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Detrended Flucatuation Analysis (DFA) of Long-range temporal correlations
by Simon-Shlomo Poil
The DFA algorithm is a scaling analysis method used to estimate long-range temporal correlations.
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| nbt_Biomarker
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% nbt_Biomarker(NumChannels) - Creates a biomarker object - this is the
% basic NBT biomarker object
%
% Usage:
% >> Biomarker = nbt_Biomarker(NumChannels);
%
% Inputs:
% NumChannels - Number of Channels
%
% Outputs:
% Biomarker - Biomarker object
%
% Example:
%
% References:
%
%
% See also:
%
%------------------------------------------------------------------------------------
% Originally created by Simon-Shlomo Poil (2009), see NBT website (http://www.nbtwiki.net)
%------------------------------------------------------------------------------------
classdef nbt_Biomarker
properties
MarkerValues % the biomarker values
NumChannels % number of channels
Fs % The sampling frequency
DateLastUpdate %last date this biomarker was updated
PrimaryBiomarker % the primary biomarker to use in scripts
Biomarkers % list of all biomarkers in the object
BiomarkerUnits %list of biomarker units
ReseacherID % ID of the Reseacher or script that made the last update
ProjectID % The ID of the project which the biomarker belongs to
SubjectID % The ID of the subject
FrequencyRange %Frequency range of processed signal [] means broadband.
Condition % The condition ID
NBTversion
end
methods
function BiomarkObject = nbt_Biomarker()
BiomarkerObject.Condition = NaN;
BiomarkerObject.DateLastUpdate = datestr(now);
BiomarkerObject.ReseacherID = NaN;
BiomarkerObject.SubjectID = NaN;
BiomarkerObject.ProjectID = NaN;
BiomarkerObject.FrequencyRange = [];
BiomarkerObject.Biomarkers = {'MarkerValues'};
BiomarkerObject.NBTversion = 2;
end
function biomarkerObject=nbt_UpdateBiomarkerInfo(biomarkerObject, SignalInfo)
biomarkerObject.DateLastUpdate = datestr(now);
biomarkerObject.FrequencyRange = SignalInfo.frequencyRange;
biomarkerObject.SubjectID = SignalInfo.subjectID;
biomarkerObject.Condition = SignalInfo.condition;
biomarkerObject.ProjectID = SignalInfo.projectID;
biomarkerObject.Fs = SignalInfo.converted_sample_frequency;
%set Badchannels to NaN
if(~isempty(SignalInfo.BadChannels))
for i=1:length(biomarkerObject.Biomarkers)
eval(['biomarker=biomarkerObject.' biomarkerObject.Biomarkers{1,i} ';']);
if(iscell(biomarker))
for m=1:length(biomarker)
if(~iscell(biomarker{m,1}))
biomarker{m,1}(find(SignalInfo.BadChannels)) = NaN;
else
for mm=1:length(biomarker{m,1})
biomarker{m,1}{mm,1}(find(SignalInfo.BadChannels)) = NaN;
end
end
end
else
biomarker(find(SignalInfo.BadChannels)) = NaN;
end
eval(['biomarkerObject.' biomarkerObject.Biomarkers{1,i} '=biomarker;']);
end
end
end
end
end
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