Electrocardiogram classification of lead convolutional neural network based on fuzzy algorithm

Yang, Xi; Hu, Qiping; Li, Shuaihao

doi:10.3233/jifs-179576

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…Yang et al, in [ 88 ], presented an interesting fuzzy approach by using CNNs for the purpose of automated ECG signals’ analysis. The convolutional neural networks were used for the ECG classification without features extraction, which may result in information loss.…”

Section: Electrocardiographymentioning

confidence: 99%

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach—Part I: Cardiac Signals

Martínek

Ládrová

Šidiková

et al. 2021

Sensors

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Advanced signal processing methods are one of the fastest developing scientific and technical areas of biomedical engineering with increasing usage in current clinical practice. This paper presents an extensive literature review of the methods for the digital signal processing of cardiac bioelectrical signals that are commonly applied in today’s clinical practice. This work covers the definition of bioelectrical signals. It also covers to the extreme extent of classical and advanced approaches to the alleviation of noise contamination such as digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation and wavelet transform.

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Section: Electrocardiographymentioning

confidence: 99%

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach—Part I: Cardiac Signals

Martínek

Ládrová

Šidiková

et al. 2021

Sensors

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“…Al Rahhal et al proposed a dense convolutional network to detect arrhythmias and proposed focal loss to reduce the problems caused by data imbalance [17]. Yang et al proposed an ECG classification method based on a lead CNN, which used fuzzy sets to reduce the order of extracted ECG image features and optimized the network using the residual structure [18]. In addition, long short-term memory (LSTM) has been widely used in the classification of ECG signals owing to its excellent performance in processing time series data.…”

Section: Introductionmentioning

confidence: 99%

Automated Detection of Arrhythmia for Hybrid Neural Network of LSTM-Residual with Multi-Information Fusion

Tao

Liu

Liang

2021

Mathematical Problems in Engineering

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Arrhythmia is a common cardiovascular disease; the electrocardiogram (ECG) is widely used as an effective tool for detecting arrhythmia. However, real-time arrhythmia detection monitoring is difficult, so this study proposes a long short-term memory-residual model. Individual beats provide morphological features and combined with adjacent segments provide temporal features. Our proposed model captures the time-domain and morphological ECG signal information simultaneously and fuses the two information types. At the same time, the attention block is applied to the network to further strengthen the useful information, capture the hidden information in the ECG signal, and improve the model classification performance. Our model was finally trained and tested on the MIT-BIH arrhythmia database, and the entire dataset was divided into intrapatient and interpatient modes. Accuracies of 99.11% and 85.65%, respectively, were obtained under the two modes. Experimental results demonstrate that our proposed method is an efficient automated detection method.

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An Efficient Heartbeats Classifier Based on Optimizing Convolutional Neural Network Model

et al. 2021

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Electrocardiogram classification of lead convolutional neural network based on fuzzy algorithm

Cited by 5 publications

References 12 publications

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach—Part I: Cardiac Signals

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach—Part I: Cardiac Signals

Automated Detection of Arrhythmia for Hybrid Neural Network of LSTM-Residual with Multi-Information Fusion

An Efficient Heartbeats Classifier Based on Optimizing Convolutional Neural Network Model

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