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ripplelab hilbert detection algorithm implemented in  (MathWorks Inc)


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    MathWorks Inc ripplelab hilbert detection algorithm implemented in
    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 <t>Hilbert</t> 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.
    Ripplelab Hilbert Detection Algorithm Implemented In, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/ripplelab hilbert detection algorithm implemented in/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    ripplelab hilbert detection algorithm implemented in - by Bioz Stars, 2026-03
    90/100 stars

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    1) Product Images from "Spikes on ripples are better interictal biomarkers of epilepsy than spikes or ripples"

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

    Journal: Brain Communications

    doi: 10.1093/braincomms/fcaf056

    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.
    Figure Legend 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.

    Techniques Used: Biomarker Discovery



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    ripplelab hilbert detection algorithm implemented in  (MathWorks Inc)
    90
    MathWorks Inc ripplelab hilbert detection algorithm implemented in
    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 <t>Hilbert</t> 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.
    Ripplelab Hilbert Detection Algorithm Implemented In, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/ripplelab hilbert detection algorithm implemented in/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    ripplelab hilbert detection algorithm implemented in - by Bioz Stars, 2026-03
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    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.

    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