APPLICATION OF 1D CNN IN ECG CLASSIFICATION

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 09 Issue: 05 | May 2022 www.irjet.net p-ISSN: 2395-0072
© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 1342
APPLICATION OF 1D CNN IN ECG CLASSIFICATION
ABHISHEK SHARMA 1, MAHANTHI ABHISHEK 2REDDY TRIVENI 3 GUSIDI SANTOSHI4
GUIDED BY MR.GANESH- ASSISTANT PROFESSOR
1,2,3,4 Final Year B.Tech, CSE, Sanketika Vidya Parishad Engineering College, Visakhapatnam, A.P, India .
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Abstract - The machine-driven detection of suspicious
anomalies in graph (ECG) recordings permits frequent
personal heart health observation and might drastically cut
back the quantity of ECGs that require to be manually
inspected by the cardiologists, excluding those classified as
traditional, facilitating health care decision-making and
reducing a substantial quantity of your time and cash. during
this paper, we tend to gift a system able to mechanically find
the suspect viscus pathologies in graph signals from personal
observation appliances, desiring to alert the patient to send
the graph to the MD for an accurate designation and correct
medical aid. the most contributions of this work ar (a) the
implementation of a binary classifier supported a 1D-CNN
design for detective work the suspect anomalies in ECGs,
notwithstanding the type of viscus pathology; (b) the analysis
was applied on twenty one categories of various viscus
pathologies classified as anomalous; and (c) the likelihood to
classify anomalies even in graph segments containing, at an
equivalent time, over one category of viscus pathologies.
Moreover, 1D-CNN-based architectures will enable
implementation of the system on low-cost good devices with
low machine knottiness. The system was tested on the graph
signals from the MIT-BIH graph heart diseaseinfofortheMLII
derivation. 2 numerous experiments were applied, showing
outstanding performance compared to alternative similar
systems. the simplest result showed high accuracy and recall,
computed in terms of graph segments, and even higher
accuracy and recall in terms of patients alerted, thus
considering the detection of anomaliesregardingentire graph
recordings. Dropout is a technique where randomly chosen
neurons are ignored during training. They are “dropped out”
randomly. This means that their assistancetotheactivationof
downstream neurons is temporallyremovedontheahead pass
and any weight updates are not applied to the neuron on the
backward pass.
Key Words: ECG signal detection; portable monitoring
devices; 1D-convolutional neural network; deep
learning, Dense and Dropout
1. INTRODUCTION
The aging of the population is guiding to a rise in patients
stricken by internal organ pathologies, so requiring medical
instrument observance. associate degree ECG (ECG) is a
simple, rapid, and non-invasive tool that tracesthe electrical
activity of the center revealingthepresenceofinternal organ
pathologies like conductivity sickness, channelopathies,
structural cardiovascular disease, andformeranemia injury.
On the opposite hand, investigation the altered acoustic
characteristics of the internal organ tones, as associate
degree example, might enable the first identificationofvalve
malfunction. Systems able to support the doctors’ add the
diagnosing of pathologies will facilitate health care higher
cognitive process reducing significantly expenditureofyour
time and cash. The ECG has become the process most
typically accomplished in clinical medicineandtherefore the
diffusion of wearable and transportable devices has been
sanctionative patients to perpetually monitor their internal
organ activity, for instance, elder individuals through
wireless device networks . Cardiologists cannot examine
countless ECGs daily recorded from transportable devices.
Thus, systems able to mechanically sight suspicious
anomalies in ECGs square measure needed, to scale back the
quantity of ECGs that require to be manually examined by
the cardiologists, characteristic people who want an extra
examination and additionally the urgency of such
examination. For this reason, systems need high detection
performance to avoid that ordinary ECGs incorrectly
detected as abnormal ought to be examined by a medical
skilled, and, even a lot of vital, the presence of associate
degree medical instrument alteration, that may well be
associate degree indicator of internal organ pathology, is
recognized and doesn't escape the observation of the heart
surgeon. to create the abnormal ECGs be examined by the
doc which the correct medical care is run, the detection
system ought to maximize recall for abnormal ECGs, that is
to maximise the quantity of ECGs properly classified as
abnormal, even losing accuracy. the longer term of fast and
economical sickness diagnosing lies withinthedevelopment
of reliable non-invasive strategies additionally through the
employment of computer science techniques. Artificial
neural networks and deep learning architectures have
recently found broad applications achieving placing success
in several domains like image classification, speech
recognition intrusion detection systems, smart city, and
biological studies. Therefore, high expectations square
measure placed on the employment of such techniques
additionally for the advance of health care and clinical
observe. what is more, varied transportable devices for
private and frequent observance of internal organ activity,
like Kardia, D-hearth, and need, square measure spreading.
The goal of this paper is to implement a system able to
mechanically sight the suspect internal organ pathologies in
ECG signals to support personal observancedevices.wetend
to propose a 1D-CNN design optimized to sight abnormal

ECG recordings, in spite of the sort of internal organ
pathology, as well as within the analysis of twenty one
categories of anomalies. The system here projected was
designed to be enforced on devices for private use and to
solely send to the heart surgeon ECGs detected with the
suspect of a internal organ alteration for any examination,
therefore no info regarding the particular category of
anomaly is detected. The projected system relies ona binary
classification model. In fact, as of now, we wish to create it
clear that the most goal of our study isn't to classify
completely different internal organ pathologies, however to
create positive that the suspect pathology may be detected
which patients square measure alarmed: then an accurate
diagnosing may be carried on with specific tests and
therefore the intervention of medical employees. The heart
surgeon can study all ECGs detected as abnormal
characteristic the pathology and prescribing the correct
treatment. The system has been enforced to realize high
levels of recall for abnormal ECGs to attenuatethelikelihood
that the presence of any quite internal organ alteration may
escape the observation of the heart surgeon.
2. BACKGROUND
Many studies have projected the implementation of artificial
neural networks and deep learning architectures for the
event of automatic systems able to acknowledge suspect
viscus anomalies. withintheliterature,thedetectionofviscus
anomalies has been investigated by analyzing each hearts
sounds noninheritable by digital stethoscopes and ECG
signals from moveable devices. Meintjes et al. enforced
continuous riffle remodel (CWT) scalograms and
convolutional neural networks for the right classification of
the elemental heart sounds in recordings of traditional and
pathological heart sounds. They enforced a strategy to
differentiate between the primary and second heart sounds
exploitation CWT decomposition and convolutional neural
network (CNN) options. Results show the high potential
within the use of CWT and CNN within the analysis of heart
sounds compared to support vector machine (SVM), and k-
nearest neighbors (kNN) classifiers. In authors propose the
classification of heart sounds on short, nonsegmental
recordings and normalized spectralamplitudeoffiveslength
phonocardiogram segments were determined by quick
Fourier remodeland riffle entropy by riffle analysis. Spectral
amplitude and riffle entropy options were then combined
during a classification tree. They achieved accuracy
corresponding to alternative algorithms obtained while not
the quality of segmentation. Redlarski et al. bestowed a
replacement heart sound classification technique combining
linear prophetic writing coefficients, used for feature
extraction, with a classifier designed upon combining a
support vector machine and also the changed cuckoo search
algorithmic rule. It showed smart performance of the
diagnostic system, in terms of accuracy,complexity,andvary
of distinguishable heart sounds. With the appliance of deep
learning architectures, additionally the accuracy of ECG
diagnosticanalysishasachievednewhighlevels.Thesystems
enforced exploitation such techniques enable the automatic
interpretation of ECG signals from moveable devices in
period of time. The common deep learning networks for the
analysis of ECG signals area unit chiefly supported repeated
neural networks (RNNs), convolutional neural networks
(CNNs), and a few alternative architectures. Chauhan et
al.investigated the relevance of deep repeated neural
network architectures with long immediatememory(LSTM)
for detection viscus arrhythmias in ECG signals. This
approach is sort of quick, doesn't need preprocessing of the
information or hand-coded options, and doesn't want
previous data regarding the abnormal signal. The network
was tested on the MIT-BIH cardiopathy info for the
classification of 4differingtypesofArrhythmiasshowingthat
LSTMs could also be a viablecandidateforanomalydetection
in ECG signals. Saadatnejad et al. projected Associate in
Nursing LSTM-based ECG classification algorithmic rule for
continuous viscus observance on wearable devices. The
preprocessed information extracting RR interval and riffle
options from ECG samples. The ECG signal beside the
extracted options was fed into multiple LSTM repeated
neural networks. The MIT-BIH ECG cardiopathy info was
used for the classification of six differing types of anomalies.
The projected algorithmic rule achieved correctLSTM-based
ECG classification to wearable devices with low procedure
prices. shaft et al. bestowed Associate in Nursing
unsupervised statistic anomaly sightion algorithmic rule to
detect anomaliesinECGreadings.Theyperformedarepeated
LSTM network to predict {the traditional |thetraditional|the
conventional} time-series behaviorwhilenottheusageofthe
anomaly category labels building a variable normal error
model for the nominal information. abnormal events were
detected with a high chance through a high Mahalanobis
distance. They classified six anomalycategoriesandobtained
smart performance achieving high levels of exactness and
recall.
3. ECG SIGNAL
The mechanical pumping activity of the guts muscle is
decided by the singsong generation of AN electrical impulse
that originates at the amount of the heart muscle and,
through specialisedconductivitypathways,spreadstoanyor
all muscle cells inflicting cycles of depolarisation and
repolarization underlying the contraction of single cells.
electrocardiogram is that the graphic replica oftheelectrical
activity of the guts throughout its functioning, recorded at
the surface of the body. The doctor, typically a specialist

specialist, interprets the medical instrument recording by
police work the presence of viscus arrhythmias, structural
changes within the viscus cavities, atria and/or ventricles,
ischemia, infarction, and alternative cardiopathies,
characterised by AN alteration of conductivity. A beat of
electrocardiogram signals will be determined by 5
characteristic waves—P, Q, R, S, and T, whereverevery wave
is said to a particular interval of the polarization–
depolarization cycle. The characteristic of the traditional
electrocardiogram is thatvariessolelywithinthepresenceof
issues. the basic morphology of the electrocardiogram is
given by 3 deflections (P, QRS, and T), that represent the
formation and diffusion of the viscus electrical impulse on
the pathways of the conductivity system
Figure 1. Tracing of a normal electrocardiogram (ECG)
including P wave, QRS complex, and T wave
4. MATERIALS AND METHODS
4.1. Dataset
The projected technique was tested on the MIT‐BIH cardiac
arrhythmia info equipped by PhysioNet, an online resource
for complicated physical signals databases . The MIT‐BIH
cardiac arrhythmia info contains forty eight forty eight
extracts of two‐channelambulantgraphrecordings,obtained
from forty seven subjects: twenty five males aged between
thirty two and eighty nineyearsandtwentytwofemalesaged
between twenty three and eighty nine years. As represented
by the authors in , the info consists of twenty three
recordings at random elite by a collection of 4000 24‐h
ambulant graph recordings collected from a mixed
population of hospitalized (approximately 60%) and
ambulant (approximately 40%) patients at letter Israel
Hospital in Hub of the Universe. The remaining twenty five
recordings were elite from constant set to contain less
common however clinically vital arrhythmiasthatwillnotbe
delineated in a very little random sample. The recordings
were digitized at 360 samples per second per channel with
associatedegreeassociatedegreebitresolutionovera10‐mV
vary. For our analysis, we have a tendency to used constant
knowledge printed by Kaggle in txt and CSV format, since
they were easier to method .The info contains twenty two
categories, one fora standardbeat, and twentyoneforvaried
forms of anomalies in graph recordings.
4.2. DATA ORGANIZATION
Data were structured in a very tabular kind to be processed
by the neural network. The ECGs area unit area unit
recordings together with main derivations, varied among
subjects. In most ECGs, one channel could be a could be a
(MLII) and also the different channel is mostly V1, generally
V2, V4, or V5, looking on the topic. For this reason, since the
MLII issort of gift in each graph, wehave a tendency to solely
thought of this lead.Four graph recordings, solelycontaining
leads V1 and V5, were excluded from the analysis. The
30‐min graph recordings were fragmented into segments of
fifteen s. Since the recordings were digitized at 360 samples
per second, every section consisted of 5400 militia
Based on the annotations assigned to the peaks gift, every
section was tagged as follows: if within the section all peaks
were annotated as traditional then the complete section was
tagged as normal; if within the section a minimum of one
peak was annotated as abnormal, presenting any quite
anomalies, then the complete section was tagged as
abnormal. within the dataset,every row described a element
of fifteen s tagged as traditional or abnormal. tagged as
traditional were 2105 segments and3175asabnormal.Since
the dataset bestowed unbalanced categories showing a
proportion bias, we tend to undersampled the segments
tagged as abnormal to stay solely a locality of those
information, therefore reconciliation the coaching set. The
abnormal segments in excesswereenclosedwithinthecheck

set. As are elaborated later, we tend to administered 2
distinctexperiments,preprocessinginformationinnumerous
ways that. within the 1st experiment, segments were
unsystematically enclosed within the coaching set,
exploitation associateequalproportion of segmentsforthe2
categories. The coaching set consisted of 2930 segments,
1465 tagged as traditional,and1465taggedasabnormal.The
coaching set was split into seventieth for coaching and half-
hour for validation, keeping a similar proportion of
traditional and abnormal segments. Segments contained
within the check set bestowed a definite proportion of the 2
categories, togetherwith640taggedastraditional(60%)and
426 as abnormal(40%). samples oftraditionalandabnormal
ECG recordings area unit shown in Figure a pair of. Table
three shows the quantity of ECG segments for every
traditional or abnormal category. The segments classified as
abnormal will embody one or additional sorts of anomalies.
4.3 DENSE AND DROPOUT
Dropout is a technique used to prevent a modelfromover
fitting. Dropout works by randomly setting the outgoing
edges of hidden units to 0 at each update of the training
phase.
The effect is that the network becomes less sensitive to the
specific weights of neurons. This in turn results in a network
that is capable of better generalization and is less likely to
overfit the training data
5. . RESULTS AND DISCUSSIONS
we performed the projected 1D convolution neural network
on the MIT‐BIH heart disease knowledge processed as
segments of fifteen s consisting of 5400 samples as well as
twenty one totally different categories of anomalies. within
the 1st experiment, we tend to split the dataset supported
segments as explained in Section four.2. The network was
trained on seventieth of the set and was valid on the
remaining half-hour for two hundred epochs. Results of the
validation performed on the coaching set square measure
shown in Figure four. The network stable in convergence
during a coaching method of two hundred epochs. {the
learning|the coaching|the educational} curves ofthetraining
and validation loss stable below zero.5 and therefore the
learningcurvesofthecoachingandvalidationaccuracystable
around ninetieth, each with a least gap between the ultimate
worth
In order to validate the stability of the proposed method, we
used a k-fold cross validationas explained in Section 4.4. The
average accuracy of the model was 89.3 ± 0.26% of standard
deviation and the average loss was of 0.28 ± 0.06% and an
average recall 85.6 ± 0.03%. In order to assess the
performance, the network was evaluated on the test set. The
confusion matrix and the related metrics were computed
(Table 5). The network showed an accuracy of 89.51%,anda
recall of 91.09% for normal and he related metrics were
computed (Table 5). The network showed an accuracy of
89.51%, and a recall of 91.09% for normal and 87.79% for
anomalous segments.

We compared the planned network with the studies that, at
the state of the art, performed identical 1D convolutional
neural network mistreatment the MIT-BIH cardiac
arrhythmia Dataset assumingtoimplementassociatedegree
automatic classification of viscuspathologies supported ECG
signals. The results of this comparison area unit reportable
in Table-6
TRAINING AND TESTING:-
Training accuracy : 98.43%
Testing accuracy : 92.57%
5.1 CONFUSION MATRIX
The network showed associate accuracy of eighty four.94%,
a recall of fifty five.40% for traditional and ninety four.36%
for abnormal segments, a exactness of seventy five.79% for
traditional and eighty six.91% for abnormal segments,
associated an F1-score of sixty four.01% for traditional and
ninety.48% for abnormal segments. The model showed the
next recall for abnormal segments comparedtotheprevious
take a look at. Analyzing the seventy seven segments
containing anomalies and incorrectly classified as
traditional, we have a tendency to ascertain that, for all of
them, a minimum of one among the parts extracted from a
similar graphical record recordingwasproperlyclassifiedas
abnormal. This result showed a motivating improvement
within the performance of the projected methodology since,
thinking in terms of patients instead of segments,thesystem
was ready to sight 100% of graphical record recordingsfrom
patients laid low with internal organ pathologies.
Considering this performance, wehavea tendencytobelieve
that the inevitable goal to sight abnormal segments and
minimize uncomprehensible alarmswasreached.Thisclaim
should be thought-about within the operative context
wherever the model was deployed,withinwhichthevalue of
a warning was significantly but an uncomprehensible one.
Moreover, we wish to spotlight that these results were
achieved with the second experiment whose settings
represent a more in-depth situation intermsofmodel usage.
within the second experiment, the model was valid on

graphical record records of various patients ne'er conferred
to the model throughout the coaching. This experimentstyle
isn't common since validation is sometimes disbursed with
the same old holdout methodology, that is, segments from a
similar graphical record recordings may well be interleaved
between coaching and take a look at sets. With holdout,
there area unit possibilities that some signal patterns are
already conferred to the model within the coaching part.
However, it ought to be noted that the system delineated
here still presents a major warning rate. we have a tendency
to area unit today investigation completely different
methods to cut back the desired employment because of
false alarms. One viable resolution is to isolate and collect
solely abnormal detected segments; once they occur in vital
quantities may be sent remotely for skilled verification.
trying solely at a number of the segments, simply those
classified as abnormal by our model may be a awfully fast
job for associate skilled, definitelyless hardthanobservanta
complete graphical record, even additional therefore if
remarked a Holter examination This operational resolution
is well matched additionallyforcontinual andactivelearning
techniques, that's to sporadically retrain the model
supported new associate degree notations from an expert;
during this approach, as new examples inherit the system a
probable gradual decrease in warning events is predicted,
with additional balanced performances.
6. CONCLUSIONS
The diffusion of non-public transportable observance
devices may involve the coverage of countless ECG
recordings a day. Systems ready tosupportthecardiologists’
add the interpretation of ECGs for the identification of
internal organ pathologies ar needed to facilitatehealthcare
deciding reducing significantly the expenditure of your time
and cash. The automatic detectionofsuspiciousanomaliesin
ECG recordings will drastically cut back the amount of ECGs
that require to be manually examined by the cardiologists,
excluding those classified as traditional. within the gift
paper, we tend to propose a system ready to mechanically
sight the suspect internal organ pathologies in ECG signals
from personal observance devices, employing a 1D-CNN
design. The 1D-CNN model overcomes the issues of
vanishing gradient and gradient exploding associated with
perennial neural networks, creating their coaching tough.
Moreover, the 1D-CNN model permits one to implement
period of time and cheap systems and it's characterised by
low procedure complexness, possible implementation on
good devices, and cloud computing. The system was
optimized to sight the suspect anomalies classifying regular
and abnormal ECG, despite the type of internal organ
pathology. The projected model was tested on the MIT-BIH
ECG heart condition info, including twenty one totally
different categories of ECG anomalies. 2 totally different
experimentations were disbursed showing outstanding
performance compared to the opposite studies conducted
mistreatment the 1D-CNN design testedontheMIT-BIHECG
heart condition info. specially, the network achieved
accuracy and recall, severally, of 84.94% and 94.36%
computed regarding the ECG signal segments and accuracy
and recall of 100 percent once computed regarding the
patients, so considering the detection of anomalies within
the entire ECG recordings. we tend to ar currently
functioning on a doable personalization of the model,
tunable toward one person. we tend to expect that the
performance of this sort of model might be far better than a
general one, with Associate in Nursing supplementary price
of model activity before the particular usage. within the
same study, we tend to are investigation a model trained on
“normal” components of healthy patients and abnormal
elements of pathological patients (since it's impractical to
get abnormal segments from a healthy patient). this may
permit North American country to look at if the model
trained on this new dataset presents distinct characteristics
than the model represented during this paper.Here,wetend
to needed to check our study with studies that were as
unvaried as doable, a minimum of within the dataset used,
thus we tend to didn't introduce any further changes
concerning the coaching knowledge.
7. FUTURE SCOPE
We have implemented a classification model to classify
different heartbeat waveforms which can point out
abnormal ECG results using Dense And Dropout so that the
patients can consult a doctor. By workingmoreontheseECG
results, this model can be worked upon to detect particular
heart diseases and not just abnormal heart beats. This can
potentially diagnose heart diseases or can warn the patients
who are prone to certain heart disease to take care of their
health more
REFERENCES
1. Cai, W.; Hu, D. ECG Interpretation with Deep Learning. In
Feature Engineering and Computational Intelligence in ECG
Monitoring; Liu, C., Li, J., Eds.; Springer: Singapore, 2020; pp.
143–156.
2. Chamley, R.R.; Holdsworth, D.A.; Rajappan, K.; Nicol, E.D.
ECG interpretation: Interpretation of the ECG in young, fit,
asymptomatic individuals undertaking high-hazard
occupations is the topic of the fourth article in the
occupational cardiology series. Eur. Heart J. 2019, 40, 2663–
2666. [CrossRef]
3. Dimauro, G.; Caivano, D.; Ciccone, M.M.; Dalena,G.;Girardi,
F. Classification of cardiac tones of mechanical and native
mitral valves. In Ambient Assisted Living, Lecture Notes in
Electrical Engineering; Spring: Berlin/Heidelberg,Germany,
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4. Schläpfer, J.; Wellens, H.J. Computer-Interpreted
Electrocardiograms. J. Am. Coll. Cardiol. 2017, 70, 1183–
1192. [CrossRef]
5. Dimauro, G.; de Ruvo, S.; di Terlizzi, F.; Ruggieri, A.; Volpe,
V.; Colizzi, L.; Girardi, F. Estimate of Anemia with New Non-
Invasive Systems—A Moment of Reflection. Electronics
2020, 9, 780. [CrossRef]
6. Dimauro, G.; Bevilacqua, V.; Colizzi, L.; di Pierro, D.
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7. Dimauro, G.; Guarini, A.; Caivano, D.; Girardi, F.; Pasciolla,
C.; Iacobazzi, A. Detecting Clinical Signs of Anaemia From
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8. Kasiviswanathan, S.; Vijayan, T.B.; Simone, L.; Dimauro, G.
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9. Monaco, A.; Cattaneo, R.; Mesin, L.;Fiorucci,E.;Pietropaoli,
D. Evaluation of autonomic nervous system in sleep apnea
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study. Cranio 2014, 32, 139–147. [CrossRef]10.Castroflorio,
T.; Mesin, L.; Tartaglia, G.M.; Sforza, C.; Farina, D. Use of
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BIOGRAPHIES
REDDY TRIVENI
ABHISHEK SHARMA
GUSIDI SANTOSHI
MAHANTHI ABHISHEK
Pursuing B-tech from Department of
computer science and Engineering at
Sanketika Vidya Parishad Engineering
College
GANESH ALLU
Currently working as assistant professor
from Department of Computer Science
and Engineering at Sanketika Vidya
Parishad Engineering College
College
College
College

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