1/30 Classification of acute myocardial infarction based on discriminant analysis and automatic...
-
date post
19-Dec-2015 -
Category
Documents
-
view
219 -
download
0
Transcript of 1/30 Classification of acute myocardial infarction based on discriminant analysis and automatic...
1/30
Classification of acute myocardial infarction based on discriminant
analysis and automatic fiducial point detection in the ECG
Group 856bDept. of Health science and Technology
Ina LewinskyMads Hylleberg
Flemming H Gravesen Fall 2004
2/30
Contents
Introduction (Ina)Preprocessing (Ina)Feature selection and extraction (Mads)Fiducial point detection (Mads)Classification (Flemming)Discussion (Flemming)
3/30
Introduction
• Initiating problem• AMI
• Development• Diagnosis• Treatment
• STEMI vs. NSTEMI• Previous studies• Hypotheses• Data• Preprocessing
4/30
Initiating problem
• 2000-3000 cases of AMI each year• Fatal condition
• 10-20 % die before admission to hospital• 20 % of these die during the stay
• Treatment gives better results when initiated earlier• Fast and accurate diagnosis is needed to improve outcome• Usefull in pre- (ambulance)• Descision support especially for young learning doctors
• Definition of AMI• Acute necrosis in myocardial structure based in luminal
obstruction of coronary arteries• Obstruction is mainly caused by athersclerosis and
thrombosis
5/30
Diagnosis of AMI
• World Health organisation• Chest discomfort• Rise in certain blood markers
• Creatine Kinase• Myoglobin• Troponin
• Typical ECG patterns• Caused by ischemia or necrosis due to the
obstruction
6/30
Acute coronary syndrome
7/30
Development of AMI
• Luminal obstruction is caused by atherosclerosis and thrombosis
8/30
Two types of AMI
• ST elevation AMI (STEMI)• Non ST elevation AMI (NSTEMI)
9/30
Treatment
• Thrombolysis• Dslfkh• Sdkhf• Only significant in ST elevation AMI
• Percutaneous intervention
10/30
Previous studies
• Rule based systems• Artificial neural networks• Statistic approaches
• According to Willems et al. Statistical aprocahes yield best results
• Features• Traditional features
• ST elevation 80 ms after J point• T inversion• Q wave
• Additional features• Morphology of ST segment and T wave• Reciprocal changes
11/30
Hypotheses
The use of reciprocal features and morphology features from the ST segment and T wave imporves the detection of AMI relative to the use of non traditional ST features alone
It is possible in the classification to distinguish between the two groups : non ST elevation AMI and ST elevation AMI.
12/30
Data
•Definition of groups:• NSTEMI• STEMI• Healthy controls
n = 175
n = 162
21 yo u n g co n tro lsage < 30 w as
ex c lu ded an d u sedas referen ce
15 w as ex c lu d eddu e to ex cessiven o ise in o n e o r
mo re lead s
n = 141
13 p atien ts h adn o rmal E C Gacco rd in g tocard io lo gist
n = 126
N ST E M I = 1 0 C o n t ro l = 5 1ST E M I = 8 0
Evaluat io nT r aining
2/3 1/3
13/30
Preprocessing
• Noise in the ECG• Power line noise• Base line drift• Electrode contact noise
• Purpose of filtering• Attinuate noise to achieve a signal ready for fidoucial
point detection• Ensure the morphology of the ECG is intact
14/30
Choice of filters
• Simple frequency selective filters are chosen• Well proven apporach for ECG• Easy to implement• No problematic noise in the ECG
• Baseline filter• High pass -implemented as low pass and
subtracted from the signal• IIR• Cut off frequency of 0.67 Hz
• Low pass filter – remove high frequency noise• FIR• Cut off frequency of 40 Hz?????
• Notch filter – remove power line noise• Bandstop FIR filter
15/30
Result of preprocessing (averaging ???)
16/30
Contents
Introduction (Ina)Preprocessing (Ina)Feature selection and extraction
(Mads)Fidoucial point detection (Mads)Classification (Flemming)Discussion (Flemming)
17/30
Contents
Introduction (Ina)Preprocessing (Ina)Feature selection and extraction (Mads)Fidoucial point detection (Mads)Classification (Flemming)Discussion (Flemming)
18/30
JSTRQPP