HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN

Islam, Md Shofiqul; Hasan, Khondokar Fida; Sultana, Sunjida; Uddin, Shahadat; Lió, Píetro; Quinn, Julian M.W.; Moni, Mohammad Ali

doi:10.1016/j.neunet.2023.03.004

Cited by 26 publications

(7 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… 38 Another study by Islam et al introduced the same model architecture without employing the attention mechanism, achieving an accuracy of 99.99%. 39 Besides, Khan et al utilized CNNs to classify five types of arrhythmias. To address the data imbalance, they employed the SMOTE–Tomek technique and achieved 92.86% accuracy.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Attention-assisted hybrid CNN-BILSTM-BiGRU model with SMOTE–Tomek method to detect cardiac arrhythmia based on 12-lead electrocardiogram signals

Chopannejad,

Roshanpoor,

Sadoughi

2024

DIGITAL HEALTH

View full text Add to dashboard Cite

Objectives Cardiac arrhythmia is one of the most severe cardiovascular diseases that can be fatal. Therefore, its early detection is critical. However, detecting types of arrhythmia by physicians based on visual identification is time-consuming and subjective. Deep learning can develop effective approaches to classify arrhythmias accurately and quickly. This study proposed a deep learning approach developed based on a Chapman–Shaoxing electrocardiogram (ECG) dataset signal to detect seven types of arrhythmias. Method Our DNN model is a hybrid CNN-BILSTM-BiGRU algorithm assisted by a multi-head self-attention mechanism regarding the challenging problem of classifying various arrhythmias of ECG signals. Additionally, the synthetic minority oversampling technique (SMOTE)–Tomek technique was utilized to address the data imbalance problem to detect and classify cardiac arrhythmias. Result The proposed model, trained with a single lead, was tested using a dataset containing 10,466 participants. The performance of the algorithm was evaluated using a random split validation approach. The proposed algorithm achieved an accuracy of 98.57% by lead II and 98.34% by lead aVF for the classification of arrhythmias. Conclusion We conducted an analysis of single-lead ECG signals to evaluate the effectiveness of our proposed hybrid model in diagnosing and classifying different types of arrhythmias. We trained separate classification models using each individual signal lead. Additionally, we implemented the SMOTE–Tomek technique along with cross-entropy loss as a cost function to address the class imbalance problem. Furthermore, we utilized a multi-headed self-attention mechanism to adjust the network structure and classify the seven arrhythmia classes. Our model achieved high accuracy and demonstrated good generalization ability in detecting ECG arrhythmias. However, further testing of the model with diverse datasets is crucial to validate its performance.

show abstract

Section: Discussionmentioning

confidence: 99%

“…The method achieved an accuracy of 99.60% during training, 99.40% during validation and 99.01% during testing. 39 …”

Section: Introductionmentioning

confidence: 99%

Attention-assisted hybrid CNN-BILSTM-BiGRU model with SMOTE–Tomek method to detect cardiac arrhythmia based on 12-lead electrocardiogram signals

Chopannejad,

Roshanpoor,

Sadoughi

2024

DIGITAL HEALTH

View full text Add to dashboard Cite

show abstract

“…The results of comparing our proposed method with the aforementioned approaches 17,[19][20][21][22][23][24] on the MIT-BIH dataset are presented in Table 3, and for the PhysioNet 2017 dataset, the results are shown in Table 4. It is evident that the proposed method outperforms other methods, whether on the MIT-BIH dataset or PhysioNet 2017 dataset.…”

Section: Discussionmentioning

confidence: 99%

ECG autoencoder based on low-rank attention

Zhang,

Fang,

Ren

2024

Sci Rep

View full text Add to dashboard Cite

The prevalence of cardiovascular disease (CVD) has surged in recent years, making it the foremost cause of mortality among humans. The Electrocardiogram (ECG), being one of the pivotal diagnostic tools for cardiovascular diseases, is increasingly gaining prominence in the field of machine learning. However, prevailing neural network models frequently disregard the spatial dimension features inherent in ECG signals. In this paper, we propose an ECG autoencoder network architecture incorporating low-rank attention (LRA-autoencoder). It is designed to capture potential spatial features of ECG signals by interpreting the signals from a spatial perspective and extracting correlations between different signal points. Additionally, the low-rank attention block (LRA-block) obtains spatial features of electrocardiogram signals through singular value decomposition, and then assigns these spatial features as weights to the electrocardiogram signals, thereby enhancing the differentiation of features among different categories. Finally, we utilize the ResNet-18 network classifier to assess the performance of the LRA-autoencoder on both the MIT-BIH Arrhythmia and PhysioNet Challenge 2017 datasets. The experimental results reveal that the proposed method demonstrates superior classification performance. The mean accuracy on the MIT-BIH Arrhythmia dataset is as high as 0.997, and the mean accuracy and $$F_1$$ F 1 -score on the PhysioNet Challenge 2017 dataset are 0.850 and 0.843.

show abstract

“…To further evaluate the effectiveness of ISR-LSTM network, we empirically and specificity (SPF) for performance assessment [39,40]. From Table 2 and Table 3, evaluation indicators of ISR-LSTM-ResNet50 are 0.2 to 1.9 percentage points better than that of LSTM-ResNet50 and ResNet50…”

Section: Architecture Detailsmentioning

confidence: 99%

An improved SR-LSTM network for atrial fibrillation and atrial flutter signals discrimination

Wang,

Guo

2024

Preprint

View full text Add to dashboard Cite

Atrial fibrillation (AF) and atrial flutter (AFL) are the most common and easily confused arrhythmias. However, the existing AF and AFL detection approaches depend on visual examination of electrocardiogram (ECG), which is taxing and time-consuming. Motivated by state refinement module for LSTM (SR-LSTM), we investigate an improved SR-LSTM (ISR-LSTM) that is capable of adaptively refining the current states of sample points in ECG leveraging a sample point-wise attention and a representation gate mechanism for automated AF and AFL signals detection. To confirm the effectiveness, we conducted ablation experiments across two public databases. The results demonstrate the effectiveness of ISR-LSTM while achieving a significant improvement than state-of-the-art algorithms with an accuracy of 99.2% and 98.5%. To our knowledge, ISR-LSTM is the first work that integrates the interaction in ECG into deep network frame providing interpretability.

show abstract

HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN

Cited by 26 publications

References 54 publications

Attention-assisted hybrid CNN-BILSTM-BiGRU model with SMOTE–Tomek method to detect cardiac arrhythmia based on 12-lead electrocardiogram signals

Attention-assisted hybrid CNN-BILSTM-BiGRU model with SMOTE–Tomek method to detect cardiac arrhythmia based on 12-lead electrocardiogram signals

ECG autoencoder based on low-rank attention

An improved SR-LSTM network for atrial fibrillation and atrial flutter signals discrimination

Contact Info

Product

Resources

About