Electrocardiogram based arrhythmia classification using wavelet transform with deep learning model

Mohonta, Shadhon Chandra; Motin, Mohammod Abdul; Kumar, Dinesh

doi:10.1016/j.sbsr.2022.100502

Cited by 37 publications

(11 citation statements)

References 29 publications

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“…Still on CNN application for ECG interpretation, Mohonta et al [106] utilized CNN with fully connected layers on ECG features extracted using CWT to overcome the problem of long segment-based ECG detection method. The use of smaller segments makes the model computationally simpler and faster without compromising the model performance.…”

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

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“…The wavelet technique is essential in clinical studies to detect the different signals that make up a raw one; the wavelet transformation is a recent technique in non-invasive electrocardiology. Details of the ECG signal are highlighted in time and frequency resolution using wavelet transformation [16]. During our study, we have opted for the wavelet technique to extract up to the minute information of the abdominal signal and knowing that this algorithm remains complex.…”

Section: Proposed Approachmentioning

confidence: 99%

“…The LMS algorithm, as well as others related to it, is widely used in various applications of adaptive filtering due to its computational simplicity. The convergence characteristics of the LMS algorithm are studied in order to establish a range for the convergence factor that will guarantee stability; (difference between the desired and the actual signal), as shown from (13), up to (16).…”

Section: Proposed Approachmentioning

confidence: 99%

Beamforming-based algorithms for recovering information from fetal electrocardiographic sensors

et al. 2023

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We deal with the extraction of the fetal electrocardiography (ECG) signal from the raw ECG signals of the mother by the beamforming- based algorithms. The foetal ECG sensors bring out signals containing information from the pregnant mother and the infant. Detailed and separate signals are already provided by the foetal ECG instruments; but for some specific studies related to the infant conditions, it is necessary to improve the quality of the signal with a dedicated processing. In this paper, four techniques, with some enhancements, are proposed to perform the processing; we have applied the following techniques: Least Mean Square (LMS) with adaptive noise cancellation technique, Discrete Wavelet Transform (DWT)-based technique, Empirical Wavelet Transform (EWT) technique, and Multiple Signal Classification (MUSIC). The LMS and the MUSIC pertain to beamforming approach. The techniques were used to decompose and identify the different elements constituting the source signal (mother's signal) and noise cancellation by Multivariate Empirical Mode Decomposition (MEMD) technique. The signal was adaptively decomposed by LMS, DWT and MUSIC according to optimised parameters to extract some hidden components of the source signal, such as the foetal features, QRS, heartbeat etc. The results have showed that LMS, with enhancements, is more effective in identifying and removing useless noise. The techniques were applied to the ECG signal of a 30-year-old healthy pregnant woman, which allowed to verify their applicability. The present research leads to the below main contributions among others: separation of the ECG signal of the foetus from the mother, highlighting the functional state of the foetal heart rhythm (heart rate and heartbeat,) and this can show us if the foetal ECG has malfunctions.

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“…A deep-learning strategy has been presented by Shadhon Chandra Mohonta et al [220] in which the network classifies the scalogram image obtained by CWT based on the signature associated to arrhythmia. The 2D CNN is trained for automated arrhythmia identification using the recordings of CWT.…”

Section: Learning On Reconstructed Ecg Signalmentioning

confidence: 99%

Review on Compressive Sensing Algorithms for ECG Signal for IoT Based Deep Learning Framework

Kumar

Ramachandran

2022

Applied Sciences

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Nowadays, healthcare is becoming very modern, and the support of Internet of Things (IoT) is inevitable in a personal healthcare system. A typical personal healthcare system acquires vital parameters from human users and stores them in a cloud platform for further analysis. Acquiring fundamental biomedical signal, such as with the Electrocardiograph (ECG), is also considered for specific disease analysis in personal healthcare systems. When such systems are scaled up, there is a heavy demand for internet channel capacity to accommodate real time seamless flow of discrete samples of biomedical signals. So, there is a keen need for real time data compression of biomedical signals. Compressive Sensing (CS) has recently attracted more interest due to its compactness and its feature of the faithful reconstruction of signals from fewer linear measurements, which facilitates less than Shannon’s sampling rate by exploiting the signal sparsity. The most common biomedical signal that is to be analyzed is the ECG signal, as the prediction of heart failure at an early stage can save a human life. This review is for a vast use-case of IoT framework in which CS measurements of ECG are acquired, communicated through Internet to a server, and the arrhythmia are analyzed using Machine learning (ML). Assuming this use-case specific for ECG, in this review many technical aspects are considered regarding various research components. The key aspect is on the investigation of the best sensing method, and to address this, various sensing matrices are reviewed, analyzed and recommended. The next aspect is the selection of the optimal sparsifying method, and the review recommends unexplored ECG compression algorithms as sparsifying methods. The other aspects are optimum reconstruction algorithms, best hardware implementations, suitable ML methods and effective modality of IoT. In this review all these components are considered, and a detailed review is presented which enables us to orchestrate the use-case specified above. This review focuses on the current trends in CS algorithms for ECG signal compression and its hardware implementation. The key to successful reconstruction of the CS method is the right selection of sensing and sparsifying matrix, and there are many unexplored sparsifying methods for the ECG signal. In this review, we shed some light on new possible sparsifying techniques. A detailed comparison table of various CS algorithms, sensing matrix, sparsifying techniques with different ECG dataset is tabulated to quantify the capability of CS in terms of appropriate performance metrics. As per the use-case specified above, the CS reconstructed ECG signals are to be subjected to ML analysis, and in this review the compressive domain inference approach is discussed. The various datasets, methodologies and ML models for ECG applications are studied and their model accuracies are tabulated. Mostly, the previous research on CS had studied the performance of CS using numerical simulation, whereas there are some good attempts for hardware implementations for ECG applications, and we studied the uniqueness of each method and supported the study with a comparison table. As a consolidation, we recommend new possibilities of the research components in terms of new transforms, new sparsifying methods, suggestions for ML approaches and hardware implementation.

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Electrocardiogram based arrhythmia classification using wavelet transform with deep learning model

Cited by 37 publications

References 29 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

Beamforming-based algorithms for recovering information from fetal electrocardiographic sensors

Review on Compressive Sensing Algorithms for ECG Signal for IoT Based Deep Learning Framework

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