Journal: Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology

Article Title: Scalp EEG interictal high frequency oscillations as an objective biomarker of infantile spasms

doi: 10.1016/j.clinph.2020.08.013

Figure Lengend Snippet: Examples of putative real HFOs (A) and false HFOs (B) in automated HFOs detection using RippleLab are shown. (A) Time-frequency plots of HFO in a neurologically normal child (left, patient #4) and HFO in a child with epileptic spasms (right, patient#23) are shown. (B) Time-frequency plots of ringing artifact (left) and muscle artifact (right) are shown.

Article Snippet: Both RippleLab and PACT plugin (in EEGLab) run as open-source software in Matlab, with a graphic user interface without requiring typing any codes.

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Journal: Diagnostics

Article Title: Reduction of Hippocampal High-Frequency Activity in Wag/Rij Rats with a Genetic Predisposition to Absence Epilepsy

doi: 10.3390/diagnostics12112798

Figure Lengend Snippet: Representative hippocampal ripples and 50–70 Hz high frequency oscillations (HFO) in time and frequency domains. ( a ) Ripple oscillation in the dentate gyrus LPF could be visualized in the filtered signal and in the time-frequency plot (red arrow). The amplitude spectrum was plotted with a fast Fourier transform (length 512, window overlap 93.75%). ( b ) Ripple oscillation detected by means in RIPPLELAB. ( c ) High frequency oscillation 50–70 Hz (HFO) detected by means of RIPPLELAB.

Article Snippet: Hippocampal ripples (100–200 Hz) and high frequency oscillations (HFO, 50–70 Hz) were detected using GUI RIPPLELAB in MatLab [ , ].

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Journal: Brain Communications

Article Title: Spikes on ripples are better interictal biomarkers of epilepsy than spikes or ripples

doi: 10.1093/braincomms/fcaf056

Figure Lengend Snippet: Categories of events based on time-frequency analysis classification. Example of biomarkers on iEEG recordings (300 ms) for 7 out of 11 categories of events (S only, R only, FR only, S + R, S + FR, R + FR and S + R + FR). Each scenario shows a biomarker in ( I ) unfiltered iEEG (1st row); (ii) band-pass data in the frequency band 80–250 Hz (2nd row); (iii) time-frequency domain where ripples are seen as an island in the spectral content within the ripple frequency band (80–250 Hz) (3rd row); (iv) band-pass data in the frequency band 250–500 Hz (4th row) and ( V ) t I me-frequency domain where fast ripples are seen as an island in the spectral content within the fast ripple frequency band (250–500 Hz) (5th row). The detected events were visually inspected by two independent reviewers to exclude artefacts. In the filtered time domain (2nd and 4th rows), the black line represents the envelope of the analytic signal obtained using the Hilbert transform. The red dashed line represents the threshold value above which an event is considered a valid HFO. The white line in the time-frequency domain identifies the peak frequency for the HFO. S = spike; R = ripple; FR = fast ripple.

Article Snippet: HFOs were automatically detected on each channel using the RippleLab Hilbert detection algorithm implemented in MATLAB (The MathWorks, Inc.).

Techniques: Biomarker Discovery

Journal: Epilepsy research

Article Title: Harmonization of pipeline for detection of HFOs in a rat model of post-traumatic epilepsy in preclinical multicenter study on post-traumatic epileptogenesis

doi: 10.1016/j.eplepsyres.2019.03.008

Figure Lengend Snippet: EEG files details

Article Snippet: For Melbourne and UCLA, the HFOs detection was performed using RippleLab software (Matlab-based) ( Navarrete et al., 2016 ).

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