The document describes a machine learning framework for real-time arrhythmia detection from electrocardiogram (ECG) signals. The framework first trains a random forest classifier on historical ECG data to identify different types of arrhythmias. It then uses the trained classifier in real-time to analyze new ECG signals and advise physicians on the presence or absence of arrhythmias. The framework addresses challenges like class imbalance in the training data and handles missing values. Experimental results showed the framework can accurately detect arrhythmias in real-time to help physicians make timely treatment decisions.

1. INDUSTRY EXPERIENCE
REAL-TIME
ANALYTICS FOR
THE HEALTHCARE
INDUSTRY:
ARRHYTHMIA
DETECTION
Vijay Srinivas Agneeswaran, Joydeb Mukherjee,
Ashutosh Gupta, Pranay Tonpay,
Jayati Tiwari, and Nitin Agarwal
Impetus Infotech Private Limited, Bangalore,
Karnataka, India
Abstract
It is time for the healthcare industry to move from the era of ‘‘analyzing our health history’’ to the age of
‘‘managing the future of our health.’’ In this article, we illustrate the importance of real-time analytics across the
healthcare industry by providing a generic mechanism to reengineer traditional analytics expressed in the R
programming language into Storm-based real-time analytics code. This is a powerful abstraction, since most data
scientists use R to write the analytics and are not clear on how to make the data work in real-time and on highvelocity data. Our paper focuses on the applications necessary to a healthcare analytics scenario, specifically
focusing on the importance of electrocardiogram (ECG) monitoring. A physician can use our framework to
compare ECG reports by categorization and consequently detect Arrhythmia. The framework can read the ECG
signals and uses a machine learning-based categorizer that runs within a Storm environment to compare different
ECG signals. The paper also presents some performance studies of the framework to illustrate the throughput and
accuracy trade-off in real-time analytics.
Introduction
The healthcare industry is undergoing a major transformation. The old days of using paper records of patients’
data are gone with the digitization of healthcare information,
starting with the use of electronic health records (EHRs). The
use of EHRs is becoming widespread, partly dictated by financial stimulus and partly by governmental regulations.
The healthcare industry is now turning to the use of data
analytics. The pace is likely to pick up with the advent of the
Affordable Care Act (ACA), or ‘‘Obamacare,’’ which promises
to transform the healthcare industry from fee-for-service to
fee-for-value. Moreover, due to the widening of the eligibility
requirements and affordability, more people will come into
the system for healthcare. This implies the need for big-data
analytics, especially for the mandated health exchanges.
The Affordable Care Act has also spurred many innovations
in healthcare—this is evident in the number of healthcare
startups funded recently, such as the following (this list is
only indicative, not intended to be complete, and is biased
toward health analytics):
1. Health catalyst, which provides analytics suite to analyze
EHRs.
2. xG health solutions, which provides analytics of population health as well as reporting and interpretation.
Editor’s Note: Impetus supports multiple venues for dialogue in big data, providing thought leadership and services to create new ways to analyze
data to gain key opportunities in business and industry across enterprises. The following is a description of one potential application of their
expertise in machine learning within the healthcare space.
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2. INDUSTRY EXPERIENCE
Agneeswaran et al.
3. Lumeris, which uses real-time analytics of healthcare data
to improve patient care, essentially focused on making
ACA work for all players including health systems, payers,
and providers.
4. Eviti, which provides physicians with actionable information using analytics for cancer related decision making.
5. Humedica, which uses data from multiple sources including EHRs, claims data, etc. to help healthcare providers analyze patient data as well as population data.
6. HealthTap, which provides a social platform for physicians and patients to share information as well as build a
peer reputation.
comparison between the two common devices, the loop event
monitoring and the mobile cardiac outpatient telemetry
system, and their effectiveness in detecting arrhythmias.
Machine Learning–Based Classification
of ECG Data
The classifier we have developed works in two modes: the
training mode (or learning mode) and the operational mode
(or advisory mode). In the training mode, we extract features
(i.e., variables or transformed variables) in terms of which
A number of startup accelerators include Nanthealth, Rockarrhythmia types, including its absence, can be represented
health, Healthbox, and Blueprint Health Services, among others.
and we learn the parameters of the inference mechanism
about the occurrence or nonoccurThis article presents a different scerence of a type of Arrhythmia. In
nario requiring real-time analytics of
this mode, the results cannot advise
‘‘THE OLD DAYS OF USING
big data, and as an example, applies
the doctor, but rather, the input
cutting edge big data technologies to
about the label (i.e., type of arPAPER RECORDS OF
historical data. The electrocardiorhythmia or absence of it) correPATIENTS’ DATA ARE GONE
gram (ECG) signal provides critical
sponding to each record provided is
WITH THE DIGITIZATION OF
information about the heart activity
used for training (see Fig. 1).
HEALTHCARE INFORMATION,
of a patient. Continuous monitoring
of ECG is important when a patient
Once the training is complete, the
STARTING WITH THE USE
is ambulatory or at the bedside. It is
classifier goes into operational
OF ELECTRONIC HEALTH
very important to treat arrhythmic
mode, meaning it begins advising
RECORDS’’
patients on time, as delays can lead
the doctor on new, unseen, but
to potentially fatal complications.1
similar cases to those seen during
training. The doctor arrives at an
Arrhythmia detection from ECG
inference about the presence or absence of arrhythmia taking
signals is a well-studied problem. For instance, Gao et al.1
the output of the classifier into consideration. Also, if arsolve it by using an artificial neural network approach based
rhythmia is present, which type it is can be suggested by the
on a Bayesian framework. Rothman and colleagues2 make a
FIG. 1.
ML Based Classification of ECG Data: Training Mode.
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3. ARRHYTHMIA DETECTION: REAL TIME ANALYTICS
Agneeswaran et al.
classifier. The various types of arrhythmia classes (labels) will
be listed in a subsequent section. This mode of operation is
depicted in Fig. 2.
The input to machine learning algorithm is a set of historic
patient records. Clinical measurements recorded in the past
from ECG signals, namely, QRS duration, RR, P-R, Q-T intervals constitute such records, along with information such
as gender, age, and weight. This data is padded with the
categorical label a cardiologist had assigned to each record,
such as ‘‘normal’’ or one of the 15 types of pathology categories. These make up a total of 279 features as enumerated
by Guvenir et al.3
Class names and description
Class distribution:
Database:
Arrhythmia
Class code: Class:
01
02
03
04
05
06
07
08
09
10
11
12
13
14
15
16
Normal
Ischemic changes
(Coronary Artery Disease)
Old Anterior Myocardial Infarction
Old Inferior Myocardial Infarction
Sinus tachycardy
Sinus bradycardy
Ventricular Premature
Contraction (PVC)
Supraventricular
Premature Contraction
Left bundle branch block
Right bundle branch block
1. degree AtrioVentricular
block
2. degree AV block
3. degree AV block
Left ventricule hypertrophy
Atrial Fibrillation or Flutter
Others
FIG. 2.
178BD
Number of
instances:
245
44
15
15
13
25
3
2
9
50
Description of dataset
We analyzed a dataset containing 452 records belonging to
patients coming from different age groups, weights, heights
and gender (see http://archive.ics.uci.edu/ml/datasets/
Arrhythmia for more information).
There are in all 280 variables, including various arrhythmia
class types as the 280th column, in the database downloaded
from the source.1 Values for this column can be 1 to 16,
representing one of the codes as enumerated above. There are
5 categorical variables and 274 numeric variables.
Five variables had missing values in their records as enumerated below. These variables occurred in columns 11 to 15
in the original dataset.
Vector angles in degrees on front plane of:
11 T
8 values missing
12 P
22 values missing
13 QRST
1 value missing
14 J
376 values missing
Number of heart beats per minute
15 Heart rate
1 value missing
Some of the variables had ‘‘0’’ throughout the column (i.e.,
across all records). Those variables are enumerated below
with their column number followed by the variable name
20
70
132
0
0
4
5
22
140
144
‘‘DI S-prime Wave’’; 68 ‘‘AVL S-prime Wave’’
‘‘AVL Existence of ragged R wave’’; 84
‘‘AVF Existence of ragged P wave’’
‘‘V4 Existence of ragged P wave’’; 133 ‘‘V4 Existence
of diphasic derivation of P wave’’
‘‘V5 S-prime Wave’’, 142 ‘‘V5 Existence
of ragged R wave’’
‘‘V5 Existence of ragged P wave’’; 146 ‘‘V5 Existence
of ragged T wave’’
ML Based Classification of ECG Data: Operational Mode.
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152
158
205
275
‘‘V6 S-prime Wave’’ ; 157 ‘‘V6 Existence
of diphasic derivation of P wave’’
‘‘V6 Existence of ragged T wave’’;
165 ‘‘DI Amplitude S-prime Wave’’
‘‘AVL Amplitude S-prime Wave’’;
265 ‘‘V5 Amplitude S-prime Wave’’
‘‘V6 Amplitude R-prime Wave’’
The first four columns in the original datafile had non-ECG
variables as follows:
1
2
3
4
Age: Age in years , linear
Sex: Sex (0 = male; 1 = female) , nominal
Height: Height in centimeters , linear
Weight: Weight in kilograms , linear
Classification algorithm
We chose the random forest (RF) classifier2 for several reasons:
it is fast (training time); its OOB-error (out-of-bag errors) is a
good estimate for generalization error; it can handle noisy data;
it can suggest ‘‘important variables,’’ using which, a parsimonious predictive model can be built; and it has an imputation
method associated with it which at times is better choice than
using any other external methods for imputation. Additionally,
two or more separately trained RFs can be combined without
incurring much computational expenditure, and it is an ensemble classifier (i.e., a collection of classifiers), which predicts
by counting votes cast by each classifier for a class on a query
record. Predictive performance of an ensemble classifier is better
than any of its constituents. The constituent classifiers for RF are
classification trees. The advantage of using such classifiers is that
individual classifiers may be barely accurate (slightly better than
random guessing) but combining trees may produce classifiers
with much higher accuracy. Also, a great deal of variance may be
present as we move from one tree to another, but the overall
classifier’s variance is reduced because of averaging that takes
place in the course of ‘‘ensembling.’’
RF is trained by bagging (bootstrapped aggregation) of training data. Random samples ‘‘with replacement’’ are drawn from
the training data and classification trees are built using them. If
large numbers of trees are constructed (1–1/e) 63% of the
original data are used therein, the remaining 36% are used for
testing the trees to calculate OOB-errors. It can be shown that
this error is a fair indication of generalization error for the RF
classifier. Generalization error measures predictive performance of classifiers when tested with unseen data outside of
the training set but supposedly generated from the same distribution as that of the training data. These will be the kind of
data encountered by the classifier in the operational mode.
The keys to the predictive performance of RF classifier are the
strength of individual classifiers and the diversity (degree of
uncorrelatedness) of constituent classification trees in the
forest in terms of raw margin functions.4
Imputation of missing values
In the exploratory data analysis (EDA) phase, it was found
that important variables such as ‘‘heart rate’’ as measured in
MARY ANN LIEBERT, INC. VOL. 1 NO. 3 SEPTEMBER 2013 BIG DATA
number of heart beats per minute, had some missing values.
A couple of imputation algorithms were tried out,5,6 and
finally rfImpute from the randomForest package was chosen
to impute those missing values. Amelia7 was not considered
because it could produce imputed results only with a high
value of prior information [with ‘‘empri’’ parameter value as
high 0.9*nrow(data), when usually 0.01* nrow(data) is used].
The latter amounts to adding lot of artificial observations
with the same mean and variance of existing observations but
with 0 covariance.
Imbalance of data with respect to classes
The gross imbalance in the dataset (Table 1) poses problems for
selecting a subset of data to be used for training and testing. If
the training and testing sets are typically partitioned (70%–80%
for training and 30%–20% for testing), classification performance will be misleading. There are several ways to partially
address this problem. Generating artificial data for the minor
classes (via SMOTE algorithms and associated packages)8 is one
method. Another means is to down-sample data from the major
class. We have chosen the latter path [i.e., subsampling the
major class (Normal class) in proportion to the minor class
(those classes that had at least 10% data)]. While subsampling
the major class, we made sure that its maximum number did not
exceed 100% that of the minor class. Furthermore, weights were
used for the training examples supplied to the RF classifier.
Classes that had single-digit representation namely, Left ventricule hypertrophy (0.9%), Atrial Fibrillation or Flutter (1.1%),
Ventricular Premature Contraction (PVC) (0.7%), Supraventricular Premature Contraction (0.4%), and Left bundle branch
block (1.9%) were not addressed.
Variable selection for model building
Variable Selection plays a major role in the development of
predictive models. In this study, one of the reasons for selecting RF classifier over other alternatives was that it has a
means of assessing the effectiveness of each variable occurring
in the model, using which we can build a parsimonious model
for the deployment. The criteria based on which RF ranks its
‘‘Important Variables’’ are ‘‘Mean Decrease Accuracy’’ and
‘‘Mean Decrease Gini.’’ We prefer the latter for selecting the
important variables, because in some instances in the literature, it has been reported that the other measure is not stable.9
All variables with a Mean Decrease Gini value greater than its
mean value will be retained in the model, in our case by setting
the criterion threshold to its mean value (see Fig. 2). The
complete variable list with descriptions is provided in the online reference (http://archive.ics.uci.edu/ml/machine-learningdatabases/arrhythmia/arrhythmia.names).
Experimental Results and Discussion
We performed experiments on the classifier we developed to
assess its predictive performance. We enumerate the steps of
the algorithm for classification using RF below:
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Agneeswaran et al.
1. Read comma-separated values of Arrhythmia data from text file as table.
2. Identify and create a response variable showing which class datapoints belong to (280th column of original data read as
table).
3. Make sure data is complete:
Identify the columns with missing values.
Replace the missing values (occurring as ‘‘?’’) with NA (required for imputation).
4. Assign names of the Variables (for ease of identification).
5. Get rid of variables with zero entries, age, sex, height, and weight and the one specifying Arrhythmia Type (i.e., non-ECG
values). (For imputation, we cannot afford to retain so many variables with so few records. One of the imputation methods
used, Amelia, does not permit it.)
6. Perform Imputation with rfImpute/Amelia.
7. Sample imputed data judiciously (as described previously) from respective classes up to the maximum number of records it
contains except for the Normal Class (code 01). For this class try out number of records 100, 90, 80, and 70.
Toss a biased coin to generate indices between 1 and number of records (rows) in the ratio 70:30.
Generate training and test set using above indices.
8. Call Random Forest with imputed data and number of tree = 500 and other parameters.
9. Call Predict function on the test set of data.
10. Identify the important variables according to the specified criterion (MeanDecreaseGini) at specified threshold value (Set
equal to the Mean of MeanDecreaseGini).
11. Call Random Forest with important variables and training set of data and number of tree = 500 and other parameters.
12. Call Predict function on the test set of data.
13. Go back to step 7 until the list (100, 90, 80, 70) is exhausted.
Table 1 shows the computation of precision and recall, which
can be defined below as follows:
recall ¼
Precision: the number of correctly classified examples of a
particular class divided by the number of examples labeled by
the system as belonging to that particular class.10
precision ¼
jfcorrect À labelsg fpredicted À labelsgj
jcorrect À labelsj
F-score: a combination of the above two measures in the
form of harmonic mean.
F-Score ¼
jfcorrect À labelsg fpredicted À labelsgj
jpredicted À labelsj
2 · precision · recall
precision þ recall
As the system keeps operating in the field, more records for
the various cases will be collected, together with the cardiologists’ decisions for the respective records. A new RF
classifier may be trained with these data and finally it can be
Recall (sensitivity): the number of correctly classified examples of a particular class divided by the number of examples
of that particular class in the data.
Table 1: Precision/Recall Computation
Number of records Class 1 (precision, recall, Class
major class
f-score as defined below).
2
90
80
70
180BD
96.43
58.69
72.97
78.26
52.94
63.15
89.29
78.12
83.33
89.47
65.38
75.55
Class
3
Class
4
66.67 100.0
50.0
71.40 75.0 100.0
68.97 85.71 66.7
72.73 75.0
33.33
53.33 50.0
50.00
61.54 60.0
39.98
71.43 66.67 50.0
55.55 100
50.0
62.53 80.0
50.0
75.00
0.00 83.33
70.59
0.00 83.33
72.73
0.00 83.33
Class
5
Class
6
Class
9
33.33
100.0
49.96
33.33
100.0
49.96
50.0
100.0
66.67
33.33
100.0
49.96
85.71
75.00
79.99
87.5
87.5
87.5
75.00
90.00
81.82
80.00
88.80
84.17
100.0
100.0
100.0
66.67
100.0
79.99
100.0
100.0
100.0
100
100
100
Class With all variables With important variables
10
100-OOB-error
100-OOB-error
62.50
66.66
64.53
80.00
54.54
64.86
55.56
83.33
66.71
86.67
68.42
76.47
67.29
70.10
71.01
72.46
64.50
66.50
61.66
64.25
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combined with the one currently operating incrementally
using the combine() function of randomForest.
Implementation of R-based Classifier
for Real-Time Analysis
‘‘R’’ code can be executed from within a bash script, which
allows us to invoke it from within a Java program (or any
programming language or script for that matter). Storm is
an open-source real time computation framework, which
allows us to process streams of data in a parallel fashion
making it a very good choice for classification of data on a
cluster of nodes. A Storm topology consumes streams of
data and processes those streams in arbitrarily complex
ways, repartitioning the streams between each stage of the
computation.
The model file created in the previous step is referenced in
another ‘‘R’’ script, which is used for real-time classification.
Data to run classification on enters the storm framework via a
Spout which then emits it to the bolts. Each bolt runs ‘‘R’’
script in parallel and emits results of the classification (which
can get captured and used as needed) as shown in Fig. 3.
Note that for each result in Table 2, one node is a Nimbus node
and the remaining are supervisors. Each node is an 8—has 8
quad-core CPUs, 32 GB of RAM, and 32 GB of swap space.
Table 2. ECG Classification Performance Analysis
Time taken (in seconds)
Number
of predictions
(ECG
categorizations)
20K
40K
0.1 million
Sequential
processing
(no-Storm
used)
Storm
cluster with
2 nodes
(1 spout,
8 bolts)
Storm
cluster with
3 nodes
(1 spout,
16 bolts)
3,600
7,200
18,300
900
1,710
4,440
450
900
2,400
Note: We made use of only one Spout for this POC. Depending on the mechanisms of data entry into Storm
framework, it is possible to use multiple spouts, which would
enhance performance further.
Concluding Remarks
This article has presented a real-time machine-learning
platform for the healthcare domain that allows ECG signals to
be classified. It is an additional input for the physician, but a
crucial one that facilitates care-for-value. The implication is
that this work provides the basis for building a powerful
analytical framework that can work in real-time—this study
could prove extremely useful, not only for ECG classification,
but also for enabling physicians to get incremental analytics
on various kinds of patient data increasingly available in
the EHRs. Our study also enables incremental healthcare,
where the focus can shift to analytics, and consequently, to
customized real-time healthcare. The upcoming health exchanges may also benefit, as on-the-fly analytics on highvelocity data becomes essential for providers, physicians, and
patients equally.
Author Disclosure Statement
All authors are employed by Impetus.
References
FIG. 3.
Running R over Storm.
MARY ANN LIEBERT, INC. VOL. 1 NO. 3 SEPTEMBER 2013 BIG DATA
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Address correspondence to:
Vijay Srinivas Agneeswaran, PhD
Innovation Labs
Impetus Infotech India Private Limited
Pritech Park SEZ, Bellandur Outer Ring Road
Bangalore, Karnataka 560103
India
E-mail: vijay.sa@impetus.co.in
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