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processBTVisual.m
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processBTVisual.m
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%% processBTVisual.m
% Visualise Background Trend (BT) for EDF files (redocrings for one
% subject) inside the provided directory
%
%% Inputs:
% pathToPatient : EDF files directory
%% Outputs:
%% Example:
%
% patient_folder = 'Data/neonate1/';
% processBTVisual(patient_folder)
%%
% Saeed Montazeri M.
% Jan 23, 2021
% Last update : Feb 14, 2021
function processBTVisual(pathToPatient)
%% Initialization
% Adding path of required functions
addpath('./Utils/')
addpath('./Preprocessing/')
addpath(genpath('./FeatureExtraction/'))
% Resampling with new frequency
fs_new = 64;
% Electricity frequency
ElectricalFreq = 50; % default: 50Hz
% Length of epochs for feature calculation
epochs = [60 * 5]; % in second
% Channel rejection threshold
artefactThreshold = 50; % in percent
classes = {'6' '5' '4' '3' '0,1,2'};
% List of features to be extracted and weights of trained classifier
try
load('featurelist_98.mat','featurelist');
load('trained_new_cls.mat')
catch ME
rethrow(ME)
end
% Preprocess data
disp(['Preprocessing ...'])
[data] = init_Preprocessing(pathToPatient,fs_new,ElectricalFreq);
% Calculate features for each epoch
disp(['Feature calculation ...'])
[FeatureMatrix] = init_featureExtraction(data,epochs,featurelist,'',artefactThreshold);
% Classifier output
disp(['Initializing BT trend ...'])
standardFeats = (FeatureMatrix - repmat(meanFeats,size(FeatureMatrix,1),1))./repmat(stdFeats,size(FeatureMatrix,1),1);
% Predict
[Tfit,~,~,PosteriorRegion] = predict(CMdl, standardFeats);
% Smooth predicted data
smoothed1 = movmean(PosteriorRegion(:,1), 5);
smoothed2 = movmean(PosteriorRegion(:,2), 5);
smoothed3 = movmean(PosteriorRegion(:,3), 5);
smoothed4 = movmean(PosteriorRegion(:,4), 5);
smoothed5 = movmean(PosteriorRegion(:,5), 5);
smoothed = [smoothed5 smoothed4 smoothed3 smoothed2 smoothed1];
smoothed = smoothed ./ sum(smoothed, 2);
% plot BT trend
plotbt(smoothed,classes)
% Remove path
rmpath('./Utils/')
rmpath('./Preprocessing/')
rmpath(genpath('./FeatureExtraction/'))