Logistic Regression-HSMM-based Heart Sound Segmentation 1.0

File: <base>/trainSpringerSegmentationAlgorithm.m (3,759 bytes)
% function [logistic_regression_B_matrix, pi_vector, total_obs_distribution] = trainSpringerSegmentationAlgorithm(PCGCellArray, annotationsArray, Fs, figures)
% Training the Springer HMM segmentation algorithm. Developed for use in
% the paper:
% D. Springer et al., "Logistic Regression-HSMM-based Heart Sound
% Segmentation," IEEE Trans. Biomed. Eng., In Press, 2015.
%% Inputs:
% PCGCellArray: A 1XN cell array of the N audio signals. For evaluation
% purposes, these signals should be from a distinct training set of
% recordings, while the algorithm should be evaluated on a separate test
% set of recordings, which are recorded from a completely different set of
% patients (for example, if there are numerous recordings from each
% patient).
% annotationsArray: a Nx2 cell array: position (n,1) = the positions of the
% R-peaks and postion (n,2) = the positions of the end-T-waves
% (both in SAMPLES)
% Fs: The sampling frequency of the PCG signals
% figures (optional): boolean variable dictating the disaplay of figures.
%% Outputs:
% logistic_regression_B_matrix:
% pi_vector:
% total_obs_distribution:
% As Springer et al's algorithm is a duration dependant HMM, there is no
% need to calculate the A_matrix, as the transition between states is only
% dependant on the state durations.
%% 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
% 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 [logistic_regression_B_matrix, pi_vector, total_obs_distribution] = trainSpringerSegmentationAlgorithm(PCGCellArray, annotationsArray, Fs, figures)

%% Options

if(nargin < 4)
    figures = false;

numberOfStates = 4;
numPCGs = length(PCGCellArray);

% A matrix of the values from each state in each of the PCG recordings:
state_observation_values = cell(numPCGs,numberOfStates);

for PCGi = 1:length(PCGCellArray)
    PCG_audio = PCGCellArray{PCGi};
    S1_locations = annotationsArray{PCGi,1};
    S2_locations = annotationsArray{PCGi,2};
    [PCG_Features, featuresFs] = getSpringerPCGFeatures(PCG_audio, Fs);
    PCG_states = labelPCGStates(PCG_Features(:,1),S1_locations, S2_locations, featuresFs);
    %% Plotting assigned states:
        figure('Name','Assigned states to PCG');
        t1 = (1:length(PCG_audio))./Fs;
        t2 = (1:length(PCG_Features))./featuresFs;
        plot(t1, PCG_audio, 'k-');
        hold on;
        plot(t2, PCG_Features, 'b-');
        plot(t2, PCG_states, 'r-');
    %% Group together all observations from the same state in the PCG recordings:
    for state_i = 1:numberOfStates
        state_observation_values{PCGi,state_i} = PCG_Features(PCG_states == state_i,:);

% Save the state observation values to the main workspace of Matlab for
% later investigation if needed:
assignin('base', 'state_observation_values', state_observation_values)

%% Train the B and pi matrices after all the PCG recordings have been labelled:
[logistic_regression_B_matrix, pi_vector, total_obs_distribution] = trainBandPiMatricesSpringer(state_observation_values);