function [necg,scalings] = normalise_ecg(ecg,start,stop)
% normalisation function. See STEP 1-3 in the code below to understand
% what it does.
%
% inputs:
% ecg: ecg before normalization
% start/stop: starting and ending points (in samples) defining
% representative ecg segments on which to compute scalings and shifts.
% The whole ecg is not used for that otherwise the scaling would be
% dependant on the local artefacts etc.
%
% outputs:
% necg: the normalized ecg which values will be within [0 1].
% scalings: scaling factor by which the ecg(s) have been multiplied
% (NOTE: that it does not take the detrand or tanh operations into account).
%
%
% FECG extraction toolbox, version 1.0, Sept 2013
% Released under the GNU General Public License
%
% Copyright (C) 2013 Joachim Behar
% Oxford university, Intelligent Patient Monitoring Group - Oxford 2013
% joachim.behar@eng.ox.ac.uk
%
% Last updated : 28-08-2013
%
% This program is free software; you can redistribute it and/or modify it
% under the terms of the GNU General Public License as published by the
% Free Software Foundation; either version 2 of the License, or (at your
% option) any later version.
% This program is distributed in the hope that it will be useful, but
% WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
% Public License for more details.
% == manage inputs
if size(ecg,2)>size(ecg,1)
ecg = ecg';
end
% == core function
try
% STEP 1: Normalizes a multivariate dataset data (where each data vector is a row in data)
% by scaling and shifting such that in each column the min/max becomes 0/1.
% If the min and the max in some column coincide, this column is not
% changed.
[~,scalings,~] = normalizeData01(ecg(start:stop,:));
% STEP 2: detrend the ecg
detrendedInput = detrend(bsxfun(@times,ecg,scalings),'constant');
% STEP 3: take the tanh of the sigal . This step is applied in order to
% avoid outliers whih could result in the reservoir state or LMS
% weights to take unexpected values that are not covered by the normal
% trajectory given the optimized global parameters or output learned.
necg = tanh(detrendedInput);
catch ME
for enb=1:length(ME.stack); disp(ME.stack(enb)); end;
necg = ecg;
end
end
function [normData, scalings, shifts] = normalizeData01(data)
% Normalizes a multivariate dataset data (where each data vector is a row in data)
% by scaling and shifting such that in each column the min/max becomes 0/1.
% If the min and the max in some column coincide, this column is not
% changed.
%
% Input arg:
% data: a dataset, either real-valued array of size N by dim or a cell array of size
% [nrSamples, 1], where the i-th cell is a real-valued array of size N_i by dim
%
% Outputs:
% normData: the dataset normalized to columns with min/max = 0/1. Each
% column in normData is computed from the corresponding column in data by
% normalizedColumn = scalefactor * (originalColum + shiftconstant). If
% the input is a cell structure, the same scalefactors and shiftconstants are
% applied across all cells, such that the *global* min/max of normData
% becomes 0/1.
% scalings: a row vector of lenght dim giving the scalefactors
% shifts: a row vector of lenght dim giving the shiftconstants
%
% Created by H. Jaeger, June 21, 2006
if isnumeric(data)
dim = size(data,2);
mins = min(data); maxs = max(data);
scalingsInv = maxs - mins;
scalings = ones(1,dim);
shifts = zeros(1,dim);
normData = data;
for d = 1:dim
if scalingsInv(1,d) > 0
scalings(1,d) = 1/scalingsInv(1,d);
shifts(1,d) = -mins(1,d);
normData(:,d) = (data(:,d) + shifts(1,d)) * scalings(1,d);
end
end
elseif iscell(data)
dim = size(data{1,1},2);
nrSamples = size(data,1);
%check if all cells have same dim
for n = 1:nrSamples
if size(data{n,1},2) ~= dim
error('all cells must have same row dim');
end
end
mins = min(data{1,1});
maxs = max(data{1,1});
for n = 1:nrSamples
mins = min(mins, min(data{n,1}));
maxs = max(maxs, max(data{n,1}));
end
scalingsInv = maxs - mins;
scalings = ones(1,dim);
shifts = zeros(1,dim);
normData = data;
for d = 1:dim
if scalingsInv(1,d) > 0
scalings(1,d) = 1/scalingsInv(1,d);
shifts(1,d) = -mins(1,d);
for n = 1:nrSamples
normData{n,1}(:,d) = (data{n,1}(:,d) + shifts(1,d)) * scalings(1,d);
end
end
end
else error('input data must be array or cell structure');
end
end