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A clustering based system for instant detection of cardiac abnormalities from
compressed ECG
Fahim Sufi
⇑
, Ibrahim Khalil
1
, Abdun Naser Mahmood
2
RMIT University, School of Computer Science and Information Technology, 123 Latrobe St., Melbourne, VIC 3000, Australia
article info
Keywords:
Cardiac abnormality classification
Compressed ECG
CVD diagnosis
Symmetricity of bi-class clustering
CVD alert mechanism
abstract
Compressed Electrocardiography (ECG) is being used in modern telecardiology applications for faster and
efficient transmission. However, existing ECG diagnosis algorithms require the compressed ECG packets
to be decompressed before diagnosis can be applied. This additional process of decompression before per-
forming diagnosis for every ECG packet introduces undesirable delays, which can have severe impact on
the longevity of the patient. In this paper, we first used an attribute selection method that selects only a
few features from the compressed ECG. Then we used Expected Maximization (EM) clustering technique
to create normal and abnormal ECG clusters. Twenty different segments (13 normal and 7 abnormal) of
compressed ECG from a MIT-BIH subject were tested with 100% success using our model. Apart from
automatic clustering of normal and abnormal compressed ECG segments, this paper presents an algo-
rithm to identify initiation of abnormality. Therefore, emergency personnel can be contacted for rescue
mission, within the earliest possible time. This innovative technique based on data mining of compressed
ECGs attributes, enables faster identification of cardiac abnormalities resulting in an efficient telecardiol-
ogy diagnosis system.
Ó2010 Published by Elsevier Ltd.
1. Introduction
Electrocardiogram (ECG) signal has significantly been used for
diagnosing Cardiovascular Diseases (CVD), which have been the
number one killer of modern time. The existing diagnosis algo-
rithms are mostly suited for plain ECG signals (i.e. not in com-
pressed form) that work by detecting the ECG fiducial points,
namely P, Q, R, S and T (Friesen et al., 1990; Hamilton & Tompkins,
1986; Sufi, Fang, & Cosic, 2007; Surez, Silva, Berthoumieu, Gomis, &
Najim, 2007) (as shown in Fig. 1). After detecting the ECG fiducial
points, the existing ECG diagnosis algorithms employ computa-
tionally intensive processing to ascertain particular cardiac
anomalies.
According to existing research on mobile phone based telecardi-
ology application, the death rate associated with CVD can be tack-
led by harnessing the processing power of mobile technologies
(Blount, 2007; Hung & Zhang, 2003; Lee, Chen, Hsiao, & Tseng,
2007). More recent set of research confirms that the usage of
specially designed compression technologies can result in a faster
telecardiology solutions (Istepanian & Petrosian, 2000; Kim, Yoo,
& Lee, 2006; Sufi, Fang, Mahmoud, & Cosic, 2006; Sufi, Fang, Khalil,
& Mahmoud, 2009; Sufi & Khalil, 2008). However, if the ECG pack-
ets remain in compressed format during data transmission and
storage, then existing ECG diagnosis algorithms cannot be applied
directly. The compressed ECG must be decompressed before apply-
ing most of the CVD detection algorithms (Friesen et al., 1990;
Hamilton & Tompkins, 1986; Surez et al., 2007; Sufi et al., 2007).
If a hospital has hundreds of remotely monitored (real-time) CVD
patients then the hospital server might have to perform this addi-
tional task of decompression for millions of compressed ECG pack-
ets per second. Therefore, this added process of decompression
may create enormous computational burden on existing infra-
structure. To mitigate the computational burden imposed by com-
pression technology, research in Sufi et al. (2009) demonstrates a
new set of CVD diagnosis algorithms that works on compressed
ECG directly (i.e. decompression of the compressed ECG packet is
no longer required). However, the techniques of detecting cardiac
abnormality from compressed ECG presented in Sufi et al. (2009)
engages a rule based algorithm for detection of a particular disease.
In order to identify all the cardiac abnormalities, the presented sys-
tem in Sufi et al. (2009) requires hundreds of complex algorithms
to be integrated under one computationally intensive system.
Maintaining and updating such complex system for every new
abnormality is difficult.
This introduces the problem of finding a simple and fast
solution towards heart abnormality detection from compressed
0957-4174/$ - see front matter Ó2010 Published by Elsevier Ltd.
doi:10.1016/j.eswa.2010.08.149
⇑
Corresponding author.
E-mail addresses: research@fahimsufi.com (F. Sufi), ibrahimk@cs.rmit.edu.au (I.
Khalil), abdun.mahmood@rmit.edu.au (A.N. Mahmood).
1
Tel: +61399252879.
2
Tel: +61399251902.
Expert Systems with Applications 38 (2011) 4705–4713
Contents lists available at ScienceDirect
Expert Systems with Applications
journal homepage: www.elsevier.com/locate/eswa
ECG that raises alert to the cardiac specialist as soon as a cardiac
abnormality is detected.
In this paper, we present a simple but efficient data mining
based solution that detects an abnormality from the compressed
ECG instantly. This technique can be placed within a wireless mon-
itoring facility to alert the emergency personnel in an event of car-
diac abnormality of a subscribed patient.
2. Background
Human heart is responsible for maintaining oxygenated blood
circulating throughout our body, by beating about 100,000 times
per day. A human heart contains four chambers: two atria and
two ventricles. The deoxygenated blood initially enters the right
atrium. The right atrium contracts and forces the deoxygenated
blood to the right ventricle. From the right ventricle the oxygen
deficit blood rushes to the lungs, where gas exchange process takes
place and blood attains oxygen (releases carbon dioxide). The oxy-
genated (i.e. oxygen enriched) blood then enters the left atria, from
where it is redirected to the left ventricle. Finally, the left ventricle
forces the blood to the rest of the body. Both the atria contracts to-
gether, and on the other hand both the ventricular contraction oc-
curs at the same time.
An ECG signal, representation of the electrical activity of the
heart, has three major features waves; namely P wave, QRS com-
plex and T wave (as seen from Fig. 1). An atrial contraction results
in a P wave and a ventricular contraction is reflected by a QRS com-
plex. T wave, on the other hand, represents ventricular relaxation
that occurs after ventricular contraction. Cardiologists have used
different features of these feature waves to assess the condition
of the heart (see Tables 1).
As seen from Fig. 2, patient is attached with a portable ECG
acquisition device, which collects ECG signal from the patient’s
body and transmits ECG packets to the mobile phone via Bluetooth,
Wifi, Near Field Communication (NFC) or Zigbee protocol. Mobile
phone then compresses and encrypts the ECG packets and
forwards them (i.e. compressed and encrypted packets) to the
Time / Samples
A
mplitude
P
Q
R
S
T
Fig. 1. The proposed cardiac diagnosis system.
Table 1
ECG Features related to P wave, QRS complex and T wave.
P wave duration QRS complex duration T wave duration
P wave amplitude QRS complex amplitude T wave amplitude
P wave onset slope Q onset slope T wave onset slope
P wave offset slope Q offset slope T wave offset slope
QT Interval R onset slope P wave direction
RR Interval R offset slope T wave direction
ST Segment S onset slope
RR Interval S offset slope
ECG Acquisition
Device
Patient’s mobile
phone compresses
and encrypts the
ECG packets
ECG acquisition device
to mobile communication
via Bluetooth, NFC, Zigbee
or Wifi
Monitoring service / hospital employs
Data Mining Agent to detect abnormality
Ambulance or rescue team is
notified when abnormality
is detected by the Data
Mining Agent
Patient’s mobile phone transmits
the ECG packets via HTTP, MMS or
SMS to the hospital / monitoring
service
Fig. 2. Architecture of the data mining based compressed ECG diagnosis system.
4706 F. Sufi et al. / Expert Systems with Applications 38 (2011) 4705–4713
hospital or monitoring services via HTTP or MMS. The monitoring
services execute a background monitoring agent implementing
data mining techniques. This mobile phone based compressed
ECG transmission has been proposed and used in our earlier re-
search works (Sufi, 2007; Sufi et al., 2009, 2006; Sufi & Khalil,
2008).
However, for this paper we are adding a data mining module
(situated in the hospital) for identification of CVD abnormality
from compressed ECG sent by the patient, using clustering tech-
niques. These data mining techniques use the knowledge of what
is normal and what is abnormal from the monitored patient’s
ECG. The input and output to the mining agent are the compressed
ECG and a Boolean type denoting abnormality, respectively. There-
fore, for this telemonitoring solution, if the compressed ECG is de-
rived from a normal ECG, output of the data mining agent would be
negative. In case of abnormal ECG signal from the patient, the
agent will output positive detection, signalling abnormality and
alert mechanism would be activated in such a case.
3. Architecture of the proposed disease identification system
In remote telemonitoring, massive amount of ECG data is trans-
ferred (Sufi, Khalil, Fang, & Cosic, 2008), and therefore, adoption of
specialized compression technology (as demonstrated in our ear-
lier research in Sufi et al. (2009) & Sufi & Khalil (2008)) is often re-
quired. Our ECG compression technique uses the encoding function
() that transforms the ECG signal, X
n
to a compressed ECG, C
r
(Eq.
1). The lossless nature of our ECG compression technique ensures
that ECG features set, F(a subset of ECG signal X
n
as shown in
Eq. (2)) also exists within the encoded (or compressed) ECG C
r
(Eq. 3). New algorithm can be designed to reveal these encoded
ECG feature set for CVD diagnosis directly from the compressed
ECG.
As an example, Fig. 3 shows a normal ECG segment for Entry ID
CU1 of CU Ventricular Tachyarrhythmia database (Physiobank,
2009). Fig. 4 demonstrates the initiation of abnormality (i.e. Ven-
tricular Tachyarrhythmia) for that particular patient. Lastly, Fig. 5
depicts a complete episode of Ventricular Tachyarrhythmia for
the same patient. Fig. 6 shows the compressed ECG (i.e. com-
pressed using our specialized ECG compression algorithm (Sufi
et al., 2009; Sufi & Khalil, 2008)) of Figs. 3–5. Eq. (1) represents
the fact that Fig. 6 preserve the ECG features of Figs. 3–5. Within
this paper, our proposed idea is to harness data mining routines
for efficient detection of CVD anomalies (i.e. cardiac abnormality)
0 200 400 600 800 1000
−1
0
1
2
3
Fig. 3. A normal ECG segment of a patient (a random CU1 entry MIT BiH CU
Ventricular Tachyarrythmia Database).
0 200 400 600 800 1000
−2
0
2
4
Fig. 4. Initiation of abnormality (Ventricular Tachyarrythmia) with the ECG
segment for (CU1).
0 200 400 600 800 1000
−2
−1
0
1
2
Fig. 5. An abnormal (Ventricular Tachyarrythmia) ECG segment of a patient (CU1).
Fig. 6. Compressed ECG for Fig. 3 (normal ECG), Fig. 4 (normal and abnormal) and Fig. 5 (abnormal ECG).
F. Sufi et al. / Expert Systems with Applications 38 (2011) 4705–4713 4707
directly from compressed ECG (e.g. the compressed ECG shown in
Fig. 6).
ðX
n
Þ¼C
r
ð1Þ
FX
n
ð2Þ
FC
r
ð3Þ
During the compression process, 148 characters and numeric
values (0–9) are used to encode the plain text ECG signal, as seen
in Fig. 7 (ECG compression is performed inside patient’s mobile
phone). The data mining agent (DMA) of the hospital (Fig. 2) needs
to be trained with normal and abnormal ECG (from compressed
ECG) of patients. After being trained, the DMA can be tested for
irregularities (abnormal ECG). Our proposed algorithm (Algorithm
1), instantly identifies abnormal ECG segments (directly from the
compressed ECG).
3.1. Training of the proposed model
During this training phase, the proposed model learns what is
normal ECG and what is abnormal ECG. Fig. 8 shows the main
stages of this learning process from compressed ECG.
3.1.1. Character frequency calculation
As shown in Fig. 8, from the compressed ECG, the frequency of
each encoded characters is computed first. There are about 148
characters and 6 numeric subgroups for which the frequencies
are generated (Fig. 7). The frequency of these 157 character (and
numeric sub groups) are utilized as the attributes for clustering.
However, 157 attributes are too many for generating clusters (nor-
mal and abnormal ECG). Therefore, the attribute subset selection is
necessary. Using proven techniques, we first select characters from
the compressed ECG that are mainly responsible for identifying
diseases. Then, based on the selected characters (or attributes)
classification of abnormality and normality is possible.
3.1.2. Attribute subset selection
Data pre-processing using attribute selection is an important
step in data mining, since a large number of attributes often lead
to poor learning due to untenably large combinatorial search space
for the solution (Han & Kamber, 2006). The goal of feature subset
selection is to (a) reduce the dimensionality of the data to be ana-
lysed, (b) to speed up execution of learning algorithms, (c) improve
performance of data mining techniques including learning time
and predictive accuracy, (d) improve the comprehensibility of the
output. Recent studies have shown that attribute subset selection
helps improve the performance of clustering algorithms with re-
duced attributes (Sufi & Khalil, 2009; Talavera, 1999a, 1999b). In
this paper, we have adapted for use with continuous ECG signals,
a correlation based feature subset selection technique ( Hall,
1999; Sufi & Khalil, 2009), which outperforms other feature selec-
tion algorithms, such as ReliefF (Kira & Rendell, 1992) and RReliefF
(Robnik-Sikonja & Kononenko, 1997). The attribute selection is
based on an attribute’s relative utility with regards to the predicted
class as well as taking into consideration its correlation with other
attributes in the subset. The utility of an attribute can be repre-
sented using the Pearson’s co-efficient for correlation, where the
variables are standardized as in Eqs. (4) and (5)
r
xy
¼Pðx
i
xÞðy
i
yÞ
ðn1Þ
r
x
r
y
ð4Þ
U
S
¼Cr
ap
ðCþCðC1Þr
a
a
Þ
1
2
ð5Þ
wherex
i
andy
i
aresamplemeancalculatedfromthedata,
r
x
and
r
x
are
the standard deviations, a;
a2S;C jSj;r
xy
A
v
eragecorrelation
between features xand y. For a subset Sof Cfeatures, the utility
function calculates how much the features ða;
aÞare related r
ap
to the predicted class p, while being less related to each other r
a
a
.
The utility function reduces the effect of irrelevant attributes
as they are less correlated with the predicted class. It also
discards redundant attributes as they are highly correlated with each
other.
We used a greedy best first algorithm to search through the
candidate subsets for a locally optimal solution. The algorithm ini-
tiates with an empty subset, adding one attribute at a time and
estimating the utility function, to determine the correlation of
the subset with the predicted class. The next attribute is added
as long as the utility value does not decline for the best subset. If
there is a decrease then the algorithm selects the next best subset
and commences adding attributes to it. In some datasets where
there are groups of features that are locally predictive to the pre-
dicted class, we investigate the attributes that were initially dis-
carded while building the best subset. In this case, after the best
subset has been generated, the algorithm investigates the rejected
list of attributes one-by-one and evaluates its correlation to the
predicted class against the average correlation to the subset. If its
correlation to the class is greater than its correlation with the attri-
bute subset, signalling a stronger attraction to the class than the
subset, then the attribute is incorporated in the subset.
Fig. 7. 157 characters and numeric sub groups (attributes) used for generating
compressed ECG (from plain ECG signal). Details of this character substitution
based compression techniques have been described in Sufi et al. (2009), Sufi and
Khalil (2008).
Compressed ECG Attribute Subset
Selection
Clustering of
Reduced Attributes
Detection of Normal
and Abnormal Clusters
Data Mining Techniques
Calculate Frequency
of Each Characters
Fig. 8. Step by step procedure of the proposed cardiac abnormality detection technique.
4708 F. Sufi et al. / Expert Systems with Applications 38 (2011) 4705–4713
3.1.3. Automatic learning of normal and abnormal patterns using
clustering of compressed ECG features
Using the smaller subset of attributes we can now produce a
cluster from the normal compressed ECG patterns. This cluster of
normal patterns would serve as the benchmark test against future
ECG sent from the observed client. Under normal circumstances
any incoming ECG would closely match the stored cluster. How-
ever, if there is any abnormality then the clustering algorithm
would create a different cluster from the abnormal ECG. This will
generate an alarm and require urgent attention of a physician or
a cardiologist. It should be noted that procedure given in this paper
works solely on the compressed ECG character frequency, and does
not even require decompression, which would take valuable extra
time from a patient’s life.
The aim of clustering is to group a given set of objects so that
similar objects (also known as cases, instances or patterns) are
grouped together and dissimilar objects are kept apart. Although
there are many different techniques to build multi-dimensional
clusters (Mahmood, Leckie, & Udaya, 2008), we have chosen a sta-
tistical clustering technique called Expectation Maximization (EM)
(Han & Kamber, 2006) to cluster compressed ECG data, since it can
be used to find the correct number of clusters automatically.
Assuming two clusters Aand B, representing normal and abnormal
class of ECG, we describe the steps for EM clustering for two
clusters:
1. Choose model parameters mean
l
, standard deviation
r
and
probability of clusters parbitrarily for clusters Aand B.
2. For each iteration j, calculate the probability that instance I
belongs to clusters Aand B:
PðAjIÞ¼p
j
A
P
j
ðIjAÞ
P
j
ðIÞ;PðBjIÞ¼p
j
B
P
j
ðIjBÞ
P
j
ðIÞð6Þ
The probability of P(IjA) can be modelled using any distribution
function. For the commonly used Gaussian distribution that we
have adopted in this paper, it can be given by
PðIjAÞ¼ 1
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ð2
p
Þ
r
A
pexp
ðI
l
AÞ2
2
r
2
ð7Þ
3. Update the mixture parameters on the basis of the new
estimates:
P
jþ1
B
¼P
I
PðAjIÞ
n;P
jþ1
B
¼P
I
PðBjIÞ
nð8Þ
l
jþ1
A
¼P
I
IPðAjIÞ
P
I
PðAjIÞ;
l
jþ1
B
¼P
I
IPðBjIÞ
P
I
PðBjIÞð9Þ
r
jþ1
A
¼P
I
PðAjIÞðI
l
jþ1
A
Þ
2
P
I
PðAjIÞð10Þ
r
jþ1
B
¼P
I
PðBjIÞðI
l
jþ1
B
Þ
2
P
I
PðBjIÞð11Þ
4. Calculate the log likelihood value E
j
¼P
I
logðP
j
ðIÞÞ. Consider a
fixed stopping criterion
, then if jE
j
E
j+i
j6
, then stop; else set
j=j+1.
EM can decide how many clusters to create by cross validation
(as is the case in the present study), or it may be specified a priori
(normal and abnormal clusters). In the depicted scenario of Fig. 2,
the patient continuously sends the compressed ECG information to
the hospital, which clusters the new information and checks to see
if there are two clearly segregated clusters. In cases where the
compressed ECG falls under abnormal cluster (or inclines towards
abnormal cluster), as shown in Fig. 12, abnormality is detected. If
such an abnormality is observed then an immediate alarm is
raised, since the ECG pattern has been found to be significantly
0 500 1000
−2
0
2
4
0 500 1000
−2
0
2
4
0 500 1000
−2
0
2
4
0 500 1000
−2
0
2
4
0 500 1000
−2
0
2
0 500 1000
−2
0
2
0 500 1000
−2
0
2
0 500 1000
−2
0
2
0 500 1000
−2
0
2
0 500 1000
−2
0
2
0 500 1000
−2
0
2
4
0 500 1000
−2
0
2
4
0 500 1000
−5
0
5
0 500 1000
−5
0
5
0 500 1000
−5
0
5
0 500 1000
−5
0
5
0 500 1000
−2
0
2
4
0 500 1000
−2
0
2
4
0 500 1000
−2
0
2
4
0 500 1000
−2
0
2
4
Fig. 9. 20 randomely selected ECG segments for CU1 entry (from CU Ventricular Tachyarythmia – MIT BIH).
F. Sufi et al. / Expert Systems with Applications 38 (2011) 4705–4713 4709
different from normal patterns. In our experiments, the EM algo-
rithm has been successful in isolating the normal and abnormal
compressed ECG with remarkable accuracy (100%) using the 20
ECG segment dataset.
3.2. Instant abnormality detection from compressed ECG
Once the proposed model is trained, we know the cluster cen-
ters (or means) for all the selected attributes (for the classes). With
this knowledge, whenever a new compressed ECG is sent by the
patient, the DMA calculates the frequency of selected characters
(selected attribute in training stage). These inputs (attribute values
of the instance) are fed along with the cluster centers to Algorithm
1, which determines initialization of abnormality.
During an initialization of abnormality, we expect the com-
pressed ECG packet to contain both normal and abnormal ECG.
Therefore, for these initialization of abnormality packets, distances
from normal cluster centers (for the selected attributes) will start
to increase. Abnormality can be signalled, once the distance be-
tween the instance (initialization ECG packet) and normal cluster
0 100
0
50
100
0 100
0
50
100
0 100
0
50
100
0 100
0
50
100
0 100
0
50
100
0 100
0
50
100
0 100
0
50
100
0 100
0
50
100
0 100
0
50
100
0 100
0
50
100
0 100
0
50
100
0 100
0
50
100
0 100
0
50
100
0 100
0
50
100
0 100
0
50
100
0 100
0
50
100
0 100
0
50
100
0 100
0
50
100
0 100
0
50
100
0 100
0
50
100
Fig. 10. Frequency distribution of the 20 randomly selected ECG segments for CU1 entry (of Fig. 9). Boxed region shows high frequencies of attribute 115–131 denoting
abnormality from the compressed ECG.
Table 2
Selected characters (first half attributes) and their respective frequencies in compressed ECGs (normal) for 13 different instances.
At. N1 N2 N3 N4 N5 N6 N7 N8 N9 N10 N11 N12 N13 CCtr
@ 7 10 8 7 7 5 10 8 9 8 10 9 7 8.0769
$ 68551015974911 610 8.0769
Ø 596588625812 9 7 6.9231
Å 8 10 8 12 9 9 15 9 18 11 9 10 6 10.3077
å 5 12 14 11 7 7 5 11 7 7 15 12 11 9.5385
_ 69978172765 812 88
[ 5357479428 5 5 4 5.2308
] 13 9 5 8 10 7 7 8 6 12 4 10 12 8.5385
j15 13 11 11 11 15 8 17 11 11 8 5 7 11
Æ 8 14 10 6 7 8 15 12 8 11 12 8 13 10.1538
& 14 13 10 10 8 10 9 7 12 11 6 8 8 9.6923
( 7 11 5 15 11 10 11 12 10 14 6 13 9 10.3077
*47779656614 5 8 6 6.9231
: 12 8 8 13 8 8 11 5 8 8 9 13 8 9.1538
; 12 14 6 12 10 13 15 13 10 6 11 10 9 10.8462
ü11512776867612 8 2 7.4615
Á 9 9 5 11 14 7 8 9 12 7 13 8 13 9.6154
Ë 5033312432 2 6 6 3.0769
k 14 8 7 11 9 4 11 5 10 9 9 6 6 8.3846
l 9644596948 3 7 7 6.2308
m 7678735446 6 4 7 5.6923
o 2842163413 3 4 5 3.5385
r 10 14 7 11 9 9 12 11 8 16 13 11 8 10.6923
s191099996897 811 6 9.2308
4710 F. Sufi et al. / Expert Systems with Applications 38 (2011) 4705–4713
mean goes beyond a threshold value. After the detection of abnor-
mality initialization, the emergency personnel can be contacted for
the rescue of the patient (Fig. 2).
4. Results and discussion
Fig. 9 shows 20 different segments of ECG for CU1 entry of CU
Ventricular Arrythmia database (Physiobank, 2009) in a matrix for-
mat. Sub-Figs. 1–3 ([1,1], [1,2] and [1,3]) of Fig. 9 are normal ECG
segments. Sub Fig. 4 or [1,4] shows initiation of ventricular arryth-
mia. Sub Figs. 5–10 represent continual cardiac abnormality (Ven-
tricular Tachyarrythmia episode). The rest of the sub figures of
Fig. 9 show normal ECG segments for patient CU1. It should be
noted that for our proposed architecture (in Fig. 2), plain ECG (as
in Fig. 9) is not viewed anywhere. Fig. 9 only serves the purpose
of understanding the concept behind this paper.
As shown in Fig. 8, we only receive compressed ECG from which
the frequencies for all attributes (Fig. 7) are calculated. After calcu-
lating frequencies of the 157 attributes from the compressed ECGs
of Fig. 9, we can observe that certain group of characters have dif-
ferent frequency bands for normal and abnormal ECGs. Fig. 10
illustrates the fact that Sub Figs. 3–10 have notably higher frequen-
cies for attributes 115–131 (for character set {[t–z],[A–J]}). How-
ever, these sub figures (3–10) actually correspond to abnormal
ECG. Therefore, Fig. 10 represents the fact that certain compressed
character frequencies behave differently for abnormal ECG.
However, rather than manual inspection of the characters
responsible to signal abnormality, an accurate and automated
attribute selection procedure is highly desirable. Our attribute
selection process on 20 different instances provides us 48 key char-
acters or attributes that are shown in the left column of Tables 2–5.
Based on these 48 attributes, we generated cluster with previously
Table 3
Selected characters (last half attributes) and their respective frequencies in compressed ECGs (normal) for 13 different instances.
At. N1 N2 N3 N4 N5 N6 N7 N8 N9 N10 N11 N12 N13 CCtr
t 6665667564 5 6 2 5.3846
u 3263135243 4 1 5 3.2308
v 4441253131 3 1 3 2.6923
w 0131233224 4 0 12
x 0 4 2 0 3 1 0 1 0 2 0 0 2 1.1538
y 2 0 0 4 0 2 0 1 2 0 0 0 1 0.9231
z 0 2 2 1 1 3 0 1 2 2 0 1 1 1.2308
A 0 1 1 2 0 0 1 2 0 2 1 0 1 0.8462
B 3 0 2 2 3 2 0 1 3 1 1 0 0 1.3846
C 13200000100000.5385
D 1 0 0 2 2 2 0 0 1 3 2 1 0 1.0769
E 0 1 2 0 0 2 0 1 1 0 1 0 3 0.8462
F 1220113210 0 1 0 1.0769
G 1011222122 2 3 4 1.7692
H 0 0 0 0 0 0 0 0 2 0 0 1 0 0.2308
I 0 1 0 1 0 1 0 1 1 0 0 1 1 0.5385
J 0 1 0 0 0 0 2 1 2 0 0 1 0 0.5385
K 0110020000 1 0 0 0.3846
L 0 0 0 0 0 0 0 2 0 0 0 1 0 0.2308
M 0 1 0 1 0 1 2 0 0 1 2 0 1 0.6923
N 0010000000 0 0 1 0.1538
O 0 0 0 0 0 1 0 0 1 1 0 0 0 0.2308
R 0 0 0 1 1 0 0 0 1 0 0 0 1 0.3077
50–100 31 35 30 36 30 31 29 29 34 34 26 29 31 31.1538
Table 4
Selected characters (first half attributes) and their respective frequencies in
compressed ECGs (abnormal) for seven different instances.
At. An1 An2 An3 An 4 An5 An6 An7 CCtr
@ 24 50 47 53 52 46 48 45.7143
$ 6 3 1 1 2 1 2 2.2857
Ø 4 1 1 1 1 0 0 1.1429
Å 4 3 3 1 1 2 1 2.1429
å 4 2 5 0 2 2 0 2.1429
_ 0 2 2 1 3 2 1 1.5714
[ 2 2 0 1 0 0 0 0.7143
] 1 6 1 2 0 0 0 1.4286
j5 4 0 3 0 0 0 1.7143
Æ 4 3 3 3 3 1 1 2.5714
& 6 1 2 3 2 3 0 2.4286
( 3 1 0 3 0 2 1 1.4286
*0 0 3 2 1 3 1 1.4286
: 2 1 1 0 1 0 1 0.8571
; 5 2 1 4 3 0 1 2.2857
ü 5 2 1 0 2 1 0 1.5714
Á 5 3 3 0 1 1 0 1.8571
Ë 0 0 1 1 1 0 1 0.5714
k 14 13 13 12 19 14 12 13.8571
l 14 17 11 9 14 6 11 11.7143
m 8 16 16 14 15 15 16 14.2857
o10131815 26201016
r 32 34 44 33 47 37 26 36.1429
s 33 39 32 29 54 38 26 35.8571
Table 5
Selected characters (Last half attributes) and their respective frequencies in
compressed ECGs (abnormal) for seven different instances.
Att. An1 An2 An3 An 4 An5 An6 An7 CCtr
t 14 34 34 34 28 28 21 27.5714
u 21 36 28 40 37 39 34 33.5714
v 19 29 29 31 31 26 27 27.4286
w 16333619 30303228
x 15 37 26 30 33 33 20 27.7143
y 17 28 24 19 37 35 31 27.2857
z 15 48 23 39 41 33 34 33.2857
A 8 31 20 20 21 27 17 20.5714
B 15 23 27 22 30 25 23 23.5714
C 14 28 21 21 19 36 16 22.1429
D 9 21 26 13 23 25 16 19
E 10 9 19 26 14 31 21 18.5714
F 17231927 13212020
G 25 46 39 41 40 63 52 43.7143
H 7 17 15 17 11 18 19 14.8571
I 7 14 8 20 4 19 14 12.2857
J 6 8 13 12 12 16 15 11.7143
K 5 8 12 13 8 14 13 10.4286
L 398126132210.4286
M 48712310178.7143
N 2782 27196.7143
O 3 3 12 6 2 9 16 7.2857
R 3444 1574
50–100 15 13 15 49 6 9 42 21.2857
F. Sufi et al. / Expert Systems with Applications 38 (2011) 4705–4713 4711
described EM methodology. EM generates 2 clusters with 100%
accuracy when the clusters are compared (or cross-validated) to
the known class (abnormal ECG segment and normal ECG seg-
ment). It is worth mentioning that EM was not informed about
the number of clusters (i.e. 2). The log likelihood measured by
EM, after creation of 2 clusters based on the 48 selected attributes,
is 100.27906. Tables 2 and 3 show the frequency of these charac-
ters on the 13 different instances for normal ECG. On the other
hand, Tables 4 and 5 show 7 instances of abnormalities. For all
the tables cluster means or centers (right most columns for Tables
2–5) are distant. Also, for normal and abnormal cases, the respec-
tive attributes show affinity towards their corresponding class
means. Fig. 11 shows the difference in normal and abnormal ECGs
for the selected 48 attributes. Unlike Fig. 10, where 16 characters
show visual distinction (from 115 to 131), Fig. 11 shows clear dis-
tinction of 48 automatically selected attributes.
Algorithm 1. Detection of the abnormality initialization
//Notation Description:
//Input: Attribute values for all the instances
//Input: Cluster means of the 2 clusters for all the attributes
//Output: The most equidistant instance
Step 1
Create distance vector, A
j
and B
j
for
Cluster 1 and 2, where jis the number of instances
A
j
¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
X
I
i¼1
f
j
i
C
1
i
2
r
B
j
¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
X
I
i¼1
f
j
i
C
2
i
2
r
here, f
i
is the attribute value vector for all I
attributes and C
1
i
and C
2
i
are the centroid
vectors of cluster means 1 and 2 (normal & abnormal)
and i=1,2,3,...,Iis the number of attributes
Step 2
Symmetricity metric is generated by normalizing the
difference in distance vectors for the 2 clusters
S
j
¼jA
j
B
j
j
MaxðA
j
;B
j
Þ
Step 3
The most equidistant instance, Rhas the lowest value of S
j
S
R
¼MinðS
j
Þ
This study was the first demonstrated in Sufi and Khalil (2009)
and enhanced in current work to show the feasibility of an auto-
mated alert mechanism based on data mining techniques and com-
pressed ECG designed to save lives of monitored CVD patients.
Now that we can observe two distinct clusters for normal and
abnormal compressed ECG segments, the question of belonging
arises for the compressed ECG segments that contain half normal
ECG and half abnormal ECG. For example, this situation can be ob-
served for the case of 4th sub figure of Fig. 9 (row 1, column 4). This
initialization of abnormality is also depicted in Fig. 4. It should be
mentioned again that for the sake of clarity of this paper, original
ECG segments are shown in Figs. 4 and 9. However, in real moni-
toring scenario, only compressed ECGs are dealt by the patient
and the DM agent (in Fig. 2). For this initialization of abnormality
case (Fig. 4), we logically expect it to be equidistant from the
two clusters, as this particular segment contains both normal and
abnormal ECG. To represent this fact, in a two dimensional coordi-
nate is not straight forward, as we are dealing with 48 attributes
and each attribute provides individual decision of belonging to-
wards a particular cluster.
To represent the fact that compressed ECG packets containing
both normal and abnormal ECG are nearly equidistant from both
the clusters, in two dimensional coordinate, we define the concept
of symmetricity of instances in a bi-class clustering. An instance is
said to be symmetric with respect to a bi-class clustering, when the
location of the instance is nearly equidistant from both the cluster
centroids.
Algorithm 1 basically determines the instance, which is equidis-
tant from both the classes. In first step, Algorithm 1 calculates the
cluster distances for all the 20 instances of the example case (i.e.
distance from normal cluster, A
j
and distance from abnormal clus-
ter, B
j
). For this examples case, cardinality of A
j
and B
j
is 20
(jA
j
j=jB
j
j= 20).
Fig. 11. Normal and abnormal cluster means.
Normal Abnormal
1, 2, 3, 11,
12, 13, 14,
15, 16, 17,
18, 19, 20
5, 6, 7,
8, 9, 10
4
Initiation of
abnormality
Fig. 12. Segregation of normal and abnormal ECG (in two different clusters).
4712 F. Sufi et al. / Expert Systems with Applications 38 (2011) 4705–4713
Using step 2 of Algorithm 1, we can also ascertain our proposed
symmetricity metric, S
j
for the 20 instances of our example case (as
seen from Table 6). We can clearly see that the most equidistant
case, Ris the 4th (4th subplot of Fig. 9 or Fig. 4) case. Therefore,
R=4asS
4
=Min(S
j
), where, j=1,2,3,...,20.
Algorithm 1 can clearly identify the initialization of abnormal-
ity, and as soon as the algorithm detects shifts from normal cluster,
it can notify the emergency personnel for assistance of the moni-
tored patient. This paper serves as a proof of concept to show that
cardiac abnormality can be detected directly from the compressed
ECG with the application of data mining technique like EM.
Fig. 12 shows the fact that sub Fig. 4 of Fig. 9 (or Fig. 4) is equi-
distant (being more closer to abnormal cluster) from the 2 clusters
(according to Algorithm 1), even though it belongs to abnormal
cluster according to EM. Other instances (or compressed ECG seg-
ments) are clearly identified as a member of normal or abnormal
clusters.
5. Conclusion
In this paper, we have used data mining techniques like CFS
based attribute selection and EM based clustering to instantly de-
tect cardiac abnormalities of the CVD affected subjects. The pro-
posed DM driven CVD detection framework detects ECG
abnormalities without incurring delays, which is ideal for CVD af-
fected patients, as every second counts towards the mortality of
these patients (Luca, Suryapranata, Ottervanger, & Antman,
2004). For detecting the ECG anomalies, our proposed model does
not have to decompress the compressed ECG.
CVD related deaths being the number one killer of modern
times, our proposed instant ECG anomaly detection algorithm
has the potential to save the life of a CVD affected patient. This is
due to the fact that without proper monitoring (i.e. real-time
ECG monitoring demonstrated in Lee et al. (2007), Hung & Zhang
(2003), Blount (2007), Sufi et al. (2009), Sufi & Khalil (2008)), 40%
of the patients having their first symptom of CVD might be dead
within years (Access Economics Pty Limited (2008)).
According to our experimentation on MIT-BIH entries (Physio-
bank, 2009), 100% accuracy can be achieved in detecting cardiac
abnormality from compressed ECG. However, in this paper, we
had focused on essentially two clusters (i.e. normal and abnormal)
that can only determine abnormality. To know the type of the
abnormality (e.g. Ventricular Fibrillation, Atrial Fibrillation, Prema-
ture Ventricular Beat, etc.), a multicluster system, where each clus-
ter represents one particular disease, needs to be implemented in
the future.
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Table 6
A
j
,B
j
and S
j
values for the 20 ECG segments. The Fourth value of S
j
is the lowest (i.e. S
4
= 0.24573771) signaling equidistant from both normal and abnormal clusters (i.e. initiation
of abnormality).
A
j
= {17.30530722,12.36044732,13.77612638, 72.26985027,134.7904569, 120.5433543,125.0978411, 137.5469333,144.1012035, 123.8610014,12.08989901,
9.406054328,16.00034556,14.48650607, 12.22903341, 13.18553215,14.27789403, 15.17882927, 12.32616964, 13.55373225}
B
j
= {118.8628916,118.3491715,117.4859846, 54.51042274,29.27631103, 23.41155244,38.1514297, 37.79026049,35.32039337, 38.08583448,121.1030833,
120.3706758,119.8604513,119.2606355, 120.2970672, 118.8009722,119.3707786, 118.8893224, 120.5503405, 120.1616153}
S
j
= {0.854409505,0.895559494, 0.882742385,0.24573771, 0.782801307, 0.805783134, 0.695027273,0.725255521, 0.754891753, 0.692511492, 0.900168529,
0.921857593,0.866508549,0.878530699, 0.898343046, 0.88901158,0.880390375,0.87232807, 0.897750852, 0.887204144}
F. Sufi et al. / Expert Systems with Applications 38 (2011) 4705–4713 4713
