RETRACTED ARTICLE: Composite feature vector based cardiac arrhythmia classification using convolutional neural networks

Ramesh, Gopisetty; Satyanarayana, D.; Sailaja, M.

doi:10.1007/s12652-020-02259-6

Cited by 16 publications

(5 citation statements)

References 29 publications

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“…A composite feature vector is produced and fed into a CNN classifier for label prediction. On the MIT-BIH database, the system obtains an average classification accuracy of 98% [ 40 ]. They used AI (ML and DL) to estimate the patients’ potential for acquiring cardiovascular disease.…”

Section: Related Workmentioning

confidence: 99%

Reseek-Arrhythmia: Empirical Evaluation of ResNet Architecture for Detection of Arrhythmia

Haq,

Bazai,

Fatima

et al. 2023

Diagnostics

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Arrhythmia is a cardiac condition characterized by an irregular heart rhythm that hinders the proper circulation of blood, posing a severe risk to individuals’ lives. Globally, arrhythmias are recognized as a significant health concern, accounting for nearly 12 percent of all deaths. As a result, there has been a growing focus on utilizing artificial intelligence for the detection and classification of abnormal heartbeats. In recent years, self-operated heartbeat detection research has gained popularity due to its cost-effectiveness and potential for expediting therapy for individuals at risk of arrhythmias. However, building an efficient automatic heartbeat monitoring approach for arrhythmia identification and classification comes with several significant challenges. These challenges include addressing issues related to data quality, determining the range for heart rate segmentation, managing data imbalance difficulties, handling intra- and inter-patient variations, distinguishing supraventricular irregular heartbeats from regular heartbeats, and ensuring model interpretability. In this study, we propose the Reseek-Arrhythmia model, which leverages deep learning techniques to automatically detect and classify heart arrhythmia diseases. The model combines different convolutional blocks and identity blocks, along with essential components such as convolution layers, batch normalization layers, and activation layers. To train and evaluate the model, we utilized the MIT-BIH and PTB datasets. Remarkably, the proposed model achieves outstanding performance with an accuracy of 99.35% and 93.50% and an acceptable loss of 0.688 and 0.2564, respectively.

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Section: Related Workmentioning

confidence: 99%

Reseek-Arrhythmia: Empirical Evaluation of ResNet Architecture for Detection of Arrhythmia

Haq,

Bazai,

Fatima

et al. 2023

Diagnostics

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“…Cardiac Arrhythmias are one of the CVDs that conquered major in these deaths. 'Arrhythmia' is a heart rate disturbance that is caused by improper electrical conduction or formation in the heart (Ramesh et al, 2021). Recent advancements in Machine learning in bioinformatics and biomedicine have received considerable attention.…”

Section: Introductionmentioning

confidence: 99%

Arrhythmia Classification Based on Bi-Directional Long Short-Term Memory and Multi-Task Group Method

Munawar

Angappan

Konda

2023

International Journal of E-Collaboration

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Early and accurate classification of arrhythmia helps the experts to select the treatment for the patient to increase the recovery rate. The deep learning method of convolution neural network (CNN) is used for classification, and this has an overfitting problem. In this research, the multi-task group bi-directional long short term memory (MTGBi-LSTM) method is proposed to increases the performance of arrhythmia classification. The multi-task learning technique learns two ECG signals in shared representation for effective learning. The global and intra LSTM method selects the relevant feature and easily escapes from local optima. The MTGBi-LSTM model learns the unique features in shared representation that helps to overcome overfitting problem and increases the learning rate of the model. The MTGBi-LSTM model in arrhythmia classification is evaluated on MIT-BIH dataset. The MTGBi-LSTM model has 96.48% accuracy, 97.73% sensitivity, existing AFibNet has 96.36% accuracy, and 93.65% sensitivity for arrhythmia classification in CPSC 2018 dataset.

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“…It has also been utilized to detect any heart damage and examine the effects of heart regulatory devices or drugs. ECG devices with a varying number of electrodes (3)(4)(5)(6)(7)(8)(9)(10)(11)(12) were employed [3] for the signal's acquisition. The ECG signal has a nonstationary nature.…”

Section: Introductionmentioning

confidence: 99%

“…The regular heartbeat would get disrupted, and also the normal heartbeat's morphology could get affected by this improper functioning of the heart. An arrhythmia's [5] two key consequences are Ectopic Beats (EB) and Bundle Branch Block Beats (BBBB). As the overall system's foundation, the ECG sensing network is required for the collection of physiological data from the user as well as this data's transmission via a wireless medium to the IoT cloud.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Convolutional Neural Network for ECG Signal Classification Aggregated Using IoT

Karthiga¹,

Abirami²

2022

Computer Systems Science and Engineering

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Much attention has been given to the Internet of Things (IoT) by citizens, industries, governments, and universities for applications like smart buildings, environmental monitoring, health care and so on. With IoT, network connectivity is facilitated between smart devices from anyplace and anytime. IoT-based health monitoring systems are gaining popularity and acceptance for continuous monitoring and detect health abnormalities from the data collected. Electrocardiographic (ECG) signals are widely used for heart diseases detection. A novel method has been proposed in this work for ECG monitoring using IoT techniques. In this work, a two-stage approach is employed. In the first stage, a routing protocol based on Dynamic Source Routing (DSR) and Routing by Energy and Link quality (REL) for IoT healthcare platform is proposed for efficient data collection, and in the second stage, classification of ECG for Arrhythmia. Furthermore, this work has evaluated Support Vector Machine (SVM), Artificial Neural Network (ANN), and Convolution Neural Networks (CNNs)based approach for ECG signals classification. Deep-ECG will use a deep CNN to extract critical features and then compare through evaluation of simple and fast distance functions in order to obtain an efficient classification of heart abnormalities. For the identification of abnormal data, this work has proposed techniques for the classification of ECG data, which has been obtained from mobile watch users. For experimental verification of the proposed methods, the Beth Israel Hospital (MIT/BIH) Arrhythmia and Massachusetts Institute of Technology (MIT) Database was used for evaluation. Results confirm the presented method's superior performance with regards to the accuracy of classification. The CNN achieved an accuracy of 91.92% and has a higher accuracy of 4.98% for the SVM and 2.68% for the ANN.

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RETRACTED ARTICLE: Composite feature vector based cardiac arrhythmia classification using convolutional neural networks

Cited by 16 publications

References 29 publications

Reseek-Arrhythmia: Empirical Evaluation of ResNet Architecture for Detection of Arrhythmia

Reseek-Arrhythmia: Empirical Evaluation of ResNet Architecture for Detection of Arrhythmia

Arrhythmia Classification Based on Bi-Directional Long Short-Term Memory and Multi-Task Group Method

Deep Learning Convolutional Neural Network for ECG Signal Classification Aggregated Using IoT

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