%% Very simple example of how to use the ECGkit in a cluster
%
% This script is the same as "first_simple_example" but it is prepared to
% run locally without arguments, or in a cluster environment by
% using "pid_str" argument. The pid_str argument is a char with format
% 'N/M', being N <= M with default value '1/1'. You can partition a big job
% into M pieces in a cluster architecture, by starting M processes with N
% ranging from 1 to M.
%
% See also ECGwrapper, ECGtask, UnInstallECGkit, first_simple_example
%
% *Author*: Mariano Llamedo Soria
% <matlab:web('mailto:llamedom@electron.frba.utn.edu.ar','-browser') (email)>
% *Version*: 0.1 beta
% *Birthdate*: 11/13/2014
% *Last update*: 18/14/2014
% *Copyright* 2008-2014
%
function second_simple_example(pid_str)
if( nargin < 1 || ~ischar(pid_str) )
% single PID run
pid_str = '1/1';
end
root_path = fileparts(mfilename('fullpath'));
% get the parent or ECGkit root folder.
root_path = fileparts(root_path);
% default folder to look at
examples_path = [root_path filesep 'recordings' filesep ];
% the filename of the recording. In this example, be sure to choose a
% longer ECG recording. Not included with the source code, get one from
% Physionet and name this variable accordingly.
example_filename = 'longer_example_recording.mat';
% user-defined string to customize the experiment
my_user = 'my_experiment_name';
% In case of running in a user-assisted fashion.
bUseDesktop = usejava('desktop');
if( ~bUseDesktop )
% For cluster or distributed environment processing.
InstallECGkit();
end
% this is an ECGwrapper object, and is the interface to get samples
% from the recordings to the processing algorithms.
ECGw = ECGwrapper('recording_name', [examples_path example_filename]);
% this is the to indicate an ECGwrapper object the way it shoud deal
% with multi-processing
ECGw.this_pid = pid_str;
% in this example we are not interested in caching results, since the
% recording is very short, so we will re-process every call.
ECGw.cacheResults = true;
% add a user-defined prefix to the result filenames
ECGw.user_string = my_user;
% % QRS detection in ALL the signals present in the recording, with ALL
% % the QRS detection algorithms available.
% ECGw.ECGtaskHandle = 'QRS_detection';
% ECGw.Run
% QRS detection, but only in those signals that seems to be ECG. That
% mean, that the description of the signal give some clue about ECG. If
% not sure, process all signals as ECG.
ECGw.ECGtaskHandle = 'QRS_detection';
ECGw.ECGtaskHandle.only_ECG_leads = true;
% just restrict the run to the three algorithms
ECGw.ECGtaskHandle.detectors = { 'wavedet', 'gqrs', 'wqrs'};
ECGw.Run
% Pulse detection in pulsatile signals
ECGw.ECGtaskHandle = 'PPG_ABP_detector';
% only in pulsatile signals. This is donde as before, based on the
% description of the signal, if unsure, process all signals.
ECGw.ECGtaskHandle.lead_config = 'PPG-ABP-only';
ECGw.Run
% here we perform ECG delineation based on the previous QRS detections
% (the corrected ones if available) with ALL the algorithms present in
% the toolbox (wavedet only at the moment of writing this)
ECGw.ECGtaskHandle = 'ECG_delineation';
cached_filenames = ECGw.GetCahchedFileName({'QRS_corrector' 'QRS_detection'});
if( ~isempty(cached_filenames) )
ECGw.ECGtaskHandle.payload = load(cached_filenames{1});
ECGw.Run
end
% finally you can classify the heartbeats according to the AAMI
% classes, also based in the previous QRS detections (again, the
% corrected ones if available)
ECGw.ECGtaskHandle = 'ECG_heartbeat_classifier';
ECGw.ECGtaskHandle.mode = 'auto';
cached_filenames = ECGw.GetCahchedFileName({'QRS_corrector' 'QRS_detection'});
if( ~isempty(cached_filenames) )
ECGw.ECGtaskHandle.payload = load(cached_filenames{1});
ECGw.Run
% only the master PID will generate the report
if( ECGw.this_pid == ECGw.cant_pids )
% at the end, generate a report pretty-printed report with the results
reportECG(ECGw, 'LowDetail', 'full')
end
end
if( ~bUseDesktop )
UnInstallECGkit();
end