Automated detection of coronary artery disease, myocardial infarction and congestive heart failure using GaborCNN model with ECG signals

Jahmunah, V.; Ng, E. Y. K.; San, Tan Ru; Acharya, U. Rajendra

doi:10.1016/j.compbiomed.2021.104457

Cited by 70 publications

(30 citation statements)

References 66 publications

(101 reference statements)

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“…Moreover, Jahmunah et al [ 192 ] applied a CNN architecture to ECG data from several public ECG databases to detect coronary artery disease, myocardial infarction, and congestive heart failure, achieving an accuracy of 99.55%. Another study by Dai et al [ 195 ] proposed a CNN for CVD diagnosis using different intervals of ECG signals from the PTB Diagnostic ECG Database and achieved accuracies of 99.59%, 99.80%, and 99.84% for 1-, 2-, and 3-second ECG segments, respectively.…”

Section: Resultsmentioning

confidence: 99%

State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review

Petmezas¹,

Stefanopoulos²,

Kilintzis³

et al. 2022

JMIR Med Inform

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Background Electrocardiogram (ECG) is one of the most common noninvasive diagnostic tools that can provide useful information regarding a patient’s health status. Deep learning (DL) is an area of intense exploration that leads the way in most attempts to create powerful diagnostic models based on physiological signals. Objective This study aimed to provide a systematic review of DL methods applied to ECG data for various clinical applications. Methods The PubMed search engine was systematically searched by combining “deep learning” and keywords such as “ecg,” “ekg,” “electrocardiogram,” “electrocardiography,” and “electrocardiology.” Irrelevant articles were excluded from the study after screening titles and abstracts, and the remaining articles were further reviewed. The reasons for article exclusion were manuscripts written in any language other than English, absence of ECG data or DL methods involved in the study, and absence of a quantitative evaluation of the proposed approaches. Results We identified 230 relevant articles published between January 2020 and December 2021 and grouped them into 6 distinct medical applications, namely, blood pressure estimation, cardiovascular disease diagnosis, ECG analysis, biometric recognition, sleep analysis, and other clinical analyses. We provide a complete account of the state-of-the-art DL strategies per the field of application, as well as major ECG data sources. We also present open research problems, such as the lack of attempts to address the issue of blood pressure variability in training data sets, and point out potential gaps in the design and implementation of DL models. Conclusions We expect that this review will provide insights into state-of-the-art DL methods applied to ECG data and point to future directions for research on DL to create robust models that can assist medical experts in clinical decision-making.

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Section: Resultsmentioning

confidence: 99%

State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review

Petmezas¹,

Stefanopoulos²,

Kilintzis³

et al. 2022

JMIR Med Inform

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show abstract

“…Deep learning models are generally preferred for classification since it has advantages over the other methods. 28 Convolutional neural networks (CNN) is the development of artificial neural networks (ANN). The network, which gets deeper as a result of the further increase in the number of hidden layers in ANN, can be defined as a convolutional neural network.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Automated gender‐Parkinson's disease detection at the same time via a hybrid deep model using human voice

Kaya

2022

Concurrency and Computation

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Summary Gender and Parkinson disease (PD) identifications are critical parts to be noted from a given in human voice. Numerous artificial intelligence based methods have been proposed to detect gender and PD easily in literature. It is purposed to build an effective and a dependable simultaneously gender and PD recognition system based on feature extraction and feature selection methods in this study. First, CNN structure is used for obtaining deeper features from TQWT applied data and acoustic deep parameters are obtained by it. Later, these deep features are subjected to mRMR feature selection algorithm that increase the performance efficiency of the classifiers. As a result, the crucial features obtained by this hybrid structure and significant success rate 98.9% is obtained with the k‐NN classifier. Thus, gender and PD are detected at the same time. Also, this work is multiclass problem so, the other success parameters are calculated separately.

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“…Lih et al [26] coupled CNNs with long short-term memory (LSTM) to keep into account the temporal information naturally encoded into the ECG, obtaining an accuracy of 0.98. A similar approach was used in [27], which further included an inception module in the CNN to allow multiscale analysis, thus achieving an accuracy of 0.99.…”

Section: Deep Learning In Hf Diagnosismentioning

confidence: 99%

Decision Support Systems in HF based on Deep Learning Technologies

Penso

Solbiati

Moccia

et al. 2022

Curr Heart Fail Rep

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Purpose of Review Application of deep learning (DL) is growing in the last years, especially in the healthcare domain. This review presents the current state of DL techniques applied to electronic health record structured data, physiological signals, and imaging modalities for the management of heart failure (HF), focusing in particular on diagnosis, prognosis, and re-hospitalization risk, to explore the level of maturity of DL in this field. Recent Findings DL allows a better integration of different data sources to distillate more accurate outcomes in HF patients, thus resulting in better performance when compared to conventional evaluation methods. While applications in image and signal processing for HF diagnosis have reached very high performance, the application of DL to electronic health records and its multisource data for prediction could still be improved, despite the already promising results. Summary Embracing the current big data era, DL can improve performance compared to conventional techniques and machine learning approaches. DL algorithms have potential to provide more efficient care and improve outcomes of HF patients, although further investigations are needed to overcome current limitations, including results generalizability and transparency and explicability of the evidences supporting the process.

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Automated detection of coronary artery disease, myocardial infarction and congestive heart failure using GaborCNN model with ECG signals

Cited by 70 publications

References 66 publications

State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review

State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review

Automated gender‐Parkinson's disease detection at the same time via a hybrid deep model using human voice

Decision Support Systems in HF based on Deep Learning Technologies

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