Identification of Arrhythmia by Using a Decision Tree and Gated Network Fusion Model

Lu, Peng; Zhang, Yabin; Zhang, Hongpo; Chen, Li-Wei; Lin, Yusong; Mao, Xiaobo; Gao, Yang; Xi, Hao

doi:10.1155/2021/6665357

Cited by 9 publications

(3 citation statements)

References 43 publications

(36 reference statements)

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“…Lu P et al [29] used a combination of gate recurrent unit (GRU) and decision tree (DT) which is called T-GRU. The T-GRU model proposed by the authors was used for the detection of arrhythmia.…”

Section: K-nearest Neighbours (K-nn)mentioning

confidence: 99%

Machine Learning for Cardiac Arrhythmia Detection: A Systematic Survey

Singh,

Agarwal,

Kaur

et al. 2023

J. Phys.: Conf. Ser.

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A significant global worry is a substantial rise in cardiac arrhythmia cases brought on by improper food and lifestyle choices. Manual analysis of the report of the electrocardiogram to detect the presence of an anomaly is a time-consuming task. Hence, it is necessary to create an automated diagnosis system that can deliver results quickly and accurately. Numerous machine learning-based models were created by researchers working in this field to determine the severity of cardiac arrhythmias. This article provides an organized and thorough assessment of previous research in the field, with a particular emphasis on machine learning methods developed by different authors to detect cardiac arrhythmia. Additionally, covered is the performance analysis of the different algorithms. The difficulties associated with developing a model for cardiac arrhythmia and its potential future impact are finally examined in the conclusion section.

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Section: K-nearest Neighbours (K-nn)mentioning

confidence: 99%

Machine Learning for Cardiac Arrhythmia Detection: A Systematic Survey

Singh,

Agarwal,

Kaur

et al. 2023

J. Phys.: Conf. Ser.

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“…They can hence learn complex temporal dynamics within time-varying data. In this way, Lu et al [8] proposed a gated recurrent unit (GRU) and decision tree fusion model to explore the problem of arrhythmia recognition and to improve the credibility of deep learning methods. Luo et al [9] proposed an arrhythmia classification method based on a hybrid convolutional recurrent neural network (CRNN) for the time-series signal of ECG.…”

Section: Introductionmentioning

confidence: 99%

Multi-Class Arrhythmia Classification and R-Peak Detection Method of ECG Signal Based on One-Dimensional U-Net with Skip-Connection and Data Augmentation

Liu,

Xie,

Yan

et al. 2023

Fuzzy Systems and Data Mining IX

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Automatic arrhythmia analysis techniques and QRS detection provide convenience for the prevention and diagnosis of cardiac disease. The existing studies generally study arrhythmia classification and QRS recognition separately, which requires two different models and may result in time and resource wasting. To realize the goal of arrhythmia classification and R-peak detection of electrocardiograms at the same time, we proposed a method for multi-class arrhythmia classification and R-peak detection method based on one-dimensional U-net with skip-connection and data augmentation. First, the ECG signals were preprocessed by filtering and segmentation, and the ECG annotations were also processed into pixel labels of equal length. Then, we applied data augmentation techniques such as changing gain, adding noise, and flipping signals up and down to increase the diversity of the data. Finally, a modified one-dimensional U-net with skip-connection layers was built to adaptively extract deep features and to detect the arrhythmia type and R peaks of ECG at the same time. We set up an 8-category experiment using five publicly available datasets, and the experimental results show that the macro average F1 value is 94.57%, which is over 4.3% and 3% higher than that of original U-net and the skip-connection U-net without data augmentation, respectively. Meanwhile, the F1 of R-peak detection is 99.64%.

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“…Bhattacharyya et al [22] is proposed an arrhythmic heartbeat classification technique showing that the random forest (RF) algorithm is complex and expensive to handle overfitting. The decision tree (DT) technique can easily handle multiple data types [23], but it requires more impactful features, and generalization is not possible by this method [24]. The Naive Bayes (NB) [25] algorithm works well for large datasets but incorrectly predicts equally important and independent features.…”

Section: Introductionmentioning

confidence: 99%

New Hybrid Deep Learning Approach Using BiGRU-BiLSTM and Multilayered Dilated CNN to Detect Arrhythmia

Islam

Hashim

et al. 2022

IEEE Access

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Deep learning methods have shown early progress in analyzing complicated ECG signals, especially in heartbeat classification and arrhythmia detection. There is still a great deal of space for further research on this area before reaching a definite decision. This study introduced a novel hybrid framework based on a bidirectional recurrent neural network (BiRNN) with a multilayered dilated convolution neural network (CNN) for arrhythmia classification. Initially, the raw ECG signals are filtered using Chebyshev Type II method and the Daubechies wavelet method is used to solve fractal problems and signal discontinuities. Then, a synthetic signal is generated using a generative adversarial network (GAN) to handle imbalanced signal classes. The proposed Bidirectional RNN with Dilated CNN (BRDC) architecture takes advantage of multilayered dilated CNN and bidirectional RNN units (bidirectional gated recurrent Units, BiGRU, bidirectional long short-term memory, BiLSTM) to generate fusion features and then, fusion features are classified in the fully connected layer. The PhysioNet 2017 challenge (MIT-BIH) dataset is used to train and validate the proposed approach. By combining fusion features with dilated CNN, the proposed approach outperforms the existing model for arrhythmia detection with 99.90 % accuracy, 98.41 % F1-score, 97.96 % precision, and 99.90 % recall. Overall, our hybrid BRDC model provides a cost-effective ECG signal reduction and high-performance automated recognition technique to identify arrhythmia. In the future, an automatic and cloud-based system with more arrhythmia data variance to test the model's robustness will be given the highest priority.

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Identification of Arrhythmia by Using a Decision Tree and Gated Network Fusion Model

Cited by 9 publications

References 43 publications

Machine Learning for Cardiac Arrhythmia Detection: A Systematic Survey

Machine Learning for Cardiac Arrhythmia Detection: A Systematic Survey

Multi-Class Arrhythmia Classification and R-Peak Detection Method of ECG Signal Based on One-Dimensional U-Net with Skip-Connection and Data Augmentation

New Hybrid Deep Learning Approach Using BiGRU-BiLSTM and Multilayered Dilated CNN to Detect Arrhythmia

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