engineering applications: a review of recent advances. Mech. Syst. Signal Process. 119, 182–221 (2019). https://doi.org/10.1016/j.ymssp.2018.07.039

    Article  Google Scholar 

  49. H. Zhu, J. Dong, An r-peak detection method based on peaks of shannon energy envelope. Biomed. Signal Process. Control 8(5), 466–474 (2013). https://doi.org/10.1016/j.bspc.2013.01.001

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Information Technology, University of Human Development, Sulaymaniyah, Iraq

    Mokhtar Mohammadi

  2. Electrical Engineering, Foundation University, Islamabad, Pakistan

    Nabeel Ali Khan

  3. Department of Computer Engineering and Information Technology, Shahrood University of Technology, Shahrood, Iran

    Hamid Hassanpour

  4. Department of Communication and Computer Engineering, College of Engineering, Cihan University-Erbil, Erbil, Iraq

    Adil Hussien Mohammed

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  1. Mokhtar Mohammadi
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  2. Nabeel Ali Khan
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  4. Adil Hussien Mohammed
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Correspondence to Mokhtar Mohammadi.

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Mohammadi, M., Ali Khan, N., Hassanpour, H. et al. Spike Detection Based on the Adaptive Time–Frequency Analysis. Circuits Syst Signal Process 39, 5656–5680 (2020). https://doi.org/10.1007/s00034-020-01427-5

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