=Paper=
{{Paper
|id=Vol-2300/Paper25
|storemode=property
|title=Machine Learning Methods in ECG Classification
|pdfUrl=https://ceur-ws.org/Vol-2300/Paper25.pdf
|volume=Vol-2300
|authors=Iryna Mykoliuk,Daniel Jancarczyk,Mikolaj Karpinski,Viktor Kifer
|dblpUrl=https://dblp.org/rec/conf/acit4/MykoliukJKK18
}}
==Machine Learning Methods in ECG Classification==
https://ceur-ws.org/Vol-2300/Paper25.pdf
102
Machine Learning Methods in Electrocardiography
Classification
Iryna Mykoliuk1, Daniel Jancarczyk1, Mikolaj Karpinski1, Viktor Kifer2
1. Department of Computer Science and Automatics, University of Bielsko-Biala, POLAND, Bielsko-Biala, 2 Willowa St,
email: i.mykoliuk@gmail.com, djancarczyk@ath.bielsko.pl, mkarpinski@ath.bielsko.pl
2. Faculty of Computer Information Systems and Software Engineering, Ternopil Ivan Puluj National Technical University, UKRAINE,
Ternopil, 56 Ruska St, email: kifervictor@gmail.com
Abstract. This document reports results that tend to
Electrocardiogram
confirm the applicability of the machine learning
combined with signal processing for automatic atrial ECG is the process of recording the electrical activity of
fibrillation detection from a short single lead the heart over a period of time using electrodes placed on the
electrocardiography recording. skin. These electrodes detect the tiny electrical changes on
Keywords: machine learning, signal, ECG, the skin that arises from the heart muscles.
classification, heartbeat. In a conventional 12-lead ECG, 6 electrodes are placed on
the surface of the chest, 2 on hands, and 2 on legs. The
I. INTRODUCTION overall magnitude of the heart’s electrical potential is then
Automated electrocardiography (ECG) analysis has a measured from 12 different angles (“leads”) and is recorded
number of ground tasks which include noise removal, QRS over a period of time. A healthy heart has a specific order of
detection, P and T waves detection etc. The first two polarization and depolarization during each heartbeat. It starts
problems already have a number of techniques which provide with the sinoatrial node, then spreads through the atrium to
good results. In particular band pass filters, Fourier based atrioventricular node, and then to ventricles.
analysis and transforms and wavelet transformations are
commonly used for noise reduction. Pan Tompkins algorithm,
various transforms like Wavelet, Hilbert and Empirical Mode
Decomposition combined with some decision logic can
identify QRS complexes with detection rates over 99%.
P and T wave have small amplitudes, which makes them
less noise resistant. But position and shape of the waves are
the important components of ECG analysis. With the most
recent approaches based on advanced Kalman filters and
wavelet transforms, detection accuracy for those waves
reached over 90%.
In general, ECG classification solutions tend to be class Fig. 1.Typical ECG trace
testing, when a particular record is checked against some
abnormal rhythm pattern. This happens because there’s a The graph of voltage versus time produced by this
large variety of ECG shape changes, which are hard to handle noninvasive procedure is called electrocardiogram. Each
by a single algorithm. And there is a strong tendency in last ECG consists of periodic PQRST complexes which represent
decade of moving from a threshold-based analysis (which a cycle of heart activity. In particular, one complex has:
suffers from introducing to validation dataset ECG recordings 1. P-wave (atrial contraction)
from new patients or recorded with new devices) to data- 2. QRS-complex (contraction of the ventricles)
driven approaches which include classical supervised 3. T-wave (relaxation of the ventricles)
machine-learning models and neural networks. The duration of PQRST might differ depending on the
heart rate. There are a couple of ways how to measure it, one
II. BIOLOGICAL BACKGROUND of the most common is called RR-interval, which represents
Understanding of heart activity is an important part. the duration as a distance between two following R peaks
Human heart consists of four chambers: left atrium (LA), Common abnormal heart rhythms
right atrium (RA), left ventricle (LV) and right ventricle (RV).
Heart irregular rhythm can be classified by heart rate into
Between RA and RV there is a tricuspid valve, and between
tachycardia (heart beats too fast, more than 90 bpm) and
LA and LV there is a mitral valve. These valves prevent
bradycardia (heart beats too slow, less than 60 bpm); and by
blood going directly from atriums to ventricles. Ventricles
place of occurrence into supraventricular (atria contracts
also have valves which prevent blood from going into veins,
irregularly) and ventricular (ventricles contract in an irregular
there is a pulmonary valve in right ventricle and an aortic
pattern) arrhythmias.
valve in the left one.
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Atrial fibrillation is an abnormal heart rhythm While applying class weight helped to prevent overfitting
characterized by rapid and irregular beating and which has no on early training stages, the model still failed to learn class-
symptoms in most of the cases. It is the most common serious specific features.
abnormal heart rhythm and it affects approximately 2-3% of The second approach was to apply data balancing. There
the population in Europe and North America. Also, the are many techniques available for performing data balancing
percentage of people with AF increases with age with 0.14% including:
under 50 years old, 4% between 60 and 70 years old and 14% Under-sampling (deleting instances of over-represented
over 80 years old being affected. On ECG, atrial fibrillation classes)
is usually diagnosed by absence of P-wave and irregular heart Over-sampling (repeating training with under-represented
beats pattern. classes)
Atrial flutter (AFL) is an abnormal heart rhythm, similar to Generating Synthetic Samples (generating new samples of
atrial fibrillation. Both of them are types of supraventricular the under-represented based on available samples)
tachycardia. In AFL the electrical signal goes along the Use algorithms which are better in handling imbalanced
pathway in a circular motion, which results in atrial muscle data.
contractions that are faster than and out of sync with the Under-sampling, over-sampling and using of decision
lower chambers (ventricles). Similar to A-Fib the heart beats trees which can handle imbalanced data were used to work
fast, but in a regular pattern. out the problem.
Itself atrial flutter is not life-threatening, but due to slower
Normalizing data
blood pumping it creates, the side effects might cause health
problems. Measuring ECG values might generate a wide range of
On ECG atrial flutter might be diagnosed by the presence values depending on different conditions when the recording
of multiple f-waves instead of a P-wave. was performed. This might misdirect model to learn absolute
Paroxysmal supraventricular tachycardia (PSVT) is values of one instance instead of the value for all instances.
characterized by the episodes of a rapid increase of heart rate To prevent this situation, data normalization is applied
(100-250 bpm) and usually regular heart rhythm. It is most which means adjusting measured values to a common scale.
often seen in young people and infants. Alcohol, illicit drugs, The first step is to find a baseline (mean value of the record)
caffeine, and smoking might be a cause of this arrhythmia. and subtract it from the record values. And after that divide
Another abnormal heart rhythm is premature ventricular record values by the absolute value of the record. So the final
complex (PVC) characterized by long QRS complexes (> scale would be in the range [-1; 1].
0.12ms), ST segment and T wave changes (irregular, inverted Band pass filter
in leads with a dominant R wave, etc).
Ventricular fibrillation (V-Fib) is a life-threatening ECG signal might be altered with the noise of different
abnormal heart rhythm when the lower chambers quiver and sources. This might be electrical signals coming from human
the heart can’t pump blood to the body. This occurs because body activity (usually electromyography signals from
of electrical signal disorder which makes chambers contract muscles contraction), procedure artifacts (eg. connecting/
very fast in an unsynchronized way. It requires immediate disconnecting electrodes), power line interference and
treatment and can usually be stopped with a defibrillator. instrumentation noise.
Power line interference is not less represented in hand-held
III. DATA PREPROCESSING devices, and usually, occurs through inductive mechanism.
Handling of imbalanced data The power lines across the world have the frequency of 50Hz
or 60Hz, which is higher than the subject information
The data in selected dataset is quite imbalanced which frequencies (0.1Hz to 40Hz). So it can easily be eliminated
might create problems during training. The model might by filters as presented on Fig. 2.
easily overfit and predict all of the time the most represented Both electrode contact noise and electrode motion noise
category. This creates a so-called accuracy paradox: suppose cause baseline changes that occur due to variation in the
that we have a dataset with two labels A and B and 90% of position of the heart with the respect to the electrodes. More
the records are instances of A, the classifier might predict all specifically, the amplitude of the changes is defined by
the time class A which will give the accuracy of 90% variation of electrode-skin impedance (ESIV). The larger
although it definitely won’t be a good classifier. There are ESIV, the smaller change of impedance needed to cause a
several common approaches here: use abnormalities detection shift in the baseline. This kind of noise is not defined by any
algorithms or apply class weights or perform data balancing. particular range of frequencies and it isn’t easy to eliminate it
The first approach was applying class weights as a with signal processing, the task is to perform the recording
logarithmic function of the proportion of total instances procedure without patient disturbance.
divided by the number of class instances. Electromyography (EMG) noise is caused by contraction
𝜇𝜇∗𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 of muscles, not related to the heart activity (moving of hands
𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐ℎ𝑡𝑡 = 𝑚𝑚𝑚𝑚𝑚𝑚 �𝑙𝑙𝑙𝑙𝑙𝑙 � � , 1�
𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 and/or legs). For the hand-held devices usually, the highest
where μ - parameter to tune EMG noise component is waves generated by depolarization
and repolarization of hand muscles (as ECG signal goes from
heart to the device directly through hands). EMG signal
frequency is stochastic in nature, but the significant activity
ACIT 2018, June 1-3, 2018, Ceske Budejovice, Czech Republic
3
104
happens in the range of 5Hz and 450Hz. This partially it looks for the position of the higher value. This position is
overlaps with the frequencies of the ECG signal so it’s hard marked as potential R-peak location.
to eliminate it absolutely. In particular, the problematic areas R-peaks than go through false rejection algorithm which
are P and T waves of the ECG. checks the average length of the RR-interval, and rejects
those R-peaks, where the length of RR-interval is less than
half of the average.
Fig.4. Detection of R-peaks
There is also additional search back algorithm which again
looks on RR-intervals and finds positions where there might
be a missed R-peak (RR-interval is higher than 1.5 of the
average). There it lowers the threshold and runs the search
algorithm once again.
Heartbeat extraction is done with the static window. By
taking in consideration that normal heart rhythm is in the
Fig.2. Signal preprocessing with low-pass and high-pass band filters range of 60 to 100 beats per minute, we considered static
So to remove most of the noise the combination of low windows of sizes from 0.5s (120 bpm) to 0.7s (86 bpm). The
pass and high pass bands filters were used. The range of window size that served best was 0.6s with the distribution of
frequencies to be kept is from 0.1Hz to 40Hz. 0.15s for P-wave, 0.1s for QRS complex and 0.35s for ST
The SciPy filter function in combination with NumPy interval and T-wave.
convolve function was used to implement both filters. For each R-peak detected, the heartbeat template was
extracted with the static window of 0.6s which includes 0.2s
IV. FEATURE SELECTION before and 0.4s after R-peak.
Feature selection includes:
1. QRS detection.
2. Heartbeat extraction.
3. Features extraction
The task of QRS detection is to find R-peaks which
represent the heart beats. Beat detection is a procedure
preceding any kind of ECG analysis and so it is critical to
correctly detect heart beats.
One of the standard algorithms used for QRS detection is
a Pan-Tompkins algorithm. The original paper states
detection rate of 99.3%. Many modifications were made to
the algorithm since its publication including additional
filtering, search back support, outliers rejection etc. Fig. 3. Extracted heartbeat
The filtered signal is introduced as an input to the During the model development process, many features
algorithm. The processing of the data starts with a first- were considered as good for prediction. To analyze the noise
derivative filter, which helps obtain information about the of the data, transformation coefficients were used. In
slope of the QRS. particular, first 400 coefficients of Fourier transformation and
details coefficients of 4-level wavelet decomposition.
Another range of useful features is heart rate variability. It
is computed on RR-intervals, which includes time domain
components (RMSSD, SDNN, NN20, PNN20, NN50,
PNN50, mean RR, std RR, mean HR) and frequency domain
Fig.3. Signal before and after derivative filter components.
Frequency domain components are computed for 3
To get even better intensity of the slope, the squaring with frequency bands: very low frequencies (0-0.04), low
normalization is applied. This gives a graph where most of frequencies (0.04-0.15) and high frequencies (0.15-0.4). To
the values are near to zero, and in the positions of R peaks, it compute frequencies powers, Welch estimation of power
has a value much higher (usually over 0.5). spectral density is performed on interpolation of RR-intervals,
Additionally moving average is applied, which combines and the total values of power bands are computed by
two close peaks on the graph above into one. After that, the integrating (with composite trapezoidal rule) function of
algorithm finds positions on the graph that are higher than the frequencies and powers.
average (referenced threshold). And for all of those positions
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105
Additionally, minimum and maximum RR-interval lengths normal sinus rhythm, ‘2’ – is other heart rhythm and ‘3’ – is
are added to the feature vector. noisy signal. Precision is defined as the number of true
The extractor also computes the proportion of the number positives divided by the number of true positives and false
of R-peaks detected divided by the length of the record, mean positives and is a measure of a class exactness. Recall is the
and standard deviation of normalized R-peak values, number, number of true positives divided by the sum of a number of
and proportion of detected R-peak values with the inverse true positives and false negatives and it expresses the
sign. measure of classifier completeness. F1 score is used as a
Based on the templates of heart beats the module finds a model estimate. F1 measure is defined a combination of
median heartbeat as it best represents the most common specificity and sensitivity:
template of heartbeat. Median was used rather than mean 2 ∗ 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 ∗ 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟
because it is more noise resistant. The extractor also 𝐹𝐹1 =
𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 + 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟
computes the standard deviation of each point of templates. It can be used to express the average of classifier exactness
V. CLASSIFICATION and completeness. Scoring algorithm include F1 metrics only
for 3 categories: atrial fibrillation, normal rhythm, and other
One of approaches to ECG classification was based on rhythm:
decision trees. The bagging was used from the very 𝐹𝐹1(𝐴𝐴𝐴𝐴) + 𝐹𝐹1(𝑁𝑁) + 𝐹𝐹1(𝑂𝑂)
beginning with the random forest algorithm. 𝐹𝐹1 =
3
Random forest implies divide-and-conquer principle,
which makes it easy to execute in parallel. This makes So the final score of the model is an average of F1 scores
training process very fast (a couple of seconds). This is for 3 classes.
particularly good for experiments with the trees structures
optimizations. For classification process was selected VI. CONCLUSIONS
Random forest algorithm which consists of 60 decision trees. This paper shows the ECG automated analysis problem
The implementation of the algorithm is taken from the Scikit- and one of suitable algorithms for this – Random forest. Was
learn library. Results of classification process for random described the main steps of ECG signal preprocessing which
forest based model shown in table 1. includes handling of imbalanced data, data normalization and
TABLE 1. SCORES FOR RANDOM FOREST MODEL. noises filtering. Also presented QRS complexes detection and
the main features for ECG classification.
Class
Precision Recall f1-score
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ACIT 2018, June 1-3, 2018, Ceske Budejovice, Czech Republic