Download ECG Feature Elements Identification for Cardiologist Expert

Proceedings of the 2005 IEEE
Engineering in Medicine and Biology 27th Annual Conference
Shanghai, China, September 1-4, 2005
ECG Feature Elements Identification For Cardiologist Expert Diagnosis
Miss Hong Liang
University of Minnesota, USA
[email protected]
Analyzes the ECG which includes “To Print ECG wave form
and patient ID, basic measurements summary, and computerExpert System, Electrocardiogram (ECG), (P, QRS, T generated ECG interpretation” .
This paper proposes systematic math analytic methods,
wave), Decision-rule Base, Diagnosis, and Cardiologist.
especially Multiple Feature Analytic methods for ECG 5
Key Features (P wave, QRS complex and T wave region)
Abstract
Identification. [3] For simplification and accuracy of slope
This paper proposes a reliable method for Cardiologist and area estimation, I invent a differential algorithm for slope
Expert Diagnosis based on ECG Elements Identification. estimation and an integral algorithm for area estimation. For
This method analyzes ECG Key features (P wave, QRS “Exact ECG information Elements Identification” in
complex, T wave). It includes noise purification, sample incomplete or even confusing ECG information flow, I
design for digital ECG, Understanding of The HP ECG invent a convolution algorithm.
Criteria Program, and The Extended Measurements Report.
The methods proposed in this paper are highly effective,
This project report synthesizes the advantages of Math, sensitive, accurate, and reliable.
Multiple Function Analysis, Database and Knowledge Base,
and Expert System to explore the mechanism of “ECG 2. ECG Feature Elements
Feature Elements Identification For Cardiologist Expert
ECG is a wave that is a deflection from the baseline that
Diagnosis”. This report proposes an integral method of ECG
information flow for its area computing and a differential represents an electrical event in the heart, such as atria
atria
repolarization,
ventricular
method of ECG information flow for its slope computing and depolarization,
a convolution method for true ECG wave form element depolarization, ventricular repolarization, or transmission,
extraction even they confuse with each other or incomplete. and so on. Waves are deflections from the baseline, line from
This method can implement ECG report in real time and one TP segment to the next. A segment is a specific portion
provide exact explanations for the diagnostic decision of the complex as it is represented on the ECG.
The P wave represents the both atria activation
obtained. This method can offer mean (standard) values
estimation for parameters and Confident Interval computing (depolarization). The first half of the P wave is the activation
for predictive accuracy (above 85%). This method solved of the right atrium, whereas the second half of the P wave is
following problems: noise purification, incomplete and the activation of the atria septum and the left atrium. The
confusing ECG element’s key features identification, duration of the P wave itself can vary between 0.08 and 0.11
Decision-Rule Base, and Expert Diagnosis Model --- For seconds in normal adults. The QRS complex represents the
ventricular activation. T wave represents the ventricular
“Cardiologist Expert Diagnosis” research topic.
repolarization and sometimes is followed by a U wave. The
atria repolarization is represented by Tp wave, this wave can
occur in the PR interval. The PR or PQ interval is the interval
1. Introduction
between the beginning of the P wave and the beginning of
ECG 5 Key Features (P wave, QRS complex and T the QRS complex, normal duration is 0.12-0.20 sec. The
wave region) Identification is an important method for section from the end of the QRS complex till the beginning
Cardiologist expert diagnosis for normal and heart disease of T wave is known as the ST segment.
people. Hewlett-Packard designs HP PageWriter 200
Cardiograph to test, store and analyze ECG information The QRS Complex
(wave form’s amplitudes, duration, shapes, areas, intervals
The QRS complex represents ventricular depolarization.
and slopes). These techniques can obtain the quantitative
paradigm for decision by integrating (rule-based expert The main elements are Q, R, and S waves. The Q wave is the
systems, flexible logic, element identification, and first negative deflection after the P wave. The R wave is the
information science, etc.). This computerized ECG analytic first positive deflection after the P. The S wave is the first
negative deflection after the R wave. During the ventricular
system (Cardiograph) has following functions:
activation, the right precordial leads will record negative
Provides formats (Auto and Manual)
Has ECG transfer by flexible disk, local transmission through ventricular QRS complexes. On the other hand the
direct connect cable, remote transmission via telephone complexes recorded by medial precordial leads will be
modem, and computer (email, voice email and vision email). equiphasic.
Keywords:
0-7803-8740-6/05/$20.00 ©2005 IEEE.
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A Q wave is considered significant if it is 0.03 seconds or
wider, or its height is equal to or greater than one-third the
height of the R wave. The QRS complex duration should be
measured from the onset of the first deflection after the PR
interval to the end of the complex. Normally, the QRS
complex measures between 0.06 and 0.11 seconds.
The research of ECG Feature Elements PQRST shows that
the ECG waveform element extraction and ECG slope and
area analysis are very important.
3. My New Algorithm For ECG Features Identification
For solving slope estimation problem, I invent a
differential algorithm. The slope function is equal to
d(f(x))/dx. Then I can compare real ECG slope function with
critical values of the slope of key features of ECG Wave
form (PQRST) for Cardiologist Expert Diagnosis.
For solving area estimation problem, I invent an integral
algorithm. The area function is equal to Sum(f(x)*delta(x)).
Then I can compare the real area function with critical values
of the area of key features of ECG Waveform (PQRST) for
Cardiologist Expert Diagnosis.
For solving confusing and incomplete ECG’s features
identification problem, I invent a convolution algorithm for
this delayed renewal processing (model). This “model in the
real world” can be represented as a convolution of “a time
pulse series” with “an ECG wave form element” in time
domain.
1. If we take FFT or Laplace Transformation, then the
“convolution” can be changed to the “production” in the
transformed domain.
2. Then we take “log” in the transformed domain, then the
“production” can be changed to the “add” relationship.
3. Then we use logic analysis of spectrum (in the
transformed domain) to obtain the address code of “the
ECG wave form element” by Coordinate Projection in
“the Transformed Domain with log value” for expert
system and database’s “Index Code of logic Linking
Memory for expert decision”, then we can save the
related information of “ECG Wave form Element” in the
Data Cell in database.
a) After we take inverse transformation for “log” and
“FFT or Laplace” Transformation, we can obtain true
information for the “ECG Wave form Element”, even
they confuse with each other or even they are incomplete
before processing.
b) Then we can identify and abstract the amplitudes,
duration, areas, shapes, intervals and slopes which
characterize the ECG key wave form (P wave, QRS
complex, and T wave; and ST segment).
c) The time pulse series shows Heart Rate Variability
(HRV) information. [6] The short intervals of the time
pulse series show too fast heart rate and ECG, and the
long intervals of the time pulse series show too slow
heart rate and ECG.
These new algorithms are more accurate, more efficient,
more reliable and simpler for ECG Key Features
Identification. The results will be useful for cardiologist
diagnosis.
4. Abstract For Multivariate Statistical Analysis [4], [7].
“Multiple Regression Analysis”
finds “a functional
relationship between the ECG’s response variable and
predictor variables --- the amplitudes, duration, areas, shapes,
intervals and slopes”. Then through P-value significant
analysis, the important terms are accepted.
“Multivariate Categorical Data Analysis” can obtain very
important logic categories for ECG features identification.
Vectors and Matrices can represent univariate and
multivariate ECG data, for example, the full range of
measurements in the measurement matrix.
“Lineal Combination Analysis” includes Eigen-values and
Eigen-vectors,
Orthogonal
Transformation
for
Simplification, Diagonal Matrix Simplification by Eigen
Matrix and Symmetric Matrix. This procedure can generate
an explicit math summary for real ECG information flow.
“Multivariate Population Mean Vector and Population
Variance Matrix estimation” include Population Correlation
Matrix, Univariate and Multivariate Normal Distribution,
Standardization and Multivariate Normality. The procedure
can obtain mean parameters of ECG PQRST Key Features
with accuracy.
“Multivariate Sampling Distribution Estimation” includes
Multivariate Central Limit Theorem, Chi-Square Test,
Wishart distribution, Multiple Sample Comparison of Means,
Multivariate Behrens-Fisher Problem solving, MacAnova for
multiple testing, Simultaneous estimation and Confidence
region, and Relationship of Confidence Regions and Tests.
This procedure can implement Sample design for Digitizing
the ECG.
“Principal Components Analysis (PCA) method” includes
The Singular Value Decomposition (SVD), Orthogonal
Matrix of the independent components, Eigenvalues and
Eigenvectors, and Variance Matrix and Correlation Matrix,
Principal Components of Population Correlation, Inference
for principal components, and Sampling distribution of
eigen-value Fact. For feature identification purposes, each
centered sample is represented by its projections on n
independent components in PCA method. This procedure can
solve ECG Logic Category Generation and Classification
problems. These ECG logic categories are classified to five
different ECG information types which include normal (NR),
ventricular couplet (VC), ventricular tachycardia (VT),
ventricular bigeminy (VB) and ventricular fibrillation (VF).
[7]
“Classification method” includes Prior Probabilities,
Posterior Probabilities, Classification Probabilities, Kernel
estimate, Parametric density estimates, Application of
parametric density estimation for Multivariate Normal
Populations, Partitioning of observation space, Evaluation of
an estimated rule, apparent error rate (APER), Choice of
variables for classification, and Finding a “Best” subset. The
method can generate ECG logic categories and decision-rule
base for cardiologist expert diagnosis, and estimate ECG
PQRST key parameters (the amplitudes, duration, areas,
shapes, intervals and slopes).
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“Cluster Method” includes Sorting or sequence and
combining, Dividing up, Targeted number of clusters, 6. Conclusions
Estimation of mixture model, Hierarchical Agglomerate
This paper proposed ECG Feature Elements
Clustering, Single and Complete and Average Linkage, and
K-means clustering. This method can solve ECG group Identification method for Cardiologist Expert Diagnosis.
measurement problem.
This method includes ECG detection, Noise Purification,
ECG PQRST Key Features identification even they are
5. Abstract For HP Cardiograph Method [1], [2]
confused with each other or incomplete, the key parameters
(the amplitudes, duration, areas, shapes, intervals and slopes)
estimation, Global ECG Data transfer by email, Logic
5.1 Sample design for Digitizing the ECG
Category Classification, Measurements Summary, and
The ECG waveform data is recorded at a sample rate that Computer-generated ECG Interpretation. This method is
significantly exceeds the 250 samples per second at 5 E-6 V explicit, highly efficient, highly accurate, and reliable. It can
resolution of the HP Analytic Program. The HP run in real time and can offer a clear and explicit explanation
cardiograph’s digital information processing ensures the for the Cardiologist’s diagnosis.
most accurate reproduction of the patient’s ECG PQRST.
Future work will focus on Global ECG index system, ECG
Logic Category Classification and logic linking memory, and
Cardiologist Expert’s Decision-Rule (and Knowledge)
5.2 Reducing Artifact
Abstract For Exact Expert Diagnosis.
Electrical interference, patient respiration, patient
movement and muscle tremors can add noise and artifact to 7. Acknowledgments
the ECG PQRST information. Poor quality electrodes or
inadequate patient preparation can also degrade the ECG
I should be grateful for Professor J. Slagle for the advice in
PQRST information. The ECG is analyzed for muscle Expert System, for professors in Statistics in University of
artifact, AC noise, baseline wander, and leads-off. The HP Minnesota in USA for their guidance in Statistics, and for
interpretive cardiograph has been carefully designed to staffs in ECG Lab in Hospital in University of Minnesota for
substantially reduce artifact and accurately record the ECG their HP Page Writer Cardiograph --- User’s Guide and
PQRST information.
Physician’s Guide.
ECG Noise Purification --- ECG Filter Report Fields:
8. References
Auto Wander Filter in (0.05 Hz, 0.15 Hz, 0.5 Hz)
Auto Noise Filter in (40 Hz, 100 Hz, 150 Hz)
Manual Wander Filter in (0.05 Hz, 0.15 Hz, 0.5 Hz)
Manual Noise Filter in (40 Hz, 100 Hz, 150 Hz)
[1] Hewlett Packard Page Writer 200/200I Cardiograph,
User’s Guide
[2] Hewlett-Packard Interpretive Cardiograph, Physician’s
Guide
[3] V.Avbelj, etc. (2003): “Beat-to-beat repolarisation
variability in body surface electrocardiograms”, Journal of
Medical & Biological Engineering & Computing 2003,
Vol.41, pp.556-560.
[4] Bernard W. Lindgren (1993): “Statistical Theory”, 4th
Edition, and Published by “Chapman & Hall”, ISBN 0-41204181-2
[5] Thomas A. Buckingham, M.D. (2004): “Basics of ECG
Reading: Part I”,
http://www.nursce.com/courses/1071/1071.htm
[6] R.Acharya U, etc. (2004): “Classification of cardiac
abnormalities using heart rate signals”, Medical & Biological
Engineering & Computing 2004, Vol. 42, pp. 288-293
[7] Bruce D. Nearing and Richard L. Verrier, in Cardiology
Division, Harvard Medical School, “Modified moving
average analysis of T-wave alternant to predict ventricular
fibrillation with high accuracy”,
Journal of Applied
Physiology (February 2002), Vol. 92, Issue 2, pp.541-549.
[8] M.I. Owis, and etc. (2002): “Characterization of
electrocardiogram signals based on blind source separation”,
5.3 Understanding The HP Adult ECG Criteria Program
The criteria for the interpretive statements use the full
range of measurements in the measurement matrix. These
include duration, amplitudes, areas, slope, interval and other
features. Following logic categories are very important for
ECG features identification for cardiologist diagnosis. They
are: Electronic Pacemaker, Basic Cardiac Rhythm, Premature
Beats (Short R-R), Pauses (Long R-R Interval), QRS Axis,
Ventricular Conduction Delays, Tall T Waves, Drug and
Electrolyte Effects, T Wave ST Segment Depression,
Combined ST and T Abnormalities, ST Segment Elevation,
and Severity. The ECG waveforms PQRST of above heart
diseases are not normal. So we can identify related heart
diseases of patients by their ECG PQRST with cardiologists.
[2]
The understanding of the HP Adult ECG Criteria Program
includes following key parts: ECG PQRST Averaging,
ECG Analysis Program, Basic Cardiac Rhythm, Premature
Beats (Short R-R), and etc.
Medical & Biological Engineering & Computing 2002, Vol. Author Biographies:
40, pp. 557-564
Miss Hong Liang won USA Student NSF in November
1991. She passed Master Written Exam in statistics in U. of
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MN in USA in August 2001. She wins “The National Her research interests include information science and A.I.
Outstanding Scholars Award of Achievement” in May 2002. expert system.
Email: [email protected]
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