HFO frequency range than we applied SWT, and we depict pure recovered. In lower
line we illustrate raw signal and recovered HFO time frequency domain.
After applying SWT, only ripples and fast ripples (HFO) are sustained, oscillatory
activities with a frequency range beyond 80 Hz, which is clear from time frequency
illustration. Hence, HFOs are efficiently reconstructed, by visual inspection of time
frequency domain. SWT is a powerful method for separating such a range of oscillations frequency from spikes.
4 Conclusion
In our study, we tested performance of SWT filtering method, in reconstruction of pure
HFO (HFO without spiky events) on simulated data and real signal. For simulated
signal (inspired from IEEG recording), we evaluated robustness of SWT in HFOs
reconstruction for several parameters that are proved very powerful in varying quality
of obtained results. We studied effect of relative amplitude between HFO and spiky
events, impact of frequency range (ripples and fast one), overlapping rate and finally
signal-to-noise ratio. From our obtained results and for all studied constraint, SWT
reconstruction of HFO reveal a good GOF rate. Hence, such study could be as a
reference of SWT performance in reconstruction of HFO among 4 constraints (amplitude, frequency, SNR, overlap).
For real data, we evaluated performance of SWT reconstruction of HFO using time
frequency representation and neurologist expert inspection.
SWT gives good results in reconstruction of non-contaminated HFO by spiky
element even in bad environment (low SNR, different range of frequency, high overlap
and low relative amplitude). These results predispose SWT for reconstruction and
detection of all range of oscillatory frequency. Our obtained results are very promising
in further study of HFO and its networks connectivity since we are dealing with pure
HFO and hence non-contaminated cortical generators. Our perspectives are defining
pure HFO networks connectivity in order to define epileptogenic zones. This result
would have an important impact on surgical intervention of pharmaco- resistant patient
to delineate epileptogenic tissue and get free seizure.
References
1. Zijlmans, M., Jacobs, J., Zelmann, R., Dubeau, F., Gotman, J.: High-frequency oscillations
mirror disease activity in patients with epilepsy. J. Neurol. 72(11), 979–986 (2009)
2. Hulsen, T., Vlieg, J.D., Alkema, W.: BioVenn- a web application for the comparison and
visualization of biological lists using area-proportional Venn diagrams. J. BMC Genomics
9(1), 488 (2008)
3. Hadriche, A., Jmail, N., Elleuch, R.: Pezard L: Different methods for partitioning the phase
space of a dynamic system. Inter. J. Comp. Appl. 975, 8887 (2014)
4. Bragin, A., Wilson, C.L., Staba, R.J., Reddick, M.: Interictal high-frequency oscillations
(80–500 hz) in the human epileptic brain: Entorhinal cortex. J. Ann. Neurol. 52, 407–415
(2002)
362
T. Guesmi et al.
line we illustrate raw signal and recovered HFO time frequency domain.
After applying SWT, only ripples and fast ripples (HFO) are sustained, oscillatory
activities with a frequency range beyond 80 Hz, which is clear from time frequency
illustration. Hence, HFOs are efficiently reconstructed, by visual inspection of time
frequency domain. SWT is a powerful method for separating such a range of oscillations frequency from spikes.
4 Conclusion
In our study, we tested performance of SWT filtering method, in reconstruction of pure
HFO (HFO without spiky events) on simulated data and real signal. For simulated
signal (inspired from IEEG recording), we evaluated robustness of SWT in HFOs
reconstruction for several parameters that are proved very powerful in varying quality
of obtained results. We studied effect of relative amplitude between HFO and spiky
events, impact of frequency range (ripples and fast one), overlapping rate and finally
signal-to-noise ratio. From our obtained results and for all studied constraint, SWT
reconstruction of HFO reveal a good GOF rate. Hence, such study could be as a
reference of SWT performance in reconstruction of HFO among 4 constraints (amplitude, frequency, SNR, overlap).
For real data, we evaluated performance of SWT reconstruction of HFO using time
frequency representation and neurologist expert inspection.
SWT gives good results in reconstruction of non-contaminated HFO by spiky
element even in bad environment (low SNR, different range of frequency, high overlap
and low relative amplitude). These results predispose SWT for reconstruction and
detection of all range of oscillatory frequency. Our obtained results are very promising
in further study of HFO and its networks connectivity since we are dealing with pure
HFO and hence non-contaminated cortical generators. Our perspectives are defining
pure HFO networks connectivity in order to define epileptogenic zones. This result
would have an important impact on surgical intervention of pharmaco- resistant patient
to delineate epileptogenic tissue and get free seizure.
References
1. Zijlmans, M., Jacobs, J., Zelmann, R., Dubeau, F., Gotman, J.: High-frequency oscillations
mirror disease activity in patients with epilepsy. J. Neurol. 72(11), 979–986 (2009)
2. Hulsen, T., Vlieg, J.D., Alkema, W.: BioVenn- a web application for the comparison and
visualization of biological lists using area-proportional Venn diagrams. J. BMC Genomics
9(1), 488 (2008)
3. Hadriche, A., Jmail, N., Elleuch, R.: Pezard L: Different methods for partitioning the phase
space of a dynamic system. Inter. J. Comp. Appl. 975, 8887 (2014)
4. Bragin, A., Wilson, C.L., Staba, R.J., Reddick, M.: Interictal high-frequency oscillations
(80–500 hz) in the human epileptic brain: Entorhinal cortex. J. Ann. Neurol. 52, 407–415
(2002)
362
T. Guesmi et al.