function?[qrs_amp_rawqrs_i_rawdelay]=pan_tompkin（ecgfsgr）

%%?function?[qrs_amp_rawqrs_i_rawdelay]=pan_tompkin（ecgfs）
%?Complete?implementation?of?Pan-Tompkins?algorithm

%%?Inputs
%?ecg?:?raw?ecg?vector?signal?1d?signal
%?fs?:?sampling?frequency?e.g.?200Hz?400Hz?and?etc
%?gr?:?flag?to?plot?or?not?plot?（set?it?1?to?have?a?plot?or?set?it?zero?not
%?to?see?any?plots
%%?Outputs
%?qrs_amp_raw?:?amplitude?of?R?waves?amplitudes
%?qrs_i_raw?:?index?of?R?waves
%?delay?:?number?of?samples?which?the?signal?is?delayed?due?to?the
%?filtering
%%?Method?:

%%?PreProcessing
%?1）?Signal?is?preprocessed??if?the?sampling?frequency?is?higher?then?it?is?downsampled
%?and?if?it?is?lower?upsampled?to?make?the?sampling?frequency?200?Hz
%?with?the?same?filtering?setups?introduced?in?Pan
%?tompkins?paper?（a?combination?of?low?pass?and?high?pass?filter?5-15?Hz）
%?to?get?rid?of?the?baseline?wander?and?muscle?noise.?

%?2）?The?filtered?signal
%?is?derivated?using?a?derivating?filter?to?high?light?the?QRS?complex.

%?3）?Signal?is?squared.4）Signal?is?averaged?with?a?moving?window?to?get?rid
%?of?noise?（0.150?seconds?length）.

%?5）?depending?on?the?sampling?frequency?of?your?signal?the?filtering
%?options?are?changed?to?best?match?the?characteristics?of?your?ecg?signal

%?6）?Unlike?the?other?implementations?in?this?implementation?the?desicion
%?rule?of?the?Pan?tompkins?is?implemented?completely.

%%?Decision?Rule?
%?At?this?point?in?the?algorithm?the?preceding?stages?have?produced?a?roughly?pulse-shaped
%?waveform?at?the?output?of?the?MWI?.?The?determination?as?to?whether?this?pulse
%?corresponds?to?a?QRS?complex?（as?opposed?to?a?high-sloped?T-wave?or?a?noise?artefact）?is
%?performed?with?an?adaptive?thresholding?operation?and?other?decision
%?rules?outlined?below;

%?a）?FIDUCIAL?MARK?-?The?waveform?is?first?processed?to?produce?a?set?of?weighted?unit
%?samples?at?the?location?of?the?MWI?maxima.?This?is?done?in?order?to?localize?the?QRS
%?complex?to?a?single?instant?of?time.?The?w[k]?weighting?is?the?maxima?value.

%?b）?THRESHOLDING?-?When?analyzing?the?amplitude?of?the?MWI?output?the?algorithm?uses
%?two?threshold?values?（THR_SIG?and?THR_NOISE?appropriately?initialized?during?a?brief
%?2?second?training?phase）?that?continuously?adapt?to?changing?ECG?signal?quality.?The
%?first?pass?through?y[n]?uses?these?thresholds?to?classify?the?each?non-zero?sample
%?（CURRENTPEAK）?as?either?signal?or?noise:
%?If?CURRENTPEAK?>?THR_SIG?that?location?is?identified?as?a?換RS?complex
%?candidate?and?the?signal?level?（SIG_LEV）?is?updated:
%?SIG?_?LEV?=?0.125?證URRENTPEAK?+?0.875?SIG?_?LEV

%?If?THR_NOISE?%?搉oise?peak?and?the?noise?level?（NOISE_LEV）?is?updated:
%?NOISE?_?LEV?=?0.125證URRENTPEAK?+?0.875?NOISE?_?LEV
%?based?on?new?estimates?of?the?signal?and?noise?levels?（SIG_LEV?and?NOISE_LEV
%?respectively）?at?that?point?in?the?ECG?the?thresholds?are?adjusted?as?

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????文件???????18399??2014-11-12?18:07??pan_tompkin.m
?????文件???????57226??2014-11-12?18:07??ECG_sample_noisy.mat
?????文件????????1527??2014-11-12?18:07??license.txt