A Hybrid CNN-LSTM Architecture for Detection of Coronary Artery Disease from ECG

Banerjee, Rohan; Ghose, Avik; Mandana, Kayapanda

doi:10.1109/ijcnn48605.2020.9207044

Cited by 21 publications

(22 citation statements)

References 12 publications

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“…As shown in Table 5, the accuracy of our hybrid model is higher than the accuracy achieved from CNN model [14,64], the LSTM model [56], and the CNN and LSTM models used in this work. Also, the accuracy of our hybrid model is higher than that achieved from hybrid CNN-LSTM [52,54,72] and is comparable to that achieved by Oh et al [71].…”

Section: The Results From the Cnn Modelsupporting

confidence: 79%

“…Most of the models published in literature don't focus on the number of model parameters although it is an important measurement to check whether the model is suitable for the embedded system or not. Comparing our hybrid model with other similar hybrid models in recent works [52,54,71,72], our model is capable to successfully do classification for six ECG beats classes with comparable accuracy and least number of parameters. Furthermore, our model can do automated feature extraction in faster manner compared with the slow manual feature extraction presented by Banerjee et al [52].…”

Section: The Results From the Cnn Modelmentioning

confidence: 84%

“…Comparing our hybrid model with other similar hybrid models in recent works [52,54,71,72], our model is capable to successfully do classification for six ECG beats classes with comparable accuracy and least number of parameters. Furthermore, our model can do automated feature extraction in faster manner compared with the slow manual feature extraction presented by Banerjee et al [52].…”

Section: The Results From the Cnn Modelmentioning

confidence: 84%

“…SeismoNet is trained in an end-to-end manner to detect position of the R-peaks in spite of noise. A novel neural network architecture combines anomalous morphology of Electrocardiogram (ECG) waveform with abnormal Heart Rate Variability (HRV) has been proposed by Banerjee et al [52]. They have used A Convolutional Neural Network (CNN) structure to extract the morphological ECG features.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Lightweight 1D CNN-LSTM Architecture for Automated ECG Beat-Wise Classification

Obeidat¹,

Alqudah²

2021

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In this paper we have utilized a hybrid lightweight 1D deep learning model that combines convolutional neural network (CNN) and long short-term memory (LSTM) methods for accurate, fast, and automated beat-wise ECG classification. The CNN and LSTM models were designed separately to compare with the hybrid CNN-LSTM model in terms of accuracy, number of parameters, and the time required for classification. The hybrid CNN-LSTM system provides an automated deep feature extraction and classification for six ECG beats classes including Normal Sinus Rhythm (NSR), atrial fibrillation (AFIB), atrial flutter (AFL), atrial premature beat (APB), left bundle branch block (LBBB), and right bundle branch block (RBBB). The hybrid model uses the CNN blocks for deep feature extraction and selection from the ECG beat. While the LSTM layer will learn how to extract contextual time information. The results show that the proposed hybrid CNN-LSTM model achieves high accuracy and sensitivity of 98.22% and 98.23% respectively. This model is light and fast in classifying ECG beats and superior to other previously used models which makes it very suitable for embedded systems designs that can be used in clinical applications for monitoring heart diseases in faster and more efficient manner.

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Section: The Results From the Cnn Modelsupporting

confidence: 79%

Section: The Results From the Cnn Modelmentioning

confidence: 84%

Section: The Results From the Cnn Modelmentioning

confidence: 84%

Section: Introductionmentioning

confidence: 99%

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A Hybrid Lightweight 1D CNN-LSTM Architecture for Automated ECG Beat-Wise Classification

Obeidat¹,

Alqudah²

2021

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“…We excluded patients who used drug-coated balloons to treat the stenosis to increase the uniformity of the case group. We also defined non-CAD patients as having no prior cardiac history at the time of data collection, similar to previous literature [ 13 ]. All patients were followed by their physicians in cardiology out-patient clinics.…”

Section: Methodsmentioning

confidence: 99%

An Artificial Intelligence-Enabled ECG Algorithm for the Prediction and Localization of Angiography-Proven Coronary Artery Disease

et al. 2022

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(1) Background: The role of using artificial intelligence (AI) with electrocardiograms (ECGs) for the diagnosis of significant coronary artery disease (CAD) is unknown. We first tested the hypothesis that using AI to read ECG could identify significant CAD and determine which vessel was obstructed. (2) Methods: We collected ECG data from a multi-center retrospective cohort with patients of significant CAD documented by invasive coronary angiography in Taiwan from 1 January 2018 to 31 December 2020. (3) Results: We trained convolutional neural networks (CNN) models to identify patients with significant CAD (>70% stenosis), using the 12,954 ECG from 2303 patients with CAD and 2090 ECG from 1053 patients without CAD. The Marco-average area under the ROC curve (AUC) for detecting CAD was 0.869 for image input CNN model. For detecting individual coronary artery obstruction, the AUC was 0.885 for left anterior descending artery, 0.776 for right coronary artery, and 0.816 for left circumflex artery obstruction, and 1.0 for no coronary artery obstruction. Marco-average AUC increased up to 0.973 if ECG had features of myocardial ischemia. (4) Conclusions: We for the first time show that using the AI-enhanced CNN model to read standard 12-lead ECG permits ECG to serve as a powerful screening tool to identify significant CAD and localize the coronary obstruction. It could be easily implemented in health check-ups with asymptomatic patients and identifying high-risk patients for future coronary events.

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A novel intelligent deep learning predictive model for meteorological drought forecasting

Mehr

Ghiasi

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

et al. 2022

J Ambient Intell Human Comput

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A Hybrid CNN-LSTM Architecture for Detection of Coronary Artery Disease from ECG

Cited by 21 publications

References 12 publications

A Hybrid Lightweight 1D CNN-LSTM Architecture for Automated ECG Beat-Wise Classification

A Hybrid Lightweight 1D CNN-LSTM Architecture for Automated ECG Beat-Wise Classification

An Artificial Intelligence-Enabled ECG Algorithm for the Prediction and Localization of Angiography-Proven Coronary Artery Disease

A novel intelligent deep learning predictive model for meteorological drought forecasting

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