Electrocardiogram Arrhythmia Classification Research Articles

ECG Arrhythmia Classification using Deep Learning

An electrocardiogram (ECG) is a painless, noninvasive way to help diagnose numerous common heart problems. ECG plays an important role in diagnosing various Cardiac ailments. In recent years, Deep learning techniques have shown remarkable promise in achieving accurate and automated ECG arrhythmia classification. The primary goal of the system is to develop a robust and accurate system for the automated detection and classification of arrhythmias in electrocardiogram (ECG) data. By leveraging state-of-the-art techniques such as Convolutional Neural Networks (CNNs), we analyze pattern recognition within ECG signals to detect arrhythmias. Furthermore, we address the challenge of dataset scarcity by augmenting the data through nine different image cropping methods during the training phase. The implementation of techniques like Batch Normalization and data augmentation will further enhance the model's adaptability to diverse data sources, making it an invaluable tool for healthcare professionals. The CNN will be trained and tested using the ECG Dataset obtained from the MIT-BIH Database and from it, seven types of signals of arrhythmia will be classified. These seven signals are Premature Ventricular contractions (PVC), Paced beat (PAB), Right bundle branch block beat (RBB), Left bundle branch block beat (LBB), Atrial premature contraction (APC), Ventricular escape beat (VEB) and Normal beat. This system bridges the gap between advanced technology and healthcare, offering a transformative approach to ECG arrhythmia classification that has the potential to significantly improve patient outcomes and reduce the burden of manual diagnosis

CAT-Net: Convolution, attention, and transformer based network for single-lead ECG arrhythmia classification

Machine learning technologies have been applied extensively in the last decade to automatically detect and analyze various forms of arrhythmia from electrocardiogram (ECG) signals. Existing deep learning-based models focus on enhancing classification performance by exploring spatial–temporal ECG features or by implementing multi-modal and ensemble classifiers. Such approaches perform well but do not provide comprehensive accessibility for real-life applications due to the multi-lead ECG requirement. To address the issue, a single-lead ECG based network is considered. The proposed convolution, attention, and transformer-based network (CAT-Net) exhibits promising performance on arrhythmia classification by adeptly capturing local and global heartbeats’ morphological characteristics. Along with the local information captured by the convolution layer, the contextual ECG information is extracted by the multi-head attention layer present in the transformer encoder. In addition, most of the existing models suffer from lower predictive performance in minority-class arrhythmias due to the highly imbalanced ECG data. To enhance the predictive performance in minority classes, three balancing techniques – SMOTE-ENN, SMOTE-Tomek, and ADASYN – are systematically evaluated, and SMOTE-Tomek is ultimately integrated. To mitigate potential dataset bias, CAT-Net was assessed across two distinct datasets: MIT-BIH and INCART, respectively, and was shown to achieve state-of-the-art performance. CAT-Net establishes new benchmarks, achieving 99.14% overall accuracy and 94.69% macro F1 score on 5-class arrhythmia classification in the MIT-BIH dataset and 99.58% accuracy with 96.15% macro F1 score for 3-class classification in the INCART dataset.

ECGTransForm: Empowering adaptive ECG arrhythmia classification framework with bidirectional transformer

Cardiac arrhythmias, deviations from the normal rhythmic beating of the heart, are subtle yet critical indicators of potential cardiac challenges. Efficiently diagnosing them requires intricate understanding and representation of both spatial and temporal features present in Electrocardiogram (ECG) signals. This paper introduces ECGTransForm, a deep learning framework tailored for ECG arrhythmia classification. By embedding a novel Bidirectional Transformer (BiTrans) mechanism, our model comprehensively captures temporal dependencies from both antecedent and subsequent contexts. This is further augmented with Multi-scale Convolutions and a Channel Recalibration Module, ensuring a robust spatial feature extraction across various granularities. We also introduce a Context-Aware Loss (CAL) that addresses the class imbalance challenge inherent in ECG datasets by dynamically adjusting weights based on class representation. Extensive experiments reveal that ECGTransForm outperforms contemporary models, proving its efficacy in extracting meaningful features for arrhythmia diagnosis. Our work offers a significant step towards enhancing the accuracy and efficiency of automated ECG-based cardiac diagnoses, with potential implications for broader cardiac care applications. The source code is available at https://github.com/emadeldeen24/ECGTransForm.

Arrhythmia classification detection based on multiple electrocardiograms databases.

According to the World Health Organization, cardiovascular diseases are the leading cause of deaths globally. Electrocardiogram (ECG) is a non-invasive approach for detecting heart diseases and reducing the risk of heart disease-related death. However, there are limited numbers of ECG samples and imbalance distribution for existing ECG databases. It is difficult to train practical and efficient neural networks. Based on the analysis and research of many existing ECG databases, this paper conduct an in-depth study on three fine-labeled ECG databases, to extract heartbeats, unify the sampling frequency, and propose a self-processing method of heartbeats, and finally form a unified ECG arrhythmia classification database, noted as Hercules-3. It is separated into training sets (80%) and testing sets (the remaining 20%). In order to verify its capabilities, we have trained a 16-classification fully connected neural network based on Hercules-3 and it achieves an accuracy rate of up to 98.67%. Compared with other data processing, our proposed method improves classification recall by at least 6%, classification accuracy by at least 4%, and F1-score by at least 7%.

ECG Arrhythmia Classification Using Recurrence Plot and ResNet-18

Cardiovascular diseases are the leading cause of death worldwide, claiming approximately
 17.9 million lives each year. In this study, a novel CAD system to detect and classify electrocardiogram (ECG) signals is presented. Designed system employs the recurrence plot (RP) approach that transforms a ECG signal into a 2D representative colour image, finally performing their classifications via employment of Deep Learning architecture (ResNet-18). Novel system includes two steps, where the first step is the preprocessing one, which performs segmentation of the data into two-second intervals, finally forming images via the RP approach; following, in the second step, the RP images are classified by the ResNet- 18 network. The proposed method is evaluated on the MIT-BIH arrhythmia database where 5 principal types of arrhythmias that have medical relevance should be classified. Novel system can classify the before-mentioned quantity of diseases according to the AAMI Standard and appears to demonstrate good performance in terms of criteria: overall accuracy of 97.62%, precision of 95.42%, recall of 95.42%, F1-Score of 95.06%, and AUC of 95.7% that are competitive with better state-of-the-art systems. Additionally. the method demonstrated the ability in mitigating the problem of imbalanced samples.

SCL-ST: Supervised Contrastive Learning With Semantic Transformations for Multiple Lead ECG Arrhythmia Classification.

The automatic classification of electrocardiogram (ECG) signals has played an important role in cardiovascular diseases diagnosis and prediction. With recent advancements in deep neural networks (DNNs), particularly Convolutional Neural Networks (CNNs), learning deep features automatically from the original data is becoming an effective and widespread approach in a variety of intelligent tasks including biomedical and health informatics. However, most of the existing approaches are trained on either 1D CNNs or 2D CNNs, and they suffer from the limitations of random phenomena (i.e. random initial weights). Furthermore, the ability to train such DNNs in a supervised manner in healthcare is often limited due to the scarcity of labeled training data. To address the problems of weight initialization and limited annotated data, in this work, we leverage recent self-supervised learning technique, namely, contrastive learning, and present supervised contrastive learning (sCL). Different from existing self-supervised contrastive learning approaches, which often generate false negatives because of random selection of negative anchors, our contrastive learning makes use of labeled data to pull the same class closer together and push different classes far apart to avoid potential false negatives. Furthermore, unlike other kinds of signals (e.g. speech, image, video), ECG signal is sensitive to changes, and inappropriate transformation could directly affect diagnosis results. To deal with this issue, we present two semantic transformations, i.e. semantic split-join and semantic weighted peaks noise smoothing. The proposed deep neural network sCL-ST with supervised contrastive learning and semantic transformations is trained as an end-to-end framework for the multi-label classification of 12-lead ECGs. Our sCL-ST network contains two sub-networks i.e. pre-text task and down-stream task. Our experimental results have been evaluated on 12-lead PhysioNet 2020 dataset and shown that our proposed network outperforms the state-of-the-art existing approaches.

ECG Heartbeat Classification Using Machine Learning and Metaheuristic Optimization for Smart Healthcare Systems.

Early diagnosis and classification of arrhythmia from an electrocardiogram (ECG) plays a significant role in smart healthcare systems for the health monitoring of individuals with cardiovascular diseases. Unfortunately, the nonlinearity and low amplitude of ECG recordings make the classification process difficult. Thus, the performance of most traditional machine learning (ML) classifiers is questionable, as the interrelationship between the learning parameters is not well modeled, especially for data features with high dimensions. To address the limitations of ML classifiers, this paper introduces an automatic arrhythmia classification approach based on the integration of a recent metaheuristic optimization (MHO) algorithm and ML classifiers. The role of the MHO is to optimize the search parameters of the classifiers. The approach consists of three steps: the preprocessing of the ECG signal, the extraction of the features, and the classification. The learning parameters of four supervised ML classifiers were utilized for the classification task; support vector machine (SVM), k-nearest neighbors (kNNs), gradient boosting decision tree (GBDT), and random forest (RF) were optimized using the MHO algorithm. To validate the advantage of the proposed approach, several experiments were conducted on three common databases, including the Massachusetts Institute of Technology (MIT-BIH), the European Society of Cardiology ST-T (EDB), and the St. Petersburg Institute of Cardiological Techniques 12-lead Arrhythmia (INCART). The obtained results showed that the performance of all the tested classifiers were significantly improved after integrating the MHO algorithm, with the average ECG arrhythmia classification accuracy reaching 99.92% and a sensitivity of 99.81%, outperforming the state-of the-art methods.

Electrocardiogram classification using TSST-based spectrogram and ConViT.

As an important auxiliary tool of arrhythmia diagnosis, Electrocardiogram (ECG) is frequently utilized to detect a variety of cardiovascular diseases caused by arrhythmia, such as cardiac mechanical infarction. In the past few years, the classification of ECG has always been a challenging problem. This paper presents a novel deep learning model called convolutional vision transformer (ConViT), which combines vision transformer (ViT) with convolutional neural network (CNN), for ECG arrhythmia classification, in which the unique soft convolutional inductive bias of gated positional self-attention (GPSA) layers integrates the superiorities of attention mechanism and convolutional architecture. Moreover, the time-reassigned synchrosqueezing transform (TSST), a newly developed time-frequency analysis (TFA) method where the time-frequency coefficients are reassigned in the time direction, is employed to sharpen pulse traits for feature extraction. Aiming at the class imbalance phenomena in the traditional ECG database, the smote algorithm and focal loss (FL) are used for data augmentation and minority-class weighting, respectively. The experiment using MIT-BIH arrhythmia database indicates that the overall accuracy of the proposed model is as high as 99.5%. Furthermore, the specificity (Spe), F1-Score and positive Matthews Correlation Coefficient (MCC) of supra ventricular ectopic beat (S) and ventricular ectopic beat (V) are all more than 94%. These results demonstrate that the proposed method is superior to most of the existing methods.

Assessment of Compressed and Decompressed ECG Databases for Telecardiology Applying a Convolution Neural Network

Incalculable numbers of patients in hospitals as a result of COVID-19 made the screening of heart patients arduous. Patients who need regular heart monitoring were affected the most. Telecardiology is used for regular remote heart monitoring of such patients. However, the resultant huge electrocardiogram (ECG) data obtained through regular monitoring affects available storage space and transmission bandwidth. These signals can take less space if stored or sent in a compressed form. To recover them at the receiver end, they are decompressed. We have combined telecardiology with automatic ECG arrhythmia classification using CNN and proposed an algorithm named TELecardiology using a Deep Convolution Neural Network (TELDCNN). Discrete cosine transform (DCT), 16-bit quantization, and run length encoding (RLE) were used for compression, and a convolution neural network (CNN) was applied for classification. The database was formed by combining real-time signals (taken from a designed ECG device) with an online database from Physionet. Four kinds of databases were considered and classified. The attained compression ratio was 2.56, and the classification accuracies for compressed and decompressed databases were 0.966 and 0.990, respectively. Comparing the classification performance of compressed and decompressed databases shows that the decompressed signals can classify the arrhythmias more appropriately than their compressed-only form, although at the cost of increased computational time.

A Near-Sensor ECG Delineation and Arrhythmia Classification System

Cardiovascular disease (CVD) has become the leading cause of death worldwide. As a widely used method for diagnosing CVD, currently electrocardiogram (ECG) monitoring tends to be implemented in wearable devices. This paper presents the prototype an ECG delineation and arrhythmia classification (EDAC) system suitable for wearable ECG biosensors. The proposed EDAC system is intended to be implemented after the electrodes and the analog front-end circuit, and its aim is signal processing at a low hardware overhead. The system consists of a Delta-modulator-based analog-to-feature converter (AFC), a corresponding ECG detection/delineation/feature extraction algorithm (DDF), an automatic gain controller (AGC) block, and a patient-dependent linear kernel support vector machine (SVM) classifier. The AFC converts the input analog signal into digital data of the slope and slope variation of the input signal, which is then used for detecting QRS complexes, localizing the fiducial points, and extracting the feature vectors for each heartbeat in the DDF block. At the same time, the AGC sends out a gain control signal based on the detected QRS complex to adjust the gain of the front-end amplifier. Finally, the SVM block performs arrhythmia classification. The EDAC system performance is evaluated using the MIT-BIH arrhythmia database. The system achieves 0.88% (0.93%), 99.1% (99.1%), 87.0% (92.8%), 99.6% (99.5%), and 89.3% (92.9%) in F1 score, accuracy, sensitivity, specificity, and positive predictive values of the supraventricular ectopic beats (ventricular ectopic beats) versus normal heartbeats classification while maintaining a low power dissipation ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.66~\mu \text{W}$ </tex-math></inline-formula> at 1 kHz operating frequency in the front-end AFC block). The proposed system is attractive to future wearable long-term ECG monitoring biosensors.

Classification of Imbalanced Electrocardiosignal Data using Convolutional Neural Network

Background and objective: In the application of wearable heart-monitors, it is of great significance to analyze electrocardiogram (ECG) signals for anomaly detection. ECG arrhythmia classification remains an open problem in that it cannot easily recognize data from minority classes due to the imbalanced dataset and particular characteristic of the time series signal. In this study, a novel method is presented as a possible solution to imbalanced classification problems. Methods: An improved data augmentation method based on variational auto-encoder (VAE) and auxiliary classifier generative adversarial network (ACGAN) is implemented to address the difficulties resulting from the imbalanced dataset. Based on the augmented dataset, convolutional neural network (CNN) classifiers are employed to automatically recognize arrhythmias using two-dimensional ECG images. Results: In experimental studies conducted with the MIT-BIH arrhythmia database, the proposed method achieves 98.45% accuracy and 97.03% sensitivity. The sensitivities of two minority classes achieve 95.83% and 97.37%, respectively. Conclusion: In imbalanced classification, the sensitivity of minority class is a key evaluation indicator. One of the significant contributions of this study is that the proposed method can obtain higher sensitivity of minority class. The experimental results demonstrate that the proposed method for ECG arrhythmia calssification under imbalanced data has better performance compared with traditional cropping augmentation methods and traditional classifiers.

Heart Disease Prediction System

Abstract: Heart diseases are the one of the primary reasons of human death today. There are many recent technologies are used to assist the medical professionals and doctors in the prediction of heart disease in the early stage. Prediction of heart disease is a critical challenge in the area of clinical data analysis. This paper introduces a technique to detect arrhythmia, which is a representative type of cardio vascular diseases. Arrhythmia refers to any irregular change from the normal heart rhythms, means that your heart beats too quickly, too slowly, or with an irregular pattern. The Electro Cardiogram (ECG) is used as an input for the arrhythmia detection. It displays the rhythm and status of the heart. This paper propose an effective ECG arrhythmia classification approach based on a deep convolutional neural network (CNN), which has lately demonstrated remarkable performance in the field of machine learning. It perform the classification without any manual pre-processing of the ECG signals such as noise filtering, feature extraction, and feature reduction. Keywords: Arrhythmia, ECG, deep learning, CNN, ResNet

An ECG Heartbeat Classification Method Based on Deep Convolutional Neural Network.

The electrocardiogram (ECG) is one of the most powerful tools used in hospitals to analyze the cardiovascular status and check health, a standard for detecting and diagnosing abnormal heart rhythms. In recent years, cardiovascular health has attracted much attention. However, traditional doctors' consultations have disadvantages such as delayed diagnosis and high misdiagnosis rate, while cardiovascular diseases have the characteristics of early diagnosis, early treatment, and early recovery. Therefore, it is essential to reduce the misdiagnosis rate of heart disease. Our work is based on five different types of ECG arrhythmia classified according to the AAMI EC57 standard, namely, nonectopic, supraventricular ectopic, ventricular ectopic, fusion, and unknown beat. This paper proposed a high-accuracy ECG arrhythmia classification method based on convolutional neural network (CNN), which could accurately classify ECG signals. We evaluated the classification effect of this classification method on the supraventricular ectopic beat (SVEB) and ventricular ectopic beat (VEB) based on the MIT-BIH arrhythmia database. According to the results, the proposed method achieved 99.8% accuracy, 98.4% sensitivity, 99.9% specificity, and 98.5% positive prediction rate for detecting VEB. Detection of SVEB achieved 99.7% accuracy, 92.1% sensitivity, 99.9% specificity, and 96.8% positive prediction rate.

Effective compression and classification of ECG arrhythmia by singular value decomposition

Electrocardiogram (ECG) monitoring systems are widely applied to tele-cardiology healthcare programs nowadays, where ECG signals should always be compressed first during its transmission and storage. Previous studies attempted to achieve high quality decompressed signal with compression ratio as high as possible. In this paper, we investigated the performance on ECG arrhythmia classification on ECG signal decompressed after lossy compression with a high compression ratio. We proposed a simple but efficient method utilizing singular value decomposition (SVD) to decompose ECG signals, then applied the decompressed data to a convolutional neural network (CNN) and supporting vector machine (SVM) for classification. Using the optimization method with accuracy and compression ratio as objective functions, the highest average accuracy obtained is above 96% when the selected number of singular value is only 3. The evaluation results illustrated that the decompressed ECG signal even with a relatively high distortion can still achieve a satisfying performance in the arrhythmia classification.Thus,we proved that the real-time nature of the remote mobile ECG monitoring system can be greatly improved and countless people who are in need of ECG diagnosis can benefit from it.

Constrained transformer network for ECG signal processing and arrhythmia classification

BackgroundHeart disease diagnosis is a challenging task and it is important to explore useful information from the massive amount of electrocardiogram (ECG) records of patients. The high-precision diagnostic identification of ECG can save clinicians and cardiologists considerable time while helping reduce the possibility of misdiagnosis at the same time.Currently, some deep learning-based methods can effectively perform feature selection and classification prediction, reducing the consumption of manpower.MethodsIn this work, an end-to-end deep learning framework based on convolutional neural network (CNN) is proposed for ECG signal processing and arrhythmia classification. In the framework, a transformer network is embedded in CNN to capture the temporal information of ECG signals and a new link constraint is introduced to the loss function to enhance the classification ability of the embedding vector.ResultsTo evaluate the proposed method, extensive experiments based on real-world data were conducted. Experimental results show that the proposed model achieve better performance than most baselines. The experiment results also proved that the transformer network pays more attention to the temporal continuity of the data and captures the hidden deep features of the data well. The link constraint strengthens the constraint on the embedded features and effectively suppresses the effect of data imbalance on the results.ConclusionsIn this paper, an end-to-end model is used to process ECG signal and classify arrhythmia. The model combine CNN and Transformer network to extract temporal information in ECG signal and is capable of performing arrhythmia classification with acceptable accuracy. The model can help cardiologists perform assisted diagnosis of heart disease and improve the efficiency of healthcare delivery.

An efficient ECG arrhythmia classification method based on Manta ray foraging optimization

The Electrocardiogram (ECG) arrhythmia classification has become an interesting research area for researchers and developers as it plays a vital role in early prevention and diagnosis of cardiovascular diseases. In ECG signal classification, the feature extraction and selection processes are critical steps. Thus, in this paper, different ECG signal descriptors based on one-dimensional local binary pattern (LBP), wavelet, higher-order statistical (HOS), and morphological information are introduced for feature extraction. For feature selection and classification processes, a new hybrid ECG arrhythmia classification approach called MRFO-SVM that combines a metaheuristic algorithm termed Manta ray foraging optimization (MRFO) with support vector machine (SVM) is proposed to automatically determine the relevance features of LBP, HOS, wavelet and magnitude values. In MRFO-SVM approach, the MRFO is utilized to optimize the parameters of SVM and to select the significant features subset that provides the best classification performance, meanwhile SVM is used for classification purposes. The proposed MRFO-SVM approach is trained on the MIT-BIH Arrhythmia database containing four abnormal and one normal heartbeats. The experimental results of ECG arrhythmia classification using the proposed MRFO-SVM revealed with evidence its superiority with overall classification accuracy of 98.26% over seven well-known metaheuristic algorithms.

WITHDRAWN: Review of ECG arrhythmia classification using deep neural network

Recently, Deep Learning (DL) or Deep Neural Network has become a focus of research in diverse fields, including medical and healthcare, where early identification of electrocardiogram (ECG) disturbances is very helpful in healthcare management. This paper provides a thorough overview of the new DL approaches used for classification purposes to the ECG signal. This research explores different types of DL techniques, such as ResNet, InceptionV3, Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM). In many cases, CNN is mostly used as the appropriate technique for extraction of useful features.

ECG signal classification using capsule neural networks

Cardiovascular diseases (CVD) are the dominant cause of deaths in the world, of which 90% are curable. The electrocardiogram (ECG) measures the electrical stimulus of the heart noninvasively. Convolutional neural networks (CNN) act as one of the powerful machine learning techniques to classify ECG arrhythmia classification and other CVDs. Nonetheless, they have some functional flaws like ignorance of spatial hierarchies between the features and are unable to acquire a rotational invariance. To overcome these problems of CNN, a novel neural network named capsule network (CapsNet) is proposed as an efficient algorithm to provide error-free implementation of deep learning over the databases. The main focus of this work is to apply and implement CapsNet for ECG signal classification from the MIT-BIH database and compare its efficiency with the pretrained CNN networks.

Performance Analysis of ECG Arrhythmia Classification based on Different SVM Methods

Heart arrhythmias are the different types of heartbeats which are irregular in nature. In Tachycardia the heartbeat works too fast and in case of Bradycardia it works too slow. In the study of different cardiac conditions automatic detection of heart arrhythmia is done by the classification and feature extraction of Electrocardiogram(ECG) data. Various Support Vector Machine based methods are used to analyze and classify ECG signals for arrhythmia detection. There are several Support Vector Machine (SVM) methods used to classify the ECG data such as one against all, one against one and fuzzy decision function. This classification detects the existence of the arrhythmia and it helps the physicians to treat the heart patient with more accurate way. To train SVM, the MIT BIH Arrhythmia database is used which works with the heart disorder like sinus bradycardy, old inferior myocardial infarction, coronary artery disease, right bundle branch block. All three methods are implemented in proper way, and their rate of accuracy with SVM classifier is optimal when it is processed with the one-against-all method. The data sets of ECG arrhythmia are usually complex in nature, so for the SVM based classification one-against-all method has great impact and will fetch better result.

ECG Arrhythmia Classification Using STFT-Based Spectrogram and Convolutional Neural Network

The classification of electrocardiogram (ECG) signals is very important for the automatic diagnosis of heart disease. Traditionally, it is divided into two steps, including the step of feature extraction and the step of pattern classification. Owing to recent advances in artificial intelligence, it has been demonstrated that deep neural network, which trained on a huge amount of data, can carry out the task of feature extraction directly from the data and recognize cardiac arrhythmias better than professional cardiologists. This paper proposes an ECG arrhythmia classification method using two-dimensional (2D) deep convolutional neural network (CNN). The time domain signals of ECG, belonging to five heart beat types including normal beat (NOR), left bundle branch block beat (LBB), right bundle branch block beat (RBB), premature ventricular contraction beat (PVC), and atrial premature contraction beat (APC), were first transformed into time-frequency spectrograms by short-time Fourier transform. Subsequently, the spectrograms of the five arrhythmia types were utilized as input to the 2D-CNN such that the ECG arrhythmia types were identified and classified finally. Using ECG recordings from the MIT-BIH arrhythmia database as the training and testing data, the classification results show that the proposed 2D-CNN model can reach an averaged accuracy of 99.00%. On the other hand, in order to achieve optimal classification performances, the model parameter optimization was investigated. It was found when the learning rate is 0.001 and the batch size parameter is 2500, the classifier achieved the highest accuracy and the lowest loss. We also compared the proposed 2D-CNN model with a conventional one-dimensional CNN model. Comparison results show that the 1D-CNN classifier can achieve an averaged accuracy of 90.93%. Therefore, it is validated that the proposed CNN classifier using ECG spectrograms as input can achieve improved classification accuracy without additional manual pre-processing of the ECG signals.