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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

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Contents

Introduction (Ina)Preprocessing (Ina)Feature selection and extraction (Mads)Fiducial point detection (Mads)Classification (Flemming)Discussion (Flemming)

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Introduction

• Initiating problem• AMI

• Development• Diagnosis• Treatment

• STEMI vs. NSTEMI• Previous studies• Hypotheses• Data• Preprocessing

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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

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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

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Acute coronary syndrome

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Development of AMI

• Luminal obstruction is caused by atherosclerosis and thrombosis

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Two types of AMI

• ST elevation AMI (STEMI)• Non ST elevation AMI (NSTEMI)

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Treatment

• Thrombolysis• Dslfkh• Sdkhf• Only significant in ST elevation AMI

• Percutaneous intervention

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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

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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.

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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

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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

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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

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Result of preprocessing (averaging ???)

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Contents

Introduction (Ina)Preprocessing (Ina)Feature selection and extraction

(Mads)Fidoucial point detection (Mads)Classification (Flemming)Discussion (Flemming)

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Contents

Introduction (Ina)Preprocessing (Ina)Feature selection and extraction (Mads)Fidoucial point detection (Mads)Classification (Flemming)Discussion (Flemming)

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