Automated Deep Learning Based Cardiovascular Disease Diagnosis Using ECG Signals

Karthik, S.; Madasthu, Santhosh; Kavitha, M.; Paul, Arnab

doi:10.32604/csse.2022.021698

Cited by 13 publications

(3 citation statements)

References 25 publications

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“…In Table 2 , there is a detailed

examination of the MROA-DLECGSC methodology with recent techniques [ 5 , 21 ], such as deep learning-based 1D biomedical ECG signal recognition for cardiovascular disease diagnosis (DLECG-CVD), DL-based ECG signal analysis (DL-ECGA), gradient-boosting tree (GBT), random forest (RF), one-dimensional deep convolutional neural network (1-DCNN), logistic regression (LR), decision tree (DT), and K Neighbors Classifier (KNC). The simulation values indicate that the LR and DT models obtained lower

of 37.38% and 27.90%, respectively.…”

Section: Resultsmentioning

confidence: 99%

Mud Ring Optimization Algorithm with Deep Learning Model for Disease Diagnosis on ECG Monitoring System

Alluhaidan

Maashi

Arasi

et al. 2023

Sensors

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Due to the tremendous growth of the Internet of Things (IoT), sensing technologies, and wearables, the quality of medical services has been enhanced, and it has shifted from standard medical-based health services to real time. Commonly, the sensors can be combined as numerous clinical devices to store the biosignals generated by the physiological actions of the human body. Meanwhile, a familiar method with a noninvasive and rapid biomedical electrocardiogram (ECG) signal can be used to diagnose and examine cardiovascular disease (CVD). As the growing number of patients is destroying the classification outcome because of major changes in the ECG signal patterns among numerous patients, computer-assisted automatic diagnostic tools are needed for ECG signal classification. Therefore, this study presents a mud ring optimization technique with a deep learning-based ECG signal classification (MROA-DLECGSC) technique. The presented MROA-DLECGSC approach recognizes the presence of heart disease using ECG signals. To accomplish this, the MROA-DLECGSC technique initially preprocessed the ECG signals to transform them into a uniform format. In addition, the Stacked Autoencoder Topographic Map (SAETM) approach was utilized for the classification of ECG signals to identify the presence of CVDs. Finally, the MROA was applied as a hyperparameter optimizer, which assisted in accomplishing enhanced performance. The experimental outcomes of the MROA-DLECGSC algorithm were tested on the benchmark database, and the results show the better performance of the MROA-DLECGSC methodology compared to other recent algorithms.

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“…In Table 2 , there is a detailed

of 37.38% and 27.90%, respectively.…”

Section: Resultsmentioning

confidence: 99%

Mud Ring Optimization Algorithm with Deep Learning Model for Disease Diagnosis on ECG Monitoring System

Alluhaidan

Maashi

Arasi

et al. 2023

Sensors

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“…Efforts to date using even limited amounts of properly annotated ECG data suggest that this could be a productive approach for early detection of abnormalities and diagnosis. 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 However, it is not easy for experts to manually add clinical insights to real ECGs from multiple perspectives, and this is a serious obstacle facing this AI-based research strategy. The acquisition, analysis, and labeling of electrocardiograms is extremely time consuming, requiring both specialized expertise and specialized equipment.…”

Section: Introductionmentioning

confidence: 99%

Variational autoencoder–based neural electrocardiogram synthesis trained by FEM-based heart simulator

Nishikimi,

Nakano,

Kashino

et al. 2024

Cardiovascular Digital Health Journal

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“…A general overview of ECG arrhythmia classification using machine learning and deep learning methods is presented in ( Luz et al, 2016 ; Kooman et al, 2020 ; Xie et al, 2020 ; Hong et al, 2021 ; NehaSardana et al, 2021 ; Merdjanovska and Rashkovska, 2022 ). There are many different databases available for arrhythmia research, such as PTB-XL ( Wagner et al, 2020 ; Prabhakararao and Dandapat, 2021 ; Smigiel et al, 2021 ; Karthik et al, 2022 ; Palczynski et al, 2022 ), and MIT-BIH ( Acharya et al, 2017 ; Goldberger et al, 2000 ; Sayantan et al, 2018 ; Nurmaini et al, 2020 ; Yildirim et al, 2018 ; Huang et al, 2019 ; Wang et al, 2019 ). In general, many well-designed methods were proposed in the past few years.…”

Section: Introductionmentioning

confidence: 99%

A robust multiple heartbeats classification with weight-based loss based on convolutional neural network and bidirectional long short-term memory

2022

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Background: Analysis of electrocardiogram (ECG) provides a straightforward and non-invasive approach for cardiologists to diagnose and classify the nature and severity of variant cardiac diseases including cardiac arrhythmia. However, the interpretation and analysis of ECG are highly working-load demanding, and the subjective may lead to false diagnoses and heartbeats classification. In recent years, many deep learning works showed an excellent role in accurate heartbeats classification. However, the imbalance of heartbeat classes is universal in most of the available ECG databases since abnormal heartbeats are always relatively rare in real life scenarios. In addition, many existing approaches achieved prominent results by removing noise and extracting features in data preprocessing, which relies heavily on powerful computers. It is a pressing need to develop efficient and automatic light weighted algorithms for accurate heartbeats classification that can be used in portable ECG sensors.Objective: This study aims at developing a robust and efficient deep learning method, which can be embedded into wearable or portable ECG monitors for classifying heartbeats.Methods: We proposed a novel and light weighted deep learning architecture with weight-based loss based on a convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM) that can automatically identify five types of ECG heartbeats according to the AAMI EC57 standard. It was also true that the raw ECG signals were simply segmented without noise removal and other feature extraction processing. Moreover, to tackle the challenge of classification bias due to imbalanced ECG datasets for different types of arrhythmias, we introduced a weight-based loss function to reduce the influence of over-weighted categories in the ECG dataset. For avoiding the influence of the division of validation dataset, k-fold method was adopted to improve the reliability of the model.Results: The proposed algorithm is trained and tested on MIT-BIH Arrhythmia Database, and achieves an average of 99.33% accuracy, 93.67% sensitivity, 99.18% specificity, 89.85% positive prediction, and 91.65% F1 score.

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Automated Deep Learning Based Cardiovascular Disease Diagnosis Using ECG Signals

Cited by 13 publications

References 25 publications

Mud Ring Optimization Algorithm with Deep Learning Model for Disease Diagnosis on ECG Monitoring System

Mud Ring Optimization Algorithm with Deep Learning Model for Disease Diagnosis on ECG Monitoring System

Variational autoencoder–based neural electrocardiogram synthesis trained by FEM-based heart simulator

A robust multiple heartbeats classification with weight-based loss based on convolutional neural network and bidirectional long short-term memory

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