Arrhythmia Detection from 2-lead ECG
using Convolutional Denoising Autoencoders
Keiichi Ochiai

Shu Takahashi∗

Yusuke Fukazawa

NTT DOCOMO, INC.
ochiaike@nttdocomo.com

SAS Institute Japan Ltd.
shu.takahashi@sas.com

NTT DOCOMO, INC.
fukazawayu@nttdocomo.com

ABSTRACT
Cardiac arrhythmia is the cause of death a significant number ofdeaths. As such, automatic arrhythmia detection from an electrocardiogram (ECG) is an important research topic. There are three
difficulties in detecting arrhythmia ECG signals: (1) ECG waveformdifferences between patients, (2) ECG waveform changes caused byheart rate variability, and (3) observation noise. To address these
problems, in several studies, the use of a convolutional neural network (CNN) has been proposed for extracting abstract featuresfrom ECG signals. This paper presents a method to classify ECG
for arrhythmia detection using a Convolutional Denoising Autoencoder (CDAE). By combining a CNN with a denoising autoencoder(DAE), we expect that more robust features can be extracted. Our
ECG classifier is built by combining the encoder part of the CDAE
with a fully connected layer. The evaluation results show that the
proposed method outperforms the existing works with respect tospecific type of arrhythmia (i.e. ventricular ectopic beat, VEB) by
1.3% for sensitivity.

CCS CONCEPTS• Applied computing → Health care information systems; •Information systems → Data mining;

KEYWORDS
Denoising Autoencoders (DAE), Convolutional neural network(CNN),
Convolutional Denoising Autoencoders (CDAE), arrhythmia detection from electrocardiogram (ECG)

1

INTRODUCTION

Seven million people from all over the world die annually fromcardiac arrhythmia [11, 23], Detecting arrhythmia from electrocardiogram (ECG) readings is clearly an important research topic.
There has been substantial research on automatic arrhythmia classification using wavelets [25], filter banks [1], support vector machines [20] and more. Automatic arrhythmia classification from
ECG signals can be divided into four steps [14] as follows: (1) ECG
signal preprocessing, (2) heartbeat segmentation, (3) feature extraction and (4) learning/classification. In the preprocessing step,
noise reduction is applied to ECG signals. Noise reduction methods∗ This work was done while Shu Takahashi was at NTT DOCOMO, INC.

Permission to make digital or hard copies of part or all of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citationon the first page. Copyrights for third-party components of this work must be honored.
For all other uses, contact the owner/author(s).
KDD’18 Deep Learning Day, London, UK© 2018 Copyright held by the owner/author(s). 978-x-xxxx-xxxx-x/YY/MM. . . $15.00
DOI: 10.1145/nnnnnnn.nnnnnnn

originated with signal processing techniques, such as the finite
impulse response filter[19] and the Kalman filter [18]. The R peak isdetected in the segmentation step. Next, features for classification
are extracted from the ECG signals in the RR interval (RRI). Finally,
arrhythmia classification is performed on the features extracted inprevious step. Due to the fact that heartbeat segmentation methods
have been well studied and have achieved high levels of accuracy(> 99%), the key steps for improving the classification performance
are improved noise reduction and feature extraction [14, 29]. In
previous studies, feature extraction was performed by computing
handcrafted features, such as signal processing-based frequency
characteristics (e.g. wavelet transform [25] or filter bank [1]) andhigher order statistics [20]. Recently, techniques for feature extraction from ECG signals using a convolutional neural network (CNN)
were proposed and achieved a high performance for arrhythmia
detection compared to previous studies [10, 11, 21, 22, 29].
Existing studies can be divided into two categories: the patientspecific approach and the non-patient-specific approach. In thepatient-specific approach, there is a patient overlap between training and test (prediction target) data. On the other hand, there is nopatient overlap between training and test data in the non-patientspecific approach. Real-world setting applications of the patientspecific approach are very limited because it is difficult to obtain
manually annotated ECG data for any specific patient. Thus, nonpatient-specific arrhythmia classification is important for a wide
variety of applications, and hence it is a challenging problem forthe three reasons described below [29].
(1) ECG Waveform Difference between Patients
Due to the fact that ECG waveforms may vary between
patients, it is difficult to robustly detect arrhythmia for all
patients.
(2) ECG Waveform Changes Caused by Heart Rate Variability
ECG waveforms can change depending on the physiological and mental status of the patient and, thus, are influenced by, for example, stress state, excitement, and exercise.
Therefore, it is hard to robustly detect arrhythmia for various patient’s statuses.
(3) Observation NoiseECG signals contain noise caused by the features of theenvironment, such as electrical noise and the patientsâĂŹ
physical movements during ECG measurements. Thus, forappropriate classification, noise reduction is needed.
In several studies, non-patient-specific arrhythmia detection has
been examined using a CNN. For example, Rajpurkar et al. [22]built a large-scale dataset of 30,000 unique patients, for arrhythmia
detection. Their database had more than 500 times as many entries
as the MIT-BIH Arrhythmia Database which is the most widelyused, publicly available dataset for performance evaluation of ECG

KDD’18 Deep Learning Day, August 2018, London, UK
classification [22]. Rajpurkar et al. collected single-lead ECG datafrom a pool of nearly 30,000 patients who used the Zio Patch monitor provided by iRhythm Technologies1 . In addition to building
this large-scale dataset, they proposed a 34-layer CNN model toclassify 13 classes of arrhythmia. Their large-scale dataset and deeparchitecture helped extract high-level (abstract) features, which
are important for high-accuracy classification from ECG signals.
However, it is very expensive to collect annotated data on such alarge scale.
An autoencoder (AE) [8] is used to extract high-level representation of data in the field of image recognition. A Denoising Autoencoder (DAE), which can extract more robust features compared to
AE by adding noise to the input data, has been proposed by Vincent
et al. [26]. We leveraged the characteristics of both of these AE
neural networks for feature extraction from ECG signals.
In this paper, we present a method for non-patient-specific arrhythmia detection that uses a convolutional denoising autoencoder(CDAE). Due to the fact that ECG waveform differences between
patients and observation noise complicate the arrhythmia classification from ECG signals, we combine a CNN with a DAE to extractrobust features from ECG signals. By adding a fully connected layer
to the encoder part of the pretrained CDAE model, we can finetune classification model that is highly accurate for new patients.
In addition, in the hopes of improving the accuracy of arrhythmia
classification by considering the interinfluence characteristics ofmultichannel ECG, we evaluate 1D and 2D CNN filters for arrhythmia classification.
Our contributions are as follows:• We present a novel approach for feature extraction andnoise reduction for ECG classification using CDAE.
• We evaluate the effectiveness of a DAE for ECG classification using a publicly available ECG dataset.
• With our CDAE model, we achieved the highest sensitivity
and specificity of detection for a specific type of arrhythmia(ventricular ectopic beat, VEB) in the MIT-BIH Arrhythmia
Database.
The rest of this paper is organized as follows. In Section 2, we
review the previous work. Section 3 describes the proposed method.
In Section 4, we conduct an experiment on a public dataset andcompare the proposed method with existing works. Finally, we
conclude the paper and discuss future work in Section 5.

2

Keiichi Ochiai, Shu Takahashi, and Yusuke Fukazawa
MIT-BIH Arrhythmia Database [17] The peak of an ECG waveform
is called an R-peak, and the interval between two R-peaks is called
the RR interval (RRI) as illustrated in Figure 1. An ECG signal is
divided into windows, each of which includes signals on both theright and the left sides of the R-peak. Then, for each window, it isdecided whether it is arrhythmia or not based on features extracted
from each window. Therefore, it is necessary for ECG classification
to extract effective features from each window.

2.2

Related Work for Arrhythmia Detection
from ECG

As mentioned in Section 1, the state of the art in arrhythmia detection comes from a deep-learning-based approach (e.g., the use ofCNNs), which outperforms the handcrafted feature-based approach.
Therefore, in this subsection, we review studies that employ deeplearning in addition to Rajpurkar et al. [22] described in Section
1. Kiranyaz et al. [10] proposed a method for patient-specific ECG
classification using 1D CNN. The model was evaluated on the MITBIH Arrhythmia Database and focused primarily on two specifictypes of arrhythmia: VEB and supraventricular ectopic beat (SVEB).
Kiranyaz et al. [11] also proposed a personalized monitoring system.
In [11], the classification model was trained by real normal beats andsynthesized abnormal beats for binary (i.e. normal class/abnormalclass) classification. Since the target of their system was healthy
subjects, no real abnormal beats were used in the training datasets.
Zubair et al. [29] proposed a method for non-patient-specific ECG
classification using 1D CNN. The model that they proposed wascomprised of three convolutional layers, three pooling layers andone fully connected layer with Softmax. The performance of themodel was evaluated on the MIT-BIH Arrhythmia Database, andits overall accuracy is 92.7%. However, this evaluation is not strictly
non-patient-specific because there is a patient overlap between
training and test data in this evaluation. In addition, while they

R wave	(R peak)	

window	
RRI 	

RELATED WORKOver at least the past decade, researchers have developed several
techniques to detect arrhythmia from ECG signals. We will first
introduce the general framework for arrhythmia detection from
ECG signals. Next, we will review previous work on automatic
arrhythmia detection.

2.1

Arrhythmia Detection from ECGECG signals represent the cardiac electrical activity. For the diagnosis of arrhythmia and other forms of heart diseases, the heartrate, rhythm, and change in each waveform pattern of an ECG are
observed. Figure 1 shows an example of a 2-lead ECG from the1 http://irhythmtech.com/

Figure 1: Example of ECG

Arrhythmia Detection from 2-lead ECG using Convolutional Denoising
Autoencoders
evaluated the overall accuracy, the classification performance for
specific types of arrhythmia was not evaluated. Pourbabaee et al.
[21] proposed a five-layer CNN for specific arrhythmia detection(Paroxysmal Atrial Fibrillation, PAF). This method classifies subjects
as “health” or “has PAF.” They evaluated their method on the PAF
prediction challenge dataset[16]. Rahhal et al. [2] proposed to use
a stacked denoising autoencoder (SDAE) for active-learning-basedclassification.
To the best of our knowledge, our work differs from the previously proposed methods in following aspects:• Although deep CNN and SDAE have been used in existing
work, our research is the first to incorporate DAE into the
CNN model for ECG classification.
• No existing work evaluated the effect of CNN and DAE fora specific type of arrhythmia in the non-patient-specificapproach.

3

THE PROPOSED METHOD

We propose a method for feature extraction from ECG data using
CDAEs and arrhythmia classification. The proposed method is
divided into two steps as follows: In this paper, we refer to thepatient that is not contained in the training data. as the “unseen
patient.”i) Feature Extraction using CDAE (Pretraining)
DAEs are considered effective for high-level feature representation because ECG signals exhibit a variation that isdependent on the individual patient and observation noise.
In addition, since ECG signals are a form of time-series
data and it is useful to exploit the data around (both before
and after) the classification target, we use a CNN model toextract features from around the classification target [28].
Therefore, we propose to use CDAE, which combines a
DAE with a CNN for feature extraction. Similar to a DAE,a CDAE is an unsupervised deep neural network trainedso as to reconstruct input data that has been corrupted byadding noise.
ii) Training Classifier (Fine-Tuning)
To build our classifier, the encoder part of the CDAE is combined with the fully connected layer for classification. Then,the weights can be fine-tuned using back-propagation. The
way of training the weight is similar for the DAE.

KDD’18 Deep Learning Day, August 2018, London, UK
bias vector and σ (·) is an activation function. Let z ∈ Rd be reconstructed vector. Then, reverse mapping function, called the decoder,is z = дθ ′ (y) = σ (W ′y + b ′ ) with parameterized by θ ′ = {W ′, b ′ }.
The weight parameter is usually constrained by W ′ = W T . Eachtraining datum x i is then mapped to the corresponding yi and its
reconstruction zi . The parameters of the AE can be obtained byminimizing the following objective function.
nθ, θ ′=

arg min

1X
L(x i , zi )
n i=1

=

arg min

1X
L(x i , дθ ′ ( fθ (x i )))
n i=1

θ,θ ′

n

θ,θ ′

where L is a cost function„ for which we will use the mean squared
error (MSE): L(x i , zi ) = ∥x i − zi ∥ 2 .
The DAE proposed by Vincent et al. [26] can extract more robustfeatures compared to the AE algorithm by adding noise to the input
data. Figure 2 shows an example of a DAE. In the DAE, the initial
input x is corrupted to x̃ by a stochastic mapping x̃ ∼ C (x̃ |x ),which partially destroys the input data. The algorithm uses thecorrupted x̃ as input data and then maps it to the corresponding yi
and ultimately to its reconstruction zi . The parameters of DAE canbe trained by minimizing the following objective function.
nθ, θ ′=

arg min

1X
L(x i , zi )
n i=1

=

arg min

1X
L(x i , дθ ′ ( fθ (x̃ i )))
n i=1

θ,θ ′

n

θ,θ ′

Feature extraction using CDAE
(pre-training)AEs are a dimensionality reduction algorithm that was proposedby Hinton et al. [8]. The weight of the AE is trained through amultilayer neural network with a small central layer used to reconstruct the input data, which is of high dimensionality. As aresult of the training, we can obtain a higher-level representationof the input data. An AE takes an input x ∈ Rd and maps it to′the hidden representation y ∈ Rd using a deterministic mapping
function, called the encoder, y = fθ (x ) = σ (W x + b) with parameterized by θ = {W , b}. Here, W is d × d ′ weight matrix, b is(2)

Recently, owing to their high performance, CNNs have received
considerable attention in the field of image recognition [12] andnatural language processing [9, 27] etc. CNNs consist of three basic
layers: a convolutional layer, a pooling layer and a classification
layer [13]. Features whose patterns appear in the input data are
extracted using the convolutional layer, and the pooling layer offersa model that is robust to the location of the patterns appearing in
the input data [24]. Only the convolutional layer and the poolinglayer are used in the pretraining phase.

In the rest of this section, we will describe the details of each
step.

3.1

(1)

Figure 2: Example of denoising autoencoder

KDD’18 Deep Learning Day, August 2018, London, UK

Keiichi Ochiai, Shu Takahashi, and Yusuke Fukazawa

Masci et al. [15] proposed a a convolutional autoencoder (CAE)that uses CNNs in the encoder layer and in the hidden layer of the
AE. The difference between the CAE and the AE is weight sharing.
The CAE has fewer parameters than the AE. The experimental
results showed that the CAE outperforms CNN in several image
classification tasks. Du et al. [5] presented stacked convolutional denoising autoencoders (SCDAEs), which are constructed by stacking
DAEs whose weights are trained in a convolutional way. Evaluationon large-scale image datasets showed that SCDAE is superior interms of learning robustness and abstract features.
In this research, we propose to use CDAE which has a convolutional layer and a pooling layer in the encoder and decoder ofthe DAE for feature extraction. As mentioned in Section 1, there
are three problems in arrhythmia detection from ECG signals: ECG
waveform change due to patients and heart rate variability, and observation noise. By exploiting the nature of high-level (abstract) features extracted by the convolutional layer and translation-invariant
features extraction by the pooling layer, it is possible to extractrobust features against patient and heart rate variability. In addition, feature extraction that is robust to observation noise can be
performed by adding noise to input data.
For each ECG channel input x, the hidden representation y of
the kth feature map is represented by
y k = σ (W k ∗ x + b k )(3)where ∗ denotes the convolutional operation [15]. If the input datais 2-lead (channel) ECG signal, the 2D filter is applied in the convolutional operation. The decoder is denoted byX
z = σ(
W ′k ∗ y k + b ′k )
(4)
k ∈H

where H indicates the group of latent feature maps. We use the
MaxPooling as the pooling layer in the encoder, and we use Upsampling in the decoder. MaxPooling outputs the maximum value
within the region covered by the filter. Upsampling repeats (copies)
the input value up to the filter size. Let x˜i and zi be the corrupted
input data and the output data, respectively. Then, the loss function is represented as in Eq. (2). Equation (2) is optimized using a
back-propagation algorithm.

3.2

unit i is defined by the following equation.
e xip(i) = PN x
j
j=1 e

(5)

Cross-entropy is used as the loss function of classifier LC LF as
follows.
n m
1 XX
LC LF (θ ) = −yi j loд(pi j )(6)
n i=1 j=1
where n is the sample size, m is the number of classes, pi j is theoutput of the classifier of class j of the ith sample and yi j is theannotated label of class j of the ith sample.

4 EXPERIMENTS
4.1 Experimental Setting
We used the MIT-BIH Arrhythmia Database2 which is the mostwidely used database in existing ECG classification [14] for performance evaluation. This database contains 48 records, each containing 2-lead ECG signals for a 30 min segment from a 24 h recording.
Each ECG signal is bandpass-filtered at 0.1 - 100 Hz and the sampling frequency is 360Hz. The 44 records except for 102, 104, 107,
217 from MIT-BIH Arrhythmia Database are selected for the experiment because these excluded records do not have sufficient signal
quality based on Association for the Advancement of Medical Instrumentation (AAMI)3 recommendation [6]. This same criterion isused in existing work such as [10, 29]. Each record has RRI and annotated labels provided by PhysioNet. The AAMI recommends thateach ECG beat be classified into the following five heartbeat types:
N (normal beats), S (supraventricular ectopic beats, SVEB), V (ventricular ectopic beats, VEB), F (fusion beat), and Q (unclassifiablebeats).
2 https://physionet.org/physiobank/database/mitdb/
3 http://www.aami.org

SCDAE(pre-training)

Classifier(fine-tuning)+ Noise

Input

Building Classifier (Fine-Tuning)

CDAEs have no classification function. To build an arrhythmia
classifier, a fully connected layer (classification layer) is added on
top of the encoder part of the CDAE described in Section 3.1. Figure
3 presents a conceptual depiction of building the classifier froma pretrained CDAE model. The weights and the neural networkarchitecture are extracted from the pretrained CDAE model, andthe fully connected layer is added as an output layer. The numberof units of the output layer is the number of classes into which
we would like to classify arrhythmia. The classifier is trained bysupervised learning using input data x and an annotated label as
an output.
The Softmax function is used as an activation function for the
output layer of the classifier. By using the Softmax function, theoutput of each unit can be treated as the probability of each label.
Here, let N be the number of units of the output layer, let x be theinput, and let x i be the output of unit i. Then, the output p(i) of

Encoder

Encoder
copy weights

Decoder

Fully connected
layer (softmax)# of categories

OutputFigure 3: Fine-tuning of classifierArrhythmia Detection from 2-lead ECG using Convolutional Denoising
Autoencoders
Each window of an ECG waveform has a different size because
the RRI is not constant. In addition, the ECG of each patient has
a different amplitude. Therefore, we normalized ECG signals. To
standardize the length of the RRI of each window, the data is resampled at a fixed rate using a Fourier transform. Next, the amplitudeof each window is normalized from 0 to 1. In our setting, the normalization is applied after resampling at a fixed rate of 160 samples
from each window.
To evaluate the performance of the classification of unseen patients, we conducted a 5-fold cross-validation. Specifically, we divided the 44 users into five groups. The model was trained by thedata of four groups, and a test was conducted on the remaining
group. We repeatedly changed the group used for testing five times,
in conducting 5-fold Leave-One-Group-Out Cross-Validation. 5,000
beats were randomly sampled from the training data as validation
data. These validation data were then removed from the training
data. Table 1 shows an example of AAMI recommended classes ofarrhythmia and the number of samples in training and test set fromone-fold.
Classification performance is measured by using the following
four metrics in the literature [10]. Classification Accuracy (Acc),
Sensitivity (Sen), Specificity (Spe), and Positive Predictive Value(Ppv). While accuracy can be used for evaluating the overall performance, the other metrics can measure the performance of specificclass. These four metrics are defined by following numbers: true
positive (TP), true negative (TN), false positive (FP) and false negative (FN). Accuracy is the ratio of the number of correctly classified
samples to the total number of samples. Acc = (TP + TN)/(TP + TN
+ FP + FN). Sensitivity is the ratio of positives that are correctlyclassified to actually positive samples. Sen = TP/(TP + FN). Specificity is the ratio of negatives that are correctly classified to actuallynegative samples. Spe = TN/(TN + FP). Positive Predictive Value isthe ratio of the number of correctly classified positive samples tothe number of the samples which is predicted as positive. Ppv =TP/(TP + FP).
Table 2 shows the architecture of the CDAE used in our evaluation. We used three convolution layers and three pooling layers in
CDAE. ReLU [7] is used as the activation function and Nadam [4]is used as the optimization method. The initial learning rate is set
to 0.002 and gradually decreased. The input data is corrupted by
Gaussian distribution (mean=0, std=0.001).
The classifier is constructed by combining the pretrained encoder part of the CDAE with the fully connected layer. We added
32-dimensional and 5-dimensional full connected layers. Figure 4

Table 1: AAMI recommended classes of arrhythmia andnumber of samples
Label
N
S
V
F
Q

Description
Normal beats
Supraventricular ectopic beats
Ventricular ectopic beats
Fusion beats
Unknown beats

Count
Training
Test
67,810 17,792
2,447
178
5,013
1,680
726
29
12
2

KDD’18 Deep Learning Day, August 2018, London, UK
shows the details of the architecture of the classifier. Cross-entropy
was used as the loss function and Stochastic Gradient Descent [3]was used as the optimization method. The initial learning rate was
set to 0.01 and gradually decreased.

4.2

Results

Figure 5 shows the training curve of the CDAE. This figure indicatesthat the errors of the test data (loss) and validation data (val-loss)
decrease by increasing the epoch. The result shows that CDAEs can
extract high-level features not only from the training data but also
from unseen data. Figure 6 shows two examples (blue and green
lines) of ECG signals reconstructed ECG signals from the test datausing the CDAE. The reconstructed waveform shows the effect of
denoising while maintaining its overall shape.
Figure 7 shows the learning curve of the classifier, and Figure 8
shows the accuracy of each epoch. The errors of the test data (loss)
and the validation data (val-loss) decrease by increasing epochs,
similar to pretraining. The accuracy increases with the epoch.
The evaluation results are shown in Table 3 for Type V (VEB) andin Table 4 for Type S (SVEB). Since the overall accuracy is dominatedby normal beats, we evaluated a specific type of arrhythmia. Table
5 shows the confusion matrix of the proposed method. Due to thefact that there is no existing work using the MIT-BIH Arrhythmia
Database for specific types of arrhythmia with the non-patientspecific approach, we compared the proposed method with a similar
CNN model proposed in the work of Zubair et al. [29] as a baseline
method. The difference between their CNN model and the proposedmodel is the incorporation of a DAE. In addition, we evaluated theeffect of 1D and 2D CNN filters on arrhythmia classification.
From Table 3, we can see that the proposed method with the
2D convolution filter is the model with best performance. While
the performance of the 2D convolution filter is higher than thatof the 1D filter for all metrics in the proposed method, the trend
in the baseline method (Zubair et al. [29]) that we used for ourcomparison is different. Regarding sensitivity, which is the mostimportant metric for arrhythmia detection, the proposed methodwith the 2D filter outperforms the baseline method with the 1D
filter by 1.3%. From Table 4, the proposed method with the 2D filter

Table 2: Architecture of the CDAE

Encoder

Decoder

Layer
0
1
2
3
4
5
6
7
8
9
10
11
12

Type
Noise
Conv1D
MaxPooling
Conv1D
MaxPooling
Conv1D
MaxPooling
UpSampling
Conv1D
UpSampling
Conv1D
UpSampling
Conv1D

Shape(160, 2)(160, 32)
(80, 32)(80, 16)(40, 16)(40, 4)(20, 4)(40, 4)(40, 16)(80, 16)(80, 32)(160, 32)
(160, 2)

Activation
ReLU
ReLU
ReLU

ReLU
ReLU
ReLU

KDD’18 Deep Learning Day, August 2018, London, UK
80x32

conv1x5
16 feature maps

maxpool1x2

80x16

40x16

40x4

20x4

conv1x5
flatten
4 feature maps
maxpool1x2
maxpool1x2

80

32

…

conv 2x15
32 feature maps

160x32…

160x2

Keiichi Ochiai, Shu Takahashi, and Yusuke Fukazawa
5

full
full
connect connect
softmax

Encoder part of CDAE

Figure 4: Architecture of the classifier

Cross Entropy

has the highest accuracy and specificity, whereas the proposed
method with the 1D filter has the highest sensitivity and positive
predictive value for SVEB. However, the sensitivity and positive
predictive value for SVEB are quite low, which is a limitation of
the proposed method. The reason might be that the set of training
samples of Type S was small. We would like to clarify the effect ofsample size on the classification accuracy in the future work.

Epochs

Accuracy

MSE

Figure 7: Learning curve of classifier

EpochsFigure 5: Learning Curve of CDAE
EpochsScaled Amplitude

Input data x

Figure 8: Accuracy of classifier

5
Reconstructed data z

SampleFigure 6: Example of the reconstructed data using CDAECONCLUSION

In this paper, we proposed a method for arrhythmia detection from
ECG signals using a Convolutional Denoising Autoencoder. While
the CNN-based approach has been already proposed, this research isthe first to incorporate a DAE with a CNN for arrhythmia detection.
The weight of the neural network is trained by the CDAE, andthe classifier is built by combining the encoder part of the CDAE
with a fully connected layer. The evaluation results show that the
CDAE is effective for VEB in the non-patient-specific approach forarrhythmia diagnosis.
In the future work, we would like to investigate the effect of
the size of the training data on the classification performance toArrhythmia Detection from 2-lead ECG using Convolutional Denoising
Autoencoders
improve the classification accuracy of SVEB. In addition, we would
like to apply the proposed method to not only 2-lead ECGs but also
12-lead ECGs which are widely used in the clinical field. While the12-lead ECG has a vast amount of information and spatially helpful
data compared to 2-lead ECG, there is a limit in visual examination
by cardiologists because there is so much information. Therefore,
we expect automatic arrhythmia detection to be an efficient meansof diagnosis in this case, as well.

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Table 3: Classification performance for VEB
MethodFilter
Acc
Sen
Spe
Ppv
Zubair et al. [29]
1D 0.951 0.834 0.961 0.719
Zubair et al. [29]
2D 0.947 0.771 0.962 0.771
Proposed
1D 0.951 0.801 0.964 0.740
Proposed
2D 0.957 0.847 0.966 0.748
Table 4: Classification performance for SVEB
MethodZubair et al. [29]Zubair et al. [29]Proposed
Proposed

Filter
1D
2D
1D
2D

Acc
0.939
0.962
0.947
0.966

Sen
0.117
0.153
0.154
0.048

Spe
0.966
0.988
0.971
0.993

Ppv
0.108
0.231
0.234
0.214

KDD’18 Deep Learning Day, August 2018, London, UK
Table 5: Classification result of test data
Label
N
S
V
F
Q

Classification result
N
S
V
F
17,594 105
40 53
127
29
17
5
203
39 1,438
0
27
1
1
0
1
0
1
0

Q
0
0
0
0
0

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