A Novel Deep Learning based Gated Recurrent Unit with Extreme Learning Machine for Electrocardiogram (ECG) Signal Recognition

Virgeniya, S. Clement; Ramaraj, E.

doi:10.1016/j.bspc.2021.102779

Cited by 30 publications

(11 citation statements)

References 18 publications

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“…Moreover, the developed system was found effective for analyzing scanned results, as reported by the participating doctors and cardiologists. In another work, ELM, a neural network model, was applied by Virgeniya and Ramaraj [99] for ECG signal recognition. Although the study proposed a deep learning model to handle class imbalance during feature extraction, the actual classification/interpretation was performed with ELM.…”

Section: Neural-based Methodsmentioning

confidence: 99%

“…They used the annotation file for segmenting the ECG signal to its beats. Other hybrid-based feature extraction approaches which considered fiducial and contextual-based features, together with kernel-based feature extraction using CNN, for developing deep learning models were also presented in [98][99][100][101][102][103][104][105][106][107][108][109]. These studies leveraged the automatic feature extraction capability of deep learning methods to develop robust ES and DSS solutions for ECG interpretation and diagnosis.…”

Section: Hybrid Features Extraction Approachesmentioning

confidence: 99%

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Expert System and Decision Support System for Electrocardiogram Interpretation and Diagnosis: Review, Challenges and Research Directions

et al. 2022

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Electrocardiography (ECG) is one of the most widely used recordings in clinical medicine. ECG deals with the recording of electrical activity that is generated by the heart through the surface of the body. The electrical activity generated by the heart is measured using electrodes that are attached to the body surface. The use of ECG in the diagnosis and management of cardiovascular disease (CVD) has been in existence for over a decade, and research in this domain has recently attracted large attention. Along this line, an expert system (ES) and decision support system (DSS) have been developed for ECG interpretation and diagnosis. However, despite the availability of a lot of literature, access to recent and more comprehensive review papers on this subject is still a challenge. This paper presents a comprehensive review of the application of ES and DSS for ECG interpretation and diagnosis. Researchers have proposed a number of features and methods for ES and DSS development that can be used to monitor a patient’s health condition through ECG recordings. In this paper, a taxonomy of the features and methods for ECG interpretation and diagnosis were presented. The significance of the features and methods, as well as their limitations, were analyzed. This review further presents interesting theoretical concepts in this domain, as well as identifies challenges and open research issues on ES and DSS development for ECG interpretation and diagnosis that require substantial research effort. In conclusion, this paper identifies important future research areas with the purpose of advancing the development of ES and DSS for ECG interpretation and diagnosis.

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Section: Neural-based Methodsmentioning

confidence: 99%

Section: Hybrid Features Extraction Approachesmentioning

confidence: 99%

Expert System and Decision Support System for Electrocardiogram Interpretation and Diagnosis: Review, Challenges and Research Directions

et al. 2022

View full text Add to dashboard Cite

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“…There are various artificial intelligence (AI) approaches that are employed for the detection of MI based on the analysis of ECG signals [5,6,19]. These approaches are categorized into two common approaches, namely, machine learning [7,20,21] and deep learning approaches [17,18,22]. Deep learning methods are thought to be more reliable than typical machine learning methods, especially when dealing with large amounts of data.…”

Section: Related Work and Motivationmentioning

confidence: 99%

“…In this study, we employed the PTB-XL electrocardiography dataset [26], which is an updated version of the PTB database and the most common dataset used for MI detection in the literature [13,[15][16][17][18]22]. This dataset consists of 21,837 records with a length of 10 s collected from 18,885 mixed individuals (men and women).…”

Section: Ecg Dataset Descriptionmentioning

confidence: 99%

Automated Detection of Myocardial Infarction and Heart Conduction Disorders Based on Feature Selection and a Deep Learning Model

Hammad

Chelloug

Alkanhel

et al. 2022

Sensors

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An electrocardiogram (ECG) is an essential piece of medical equipment that helps diagnose various heart-related conditions in patients. An automated diagnostic tool is required to detect significant episodes in long-term ECG records. It is a very challenging task for cardiologists to analyze long-term ECG records in a short time. Therefore, a computer-based diagnosis tool is required to identify crucial episodes. Myocardial infarction (MI) and conduction disorders (CDs), sometimes known as heart blocks, are medical diseases that occur when a coronary artery becomes fully or suddenly stopped or when blood flow in these arteries slows dramatically. As a result, several researchers have utilized deep learning methods for MI and CD detection. However, there are one or more of the following challenges when using deep learning algorithms: (i) struggles with real-life data, (ii) the time after the training phase also requires high processing power, (iii) they are very computationally expensive, requiring large amounts of memory and computational resources, and it is not easy to transfer them to other problems, (iv) they are hard to describe and are not completely understood (black box), and (v) most of the literature is based on the MIT-BIH or PTB databases, which do not cover most of the crucial arrhythmias. This paper proposes a new deep learning approach based on machine learning for detecting MI and CDs using large PTB-XL ECG data. First, all challenging issues of these heart signals have been considered, as the signal data are from different datasets and the data are filtered. After that, the MI and CD signals are fed to the deep learning model to extract the deep features. In addition, a new custom activation function is proposed, which has fast convergence to the regular activation functions. Later, these features are fed to an external classifier, such as a support vector machine (SVM), for detection. The efficiency of the proposed method is demonstrated by the experimental findings, which show that it improves satisfactorily with an overall accuracy of 99.20% when using a CNN for extracting the features with an SVM classifier.

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“…First, some manipulations can be applied on initial data, such as random scaling, flipping, shifting, and noising ECG, to achieve accurate detection of multiple arrhythmias ( Vicar et al, 2020 ; Nonaka and Seita, 2021 ; Do et al, 2022 ). The same application can profit from using the synthetic samples generated from the training ones using intuitive adaptive synthetic data sampling (ADASYN, Virgeniya and Ramaraj, 2021 ) or synthetic minority oversampling technique (SMOTE, Ketu and Mishra, 2021 ). Data samples can be generated artificially by specially trained ML or DL models (such as Gaussian mixture model (GMM), generative adversarial network (GAN), LSTM/biLSTM, CNN), as has been shown for time-series ECG (including dependent multichannel signals) and 2D spectrogram applications (e.g., Lima et al, 2019 ; Brophy, 2020 ; Hatamian et al, 2020 ; Hazra and Byun, 2020 ).…”

Section: Ecg Analysismentioning

confidence: 99%

Golden Standard or Obsolete Method? Review of ECG Applications in Clinical and Experimental Context

et al. 2022

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Cardiovascular system and its functions under both physiological and pathophysiological conditions have been studied for centuries. One of the most important steps in the cardiovascular research was the possibility to record cardiac electrical activity. Since then, numerous modifications and improvements have been introduced; however, an electrocardiogram still represents a golden standard in this field. This paper overviews possibilities of ECG recordings in research and clinical practice, deals with advantages and disadvantages of various approaches, and summarizes possibilities of advanced data analysis. Special emphasis is given to state-of-the-art deep learning techniques intensely expanded in a wide range of clinical applications and offering promising prospects in experimental branches. Since, according to the World Health Organization, cardiovascular diseases are the main cause of death worldwide, studying electrical activity of the heart is still of high importance for both experimental and clinical cardiology.

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A Novel Deep Learning based Gated Recurrent Unit with Extreme Learning Machine for Electrocardiogram (ECG) Signal Recognition

Cited by 30 publications

References 18 publications

Expert System and Decision Support System for Electrocardiogram Interpretation and Diagnosis: Review, Challenges and Research Directions

Expert System and Decision Support System for Electrocardiogram Interpretation and Diagnosis: Review, Challenges and Research Directions

Automated Detection of Myocardial Infarction and Heart Conduction Disorders Based on Feature Selection and a Deep Learning Model

Golden Standard or Obsolete Method? Review of ECG Applications in Clinical and Experimental Context

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