# Arrhythmia Classification Techniques Using Deep Neural Network

Authors: Ali Haider Khan; Muzammil Hussain; Muhammad Kamran Malik
Journal: Complexity
 (2021)

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## Abstract

Electrocardiogram (ECG) is the most common and low-cost diagnostic tool used in healthcare institutes for screening heart electrical signals. The abnormal heart signals are commonly known as arrhythmia. Cardiac arrhythmia can be dangerous, or in most cases, it can cause death. The arrhythmia can be of different types, and it can be detected by an ECG test. The automated screening of arrhythmia classification using ECG beats is developed for ages. The automated systems that can be adapted as a tool for screening arrhythmia classification play a vital role not only for the patients but can also assist the doctors. The deep learning-based automated arrhythmia classification techniques are developed with high accuracy results but still not adopted by healthcare professionals as the generalized approach. The primary concerns that affect the success of the developed arrhythmia detection systems are (i) manual features selection, (ii) techniques used for features extraction, and (iii) algorithm used for classification and the most important is the use of imbalanced data for classification. This study focuses on the recent trends in arrhythmia classification techniques, and through extensive simulations, the performance of the various arrhythmia classification and detection models has been evaluated. Finally, the study presents insights into arrhythmia classification techniques to overcome the limitation in the existing methodologies.


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## Body

## 1. Introduction

Cardiac disorder may cause a severe warning to public health, and in most cases, some arrhythmias can cause severe damage or death. According to the data provided by World Health Organization (WHO) [1], approximately 25.6 million people died due to cardiovascular disease in 2020. Electrocardiogram (ECG) test is used as a diagnostic tool in healthcare institutes. The electrodes attached to the patients’ body surface can record the heart’s electrical signal over time. Figure 1 shows the ECG leads attached to the human’s body surface to get the heart’s electrical signals.Figure 1
Heart electrical signal.The healthcare professionals manually diagnose the patient heart condition by interpreting the ECG image. In the advent of technology, several automatic diagnostic tools are developed for arrhythmias classification and detection to assist doctors. PCGs and ECGs are used to diagnose arrhythmia classification. PCG (also known as heart sound auscultation) is commonly listened to or recorded by practitioners through a stethoscope to identify heart irregularities. For this reason, heartbeats have been critically studied to make a diagnosis [2–5]. Khan et al. [6] provided a very thorough introduction of related studies in the problem domain and proposed a technique based on DNN, for the classification of three kinds of arrhythmia beats. CNN is considered a state-of-the-art tool for the classification of arrhythmia, and it has been studied with several variations such as 1-dimensional, 2-dimensional, or the combination of both [7, 8]. According to Xiao et al. [7], a novel arrhythmia classification technique comprises three phases: preprocessing, 1-dimensional CNN architecture based on clique blocks with bidirectional connections between layers and transition blocks with attention mechanism, and majority voting to predict the final result. Experiments were performed on PhysioNet/CinC 2016 database. However, low-frequency noise considerations recorded in arrhythmia beats are ignored, and 2-dimensional acoustical representation requires additional computation, which cannot be accomplished without hyperparameters. Similarly, Noman et al. [8] proposed a framework based on 1-dimensional CNN for direct feature learning from raw arrhythmia beats and 2-dimensional CNN, which takes 2-dimensional time-frequency feature maps.In the state-of-the-art studies [4–8], automated system which predicts high accuracy results is developed for arrhythmia detection but still not adoptable by healthcare professionals. The primary concerns which affect the success of the developed arrhythmia detection systems are (i) manual features selection, (ii) techniques used for features extraction, and (iii) algorithm used for classification and the most important is the use of imbalanced data for classification. The automated arrhythmia detection required the feature extraction of ECG images that required domain knowledge. In the last few years, deep learning-based systems are being recognized as a tool in healthcare institutes that have capabilities to automatically extract high-level abstract features, avoiding laborious manual feature design. A deep neural network is configured in the same fashion as the human brain works. A single neuron understands and recognizes the pattern based on the logical principles among different components. The proposed work’s major concern is to review the recent trends of arrhythmia classification techniques and enlist the limitation and future requirements. This study can help the researcher get a deeper understanding of arrhythmia classification and the deep learning methods used to develop automated systems.The reminder of the paper is organized into six sections. The methods used in the proposed study are discussed in Section2, and the research community’s related work in the current field is presented in Section 3 with a close comparison. Recent trends of arrhythmia classification and limitation of the current study are discussed in Sections 4 and 5, respectively. The paper is concluded in Section 6.


## 2. Methodology

In this research study, the authors follow the mapping techniques for arrhythmia classification techniques using a deep neural network. Figure2 demonstrates the systematic methodology in a four-step process. The primary objective of this study is to critically review the existing methods based on arrhythmia classification on the publishing forums [9].Figure 2
Systematic methodology.


### 2.1. Research Objectives

The primary goal of this research is to review the development of arrhythmias classification techniques over time, i.e., January 2010 to January 2020, using the machine and deep learning approach. The primary objectives of this research study are(1)
To examine the arrhythmia classification techniques as practically implementable.(2)
To overview the existing research studies based on arrhythmia classification benefits and future research direction.(3)
Identify the latest research trends and publication interests based on arrhythmia classification.


### 2.2. Search String

The most reliable and trusted data sources available in the digital world to get the required articles for selected research studies are ACM, Elsevier, Google Scholar, IEEE Digital Library, Nature Scientific Reports, Springer, Science Direct, and Web of Science. The two main search strings are used in all data repositories to download the related articles.(1)
(“arrhythmia” OR “arrhythmia classification” OR “cardiac arrhythmia” OR “arrhythmia detection” OR “cardiac disorder” OR “electrocardiogram” OR “cardiac care”) AND (“deep neural network” OR “dnn” OR “deep learning” OR “dl” OR “neural network” OR “nn” OR “convolutional neural network” OR “cnn” OR “recurrent neural network” OR “rnn” OR “Long Short Term Memory” OR “lstm”)(2)
(“heartbeat” OR “heart disorder” OR “cardiac” “cardiac abnormality” OR “arrhythmia” OR “heart sound” “heart signals” OR “ECG” OR “ECG signals” OR “ECG Images”) AND (“deep neural network” OR “dnn” OR “deep learning” OR “dl” OR “neural network” OR “nn” OR “convolutional neural network” OR “cnn” OR “recurrent neural network” OR “rnn” OR “Long Short Term Memory” OR “lstm”)The peer-reviewed best quality articles published online from January 2010 to January 2020 only were selected for this research study.


### 2.3. Selection Criteria

Identifying and selecting the best-suited articles that match the primary objectives of this research study are to identify the latest trends of deep neural networks for arrhythmia classification techniques. Table1 shows the inclusion and exclusion criteria used in this study.Table 1
Selection criteria.
Inclusion criteriaExclusion criteriaInC-1: articles presenting good foundation knowledge for arrhythmia classificationExC-1: articles that are not limited to arrhythmia classificationInC-2: articles that are presenting technical solutions using deep neural networks for arrhythmia detectionExC-2: articles that only discussed arrhythmia but not focused on deep learning/machine learning-based solutionsInC-2: articles that used publicly available ECG datasetExC-3: articles whose datasets are not publicly available as open sourceInC-2: articles that are written in the English languageExC-4: articles that are not available in the English languageExC-5: articles that are published before January 2010


## 2.1. Research Objectives

The primary goal of this research is to review the development of arrhythmias classification techniques over time, i.e., January 2010 to January 2020, using the machine and deep learning approach. The primary objectives of this research study are(1)
To examine the arrhythmia classification techniques as practically implementable.(2)
To overview the existing research studies based on arrhythmia classification benefits and future research direction.(3)
Identify the latest research trends and publication interests based on arrhythmia classification.


## 2.2. Search String

The most reliable and trusted data sources available in the digital world to get the required articles for selected research studies are ACM, Elsevier, Google Scholar, IEEE Digital Library, Nature Scientific Reports, Springer, Science Direct, and Web of Science. The two main search strings are used in all data repositories to download the related articles.(1)
(“arrhythmia” OR “arrhythmia classification” OR “cardiac arrhythmia” OR “arrhythmia detection” OR “cardiac disorder” OR “electrocardiogram” OR “cardiac care”) AND (“deep neural network” OR “dnn” OR “deep learning” OR “dl” OR “neural network” OR “nn” OR “convolutional neural network” OR “cnn” OR “recurrent neural network” OR “rnn” OR “Long Short Term Memory” OR “lstm”)(2)
(“heartbeat” OR “heart disorder” OR “cardiac” “cardiac abnormality” OR “arrhythmia” OR “heart sound” “heart signals” OR “ECG” OR “ECG signals” OR “ECG Images”) AND (“deep neural network” OR “dnn” OR “deep learning” OR “dl” OR “neural network” OR “nn” OR “convolutional neural network” OR “cnn” OR “recurrent neural network” OR “rnn” OR “Long Short Term Memory” OR “lstm”)The peer-reviewed best quality articles published online from January 2010 to January 2020 only were selected for this research study.


## 2.3. Selection Criteria

Identifying and selecting the best-suited articles that match the primary objectives of this research study are to identify the latest trends of deep neural networks for arrhythmia classification techniques. Table1 shows the inclusion and exclusion criteria used in this study.Table 1
Selection criteria.
Inclusion criteriaExclusion criteriaInC-1: articles presenting good foundation knowledge for arrhythmia classificationExC-1: articles that are not limited to arrhythmia classificationInC-2: articles that are presenting technical solutions using deep neural networks for arrhythmia detectionExC-2: articles that only discussed arrhythmia but not focused on deep learning/machine learning-based solutionsInC-2: articles that used publicly available ECG datasetExC-3: articles whose datasets are not publicly available as open sourceInC-2: articles that are written in the English languageExC-4: articles that are not available in the English languageExC-5: articles that are published before January 2010


## 3. Literature Review

Based on the selection criteria, the fifty technical articles on arrhythmia classification are examined which are published between January 2010 to January 2020. The selected articles are critically examined, and if any selected articles are available on more than one scientific repository or database, it is considered only once. The different kinds of methodologies, classification algorithms with their accuracy results, and optimization methods used for arrhythmia classification are reviewed from the selected articles. Table2 shows the literature survey on arrhythmia classification techniques that are used in this study.Table 2
Arrhythmia classification techniques from 2010 to 2020.
Ref.Publication yearNo. of leadsClassification techniquesOptimization techniquesAccuracy (%)[9]202002CNNBAROA93.19[10]202012Extreme gradient boosting treeLow pass filter97[11]2020021D-CNN—95[12]201904DNNPCA97.8[13]201903DNN—92.07[14]2019051 D CNN-2D CNN—90.93[15]201905Residual networksData augmentation99.81[16]201806EMDLDA87[17]201805CNN LSTMDL98.10[18]201802Deep belief networks—95.57[19]201702DNN—92[20]201704SVM—98.9[21]201705GRNN—88[22]201605NN—97[23]201602Dynamic BayesianPCA99[24]201503ANFIS—96[25]201403Feed forward PNN—96.5[26]201416SVMPCA86[27]20145ML classifier—99.48[28]201305SVMPCA-LDA99.28[29]201305NNPCA94.52[30]201302MLPNN—95.1[31]201302MLPNN—85[32]201303BMLPNN—76[33]201202MNN-generalized FFNN—86.67[34]201208PNNPCA-LDA99.71[35]201105NN—95[36]201102FCM—99.05[37]201103MLPNN—96.7[38]201005MDPSO—95.58The authors present the recent trends for arrhythmia classification, the techniques used for features extraction, and the variation of deep neural networks. The study is beneficial for the scientific community to select the arrhythmia classification techniques as per their desires. Table3 identified the latest trends from recently proposed studies for arrhythmia classification.Table 3
Top arrhythmia classification techniques.
Ref.Publication yearDatabaseClassification techniquesOptimizationAccuracy (%)[39]2018PTBFCNMFB-CNN99.95[40]2018PTBCNNCNN100[41]2018MITDBCNN—84[42]2018PhysioBankCNN—84[43]2018PhysioNet challengeFCNFFT81[44]2018MITDB AFIBDNNFourier transform98.34[45]2018PhysioBankCNN—96.36PhysioNet challenge[46]2018PhysioNet challengeMLP-CNN—76.79[47]2018MITDBCNN—98.6[48]2018MITDBAlexNet VGGNetTransformation99.05[49]2017PhysioNet challengeResNet—72.1[50]2018MITDBFCN-CNN—91.33[51]2019Multiple DBCNN—95.98[52]2017MITDBFCN1D-CNN97.5[53]2017MITDB PhysioNet 2000RNNCNN94.03[54]2017PAFCNN—93.6[55]2019MultipleRNN—99.26[56]2018MITDBSVMGD-DBM99.5[57]2017MITDBRNNLSTM99.4[58]2017MITDBRNN—95


## 4. Recent Trends in Arrhythmia Classification

The primary objective of this study is to present the techniques used in arrhythmia classification with a publicly available ECG database used in deep/machine learning algorithms. The selected state-of-the-art research studies show the most common methods used for arrhythmia classification and their accuracy results. The accuracy results represent the learning algorithm’s success rate that how well the machine was trained to identify the ground truth automatically. The primary objective of this study is to help the scientific community to list the methodologies used in arrhythmia classification with prediction results that researchers can easily select the techniques as per their requirements. Figure3 shows the statistics of most adopted arrhythmia classification techniques used in selected studies.Figure 3
Arrhythmia classification techniques.


### 4.1. Deep Learning Architectures

Deep learning techniques are commonly used in medical image analysis. A deep neural network (DNN) is configured in the same fashion as the human brain works. A single neuron understands and recognizes the pattern based on the logical principles among different components. The deep neural network’s fundamental unit is a neuron trained by repetitive tasks and gets experienced just like a human brain through acquired knowledge attained in training. The focus of training and acquiring knowledge is to establish a connection between input and output. After training, the system is capable of detecting the objects about what it has been trained. By reviewing the arrhythmia classification research studies, the most common DNN methods used for arrhythmia classification are given as follows:(1)
Recurrent neural network (RNN)(2)
Long short-term memory (LSTM)(3)
Autoencoder(4)
Convolutional neural network (CNN)(5)
Deep neural network (DNN)(6)
Deep belief network (DBN)


#### 4.1.1. Recurrent Neural Network (RNN)

RNN architecture belongs to the family of neural networks. RNN allows information to be stored in loops within the adjacent layers. RNN is a popular network that uses reasoning from past experiences to train upcoming events. RNN-based deep architecture is widely used in arrhythmia classification systems that can train the sequence vectors. Figure4 shows the internal working of RNN architecture.Figure 4
RNN architecture.


#### 4.1.2. Long Short-Term Memory (LSTM)

LSTM is the reproduction of RNN architecture [59] commonly used in the field of deep learning. In general feedforward, neurons have forward connections, but LSTM has reversible feedback connections. LSTM processes data points like images or it has the capabilities to process the sequences of data, such as continuous images, video, or speech signals. LSTM architecture has the capabilities to recognize unsegmented speech and detect similar patterns. LSTM also has the ability to recognize the heart sounds signals or ECG beats. The basic architecture of LSTM contains four major elements, i.e., cell, input gate, output gate, and forget gate. The cell has a responsibility to remember the values over specific time intervals and the gates regulate the flow of information between the cells. Figure 5 shows the internal working of LSTM architecture.Figure 5
LSTM-based architecture.


#### 4.1.3. Autoencoder

Autoencoder belongs to the family of artificial neural network- (ANN-) based architecture, which is used for training efficient data coding in an unsupervised manner. They are recognized as a tool to learn basic patterns for a similar set of data. Autoencoder also performs dimensionality reduction, and it tries to generate a class similar to its original input. In the cited research studies, the author proposed different variations in autoencoder-based architecture. Figure6 shows the basic architecture of the autoencoder.Figure 6
AE-based architecture.


#### 4.1.4. Convolutional Neural Network (CNN)

CNN is a deep learning-based algorithm. CNN is widely recognized as a tool in the field of computer vision and image processing. It consists of an input layer, hidden layers, and the last output layer. In forward pass convolutional neural network, the middle layers are called hidden layers that are masked with activation function (ReLU), pooling layer, and convolution. Figure7 shows the structure of the forward pass convolutional neural network. In the state-of-the-art research studies, CNN is widely recognized as a tool for the detection of ECG beats signals.Figure 7
Structure of a forward-pass CNN.


#### 4.1.5. Deep Neural Network (DNN)

A simple DNN contains multiple hidden layers that can process the input to output layers [60]. The DNN can recognize different kinds of unstructured data. In arrhythmia classification, authors proposed different kinds of neural networks but the proposed network is composed of the same components: neuron, weight, bias, and function. All these components are capable and act just like the human brain. The deep neural network is widely recognized approach for ECG image classification. Figure 8 shows the basic structure of DNN-based architecture.Figure 8
Structure of basic DNN architecture.


#### 4.1.6. Deep Belief Network (DBN)

DBN is a class of deep neural networks; it consists of multiple layers that have a connection between all the layers in the network but not with units of each other layers. DBN can be trained by supervision to achieve better prediction. Figure9 shows the basic structure of DBN-based architecture.Figure 9
Structure of basic DBN architecture.


### 4.2. ECG Databases

The expert systems for automatic detection of arrhythmia disease required training data to understand different patterns of each class. Authors of the selected research study critically analyze arrhythmia classification systems and enlist the most cited/publicly open access ECG databases. The ECG databases available for the scientific community used as a data source are listed in Table4.Table 4
ECG databases for arrhythmia classification.
DatabaseNo. of recordsECG leadsAnnotationMITDB4702Normal, abnormalPTB54915MIPhysioNet challengeTrain 852801Normal, noise, other, and AFTest 3658QT10502P Q R ST wavesCART 12-lead7512AF, AVPAF5016 beats/sampleNormal, abnormalAFIB2502MI typesPhysioBankTrain: 687712NormalTest: 2954AbnormalMendeley ECG data168712Normal/abnormal/MI/PMIThe database as mentioned above contains variations of ECG beats with different types of arrhythmia classification. The data sources have unique importance with domain-specific characteristics. The description of the patient population in which these ECG beats were obtained is important in interpreting the methodology and clinical utility in context.


## 4.1. Deep Learning Architectures

Deep learning techniques are commonly used in medical image analysis. A deep neural network (DNN) is configured in the same fashion as the human brain works. A single neuron understands and recognizes the pattern based on the logical principles among different components. The deep neural network’s fundamental unit is a neuron trained by repetitive tasks and gets experienced just like a human brain through acquired knowledge attained in training. The focus of training and acquiring knowledge is to establish a connection between input and output. After training, the system is capable of detecting the objects about what it has been trained. By reviewing the arrhythmia classification research studies, the most common DNN methods used for arrhythmia classification are given as follows:(1)
Recurrent neural network (RNN)(2)
Long short-term memory (LSTM)(3)
Autoencoder(4)
Convolutional neural network (CNN)(5)
Deep neural network (DNN)(6)
Deep belief network (DBN)


### 4.1.1. Recurrent Neural Network (RNN)

RNN architecture belongs to the family of neural networks. RNN allows information to be stored in loops within the adjacent layers. RNN is a popular network that uses reasoning from past experiences to train upcoming events. RNN-based deep architecture is widely used in arrhythmia classification systems that can train the sequence vectors. Figure4 shows the internal working of RNN architecture.Figure 4
RNN architecture.


### 4.1.2. Long Short-Term Memory (LSTM)

LSTM is the reproduction of RNN architecture [59] commonly used in the field of deep learning. In general feedforward, neurons have forward connections, but LSTM has reversible feedback connections. LSTM processes data points like images or it has the capabilities to process the sequences of data, such as continuous images, video, or speech signals. LSTM architecture has the capabilities to recognize unsegmented speech and detect similar patterns. LSTM also has the ability to recognize the heart sounds signals or ECG beats. The basic architecture of LSTM contains four major elements, i.e., cell, input gate, output gate, and forget gate. The cell has a responsibility to remember the values over specific time intervals and the gates regulate the flow of information between the cells. Figure 5 shows the internal working of LSTM architecture.Figure 5
LSTM-based architecture.


### 4.1.3. Autoencoder

Autoencoder belongs to the family of artificial neural network- (ANN-) based architecture, which is used for training efficient data coding in an unsupervised manner. They are recognized as a tool to learn basic patterns for a similar set of data. Autoencoder also performs dimensionality reduction, and it tries to generate a class similar to its original input. In the cited research studies, the author proposed different variations in autoencoder-based architecture. Figure6 shows the basic architecture of the autoencoder.Figure 6
AE-based architecture.


### 4.1.4. Convolutional Neural Network (CNN)

CNN is a deep learning-based algorithm. CNN is widely recognized as a tool in the field of computer vision and image processing. It consists of an input layer, hidden layers, and the last output layer. In forward pass convolutional neural network, the middle layers are called hidden layers that are masked with activation function (ReLU), pooling layer, and convolution. Figure7 shows the structure of the forward pass convolutional neural network. In the state-of-the-art research studies, CNN is widely recognized as a tool for the detection of ECG beats signals.Figure 7
Structure of a forward-pass CNN.


### 4.1.5. Deep Neural Network (DNN)

A simple DNN contains multiple hidden layers that can process the input to output layers [60]. The DNN can recognize different kinds of unstructured data. In arrhythmia classification, authors proposed different kinds of neural networks but the proposed network is composed of the same components: neuron, weight, bias, and function. All these components are capable and act just like the human brain. The deep neural network is widely recognized approach for ECG image classification. Figure 8 shows the basic structure of DNN-based architecture.Figure 8
Structure of basic DNN architecture.


### 4.1.6. Deep Belief Network (DBN)

DBN is a class of deep neural networks; it consists of multiple layers that have a connection between all the layers in the network but not with units of each other layers. DBN can be trained by supervision to achieve better prediction. Figure9 shows the basic structure of DBN-based architecture.Figure 9
Structure of basic DBN architecture.


## 4.1.1. Recurrent Neural Network (RNN)

RNN architecture belongs to the family of neural networks. RNN allows information to be stored in loops within the adjacent layers. RNN is a popular network that uses reasoning from past experiences to train upcoming events. RNN-based deep architecture is widely used in arrhythmia classification systems that can train the sequence vectors. Figure4 shows the internal working of RNN architecture.Figure 4
RNN architecture.


## 4.1.2. Long Short-Term Memory (LSTM)

LSTM is the reproduction of RNN architecture [59] commonly used in the field of deep learning. In general feedforward, neurons have forward connections, but LSTM has reversible feedback connections. LSTM processes data points like images or it has the capabilities to process the sequences of data, such as continuous images, video, or speech signals. LSTM architecture has the capabilities to recognize unsegmented speech and detect similar patterns. LSTM also has the ability to recognize the heart sounds signals or ECG beats. The basic architecture of LSTM contains four major elements, i.e., cell, input gate, output gate, and forget gate. The cell has a responsibility to remember the values over specific time intervals and the gates regulate the flow of information between the cells. Figure 5 shows the internal working of LSTM architecture.Figure 5
LSTM-based architecture.


## 4.1.3. Autoencoder

Autoencoder belongs to the family of artificial neural network- (ANN-) based architecture, which is used for training efficient data coding in an unsupervised manner. They are recognized as a tool to learn basic patterns for a similar set of data. Autoencoder also performs dimensionality reduction, and it tries to generate a class similar to its original input. In the cited research studies, the author proposed different variations in autoencoder-based architecture. Figure6 shows the basic architecture of the autoencoder.Figure 6
AE-based architecture.


## 4.1.4. Convolutional Neural Network (CNN)

CNN is a deep learning-based algorithm. CNN is widely recognized as a tool in the field of computer vision and image processing. It consists of an input layer, hidden layers, and the last output layer. In forward pass convolutional neural network, the middle layers are called hidden layers that are masked with activation function (ReLU), pooling layer, and convolution. Figure7 shows the structure of the forward pass convolutional neural network. In the state-of-the-art research studies, CNN is widely recognized as a tool for the detection of ECG beats signals.Figure 7
Structure of a forward-pass CNN.


## 4.1.5. Deep Neural Network (DNN)

A simple DNN contains multiple hidden layers that can process the input to output layers [60]. The DNN can recognize different kinds of unstructured data. In arrhythmia classification, authors proposed different kinds of neural networks but the proposed network is composed of the same components: neuron, weight, bias, and function. All these components are capable and act just like the human brain. The deep neural network is widely recognized approach for ECG image classification. Figure 8 shows the basic structure of DNN-based architecture.Figure 8
Structure of basic DNN architecture.


## 4.1.6. Deep Belief Network (DBN)

DBN is a class of deep neural networks; it consists of multiple layers that have a connection between all the layers in the network but not with units of each other layers. DBN can be trained by supervision to achieve better prediction. Figure9 shows the basic structure of DBN-based architecture.Figure 9
Structure of basic DBN architecture.


## 4.2. ECG Databases

The expert systems for automatic detection of arrhythmia disease required training data to understand different patterns of each class. Authors of the selected research study critically analyze arrhythmia classification systems and enlist the most cited/publicly open access ECG databases. The ECG databases available for the scientific community used as a data source are listed in Table4.Table 4
ECG databases for arrhythmia classification.
DatabaseNo. of recordsECG leadsAnnotationMITDB4702Normal, abnormalPTB54915MIPhysioNet challengeTrain 852801Normal, noise, other, and AFTest 3658QT10502P Q R ST wavesCART 12-lead7512AF, AVPAF5016 beats/sampleNormal, abnormalAFIB2502MI typesPhysioBankTrain: 687712NormalTest: 2954AbnormalMendeley ECG data168712Normal/abnormal/MI/PMIThe database as mentioned above contains variations of ECG beats with different types of arrhythmia classification. The data sources have unique importance with domain-specific characteristics. The description of the patient population in which these ECG beats were obtained is important in interpreting the methodology and clinical utility in context.


## 5. Limitations

Arrhythmia classification techniques are critically analyzed; the researcher puts lots of effort into developing the automated system with good accuracy results. The developed system may act as clinical support for the healthcare professionals but with having few limitations. The majority of the selected systems are limited to the following:(1)
The most ECG databases are not specific to their clinical context.(2)
The description of the patient population in which these ECGs were obtained is lacking. This is important in interpreting the methodology and clinical utility in context.(3)
The algorithms are trained based on specific environments, and the generalized methodologies are ignored.


## 6. Conclusion

Cardiac disorder or arrhythmia is the most dangerous disease that leads to human death. The researcher proposed lots of arrhythmia classification systems to assist the doctors every year. The automated systems predict the high accuracy results used for arrhythmia detection but still not adoptable by healthcare professionals because in the recent studies, authors used time-series data, which is not adaptable in different application environments. Moreover, ECG’s time-series data with signal leads are not appropriate for stable baseline wanders, muscle contraction, and power line interface. In general, the practical methods generally adopted by a cardiologist for screening cardiac patients are 12 leads-based ECG images. The major concerns that affect the success of the developed arrhythmia detection systems are (i) manual features selection, (ii) techniques used for features extraction, and (iii) algorithm used for classification and the most important is the use of imbalanced data for classification. The automated arrhythmia detection required the feature extraction of ECG images that required domain knowledge. Further, the balanced dataset used for classification methods is required to avoid overfitting. In the last few years, deep learning-based systems are being recognized as a tool in healthcare institutes that have capabilities to automatically extract high-level abstract features, avoiding laborious manual feature design. Finally, this study can help the researcher get a deeper understanding of arrhythmia classification and the deep learning methods used to develop automated systems.



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*Source: 9919588-2021-04-20.xml*