Hilbert_Envelope.m

% function [hilbert_envelope] = Hilbert_Envelope(input_signal, sampling_frequency,figures)
%
% This function finds the Hilbert envelope of a signal. This is taken from:
%
% Choi et al, Comparison of envelope extraction algorithms for cardiac sound
% signal segmentation, Expert Systems with Applications, 2008
%
%% Inputs:
% input_signal: the original signal
% samplingFrequency: the signal's sampling frequency
% figures: (optional) boolean variable to display a figure of both the
% original and normalised signal
%
%% Outputs:
% hilbert_envelope is the hilbert envelope of the original signal
%
% This code was developed by David Springer for comparison purposes in the
% paper:
% D. Springer et al., "Logistic Regression-HSMM-based Heart Sound
% Segmentation," IEEE Trans. Biomed. Eng., In Press, 2015.
%
%% Copyright (C) 2016  David Springer
% dave.springer@gmail.com
%
% 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 3 of the License, or
% 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.
%
% You should have received a copy of the GNU General Public License
% along with this program.  If not, see <http://www.gnu.org/licenses/>.

function hilbert_envelope = Hilbert_Envelope(input_signal, sampling_frequency,figures)

if nargin <3,
    figures = 0;
end


hilbert_envelope = abs(hilbert(input_signal)); %find the envelope of the signal using the Hilbert transform

if(figures)
    figure('Name', 'Hilbert Envelope');
    plot(input_signal');
    hold on;
    plot(hilbert_envelope,'r');
    legend('Original Signal','Hilbert Envelope');
    pause();
end