An Automatic Diagnosis of Arrhythmias Using a Combination of CNN and LSTM Technology

Zhen-yu, Zheng; Chen, Zhencheng; Hu, Fangrong; Zhu, Jianming; Tang, Qunfeng; Liang, Yongbo

doi:10.3390/electronics9010121

Cited by 71 publications

(36 citation statements)

References 33 publications

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“…Wang in [12] developed a model based on 1D CNN and modified Elman neural network (MENN),which was consisted of the 11-layers neural network, and the non-transform ECG signal also fed the model. While some researchers proposed their CNN model for ECG analysis based on two-dimensional forms, for instance, Zheng at el in [13] converted the one dimensional ECG signal into two-dimensional images and then fed to the CNN model. Yildirim at el.…”

Section: Introductionmentioning

confidence: 99%

CNN-LSTM Based Model for ECG Arrhythmias and Myocardial Infarction Classification

Abdullah¹,

Al-Ani²

2020

Adv. sci. technol. eng. syst. j.

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ECG analysis is commonly used by medical practitioners and cardiologists for monitoring cardiac health. A high-performance automatic ECG classification system is a challenging area because there is difficulty in detecting and clustering various waveforms in the signal, especially in the manual analysis of electrocardiogram (ECG) signals. In this paper, an accurate (ECG) classification and monitoring system are proposed using the implementation of 1D Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTM). The learned features are captured from the CNN model, and then fed to the LSTM model. No handcraft features are required for the model for the ECG classification. The result of the CNN-LSTM model has demonstrated superior performance than several state-of-the-arts that cited in the result section. The proposed models are evaluated on MIT-BIH arrhythmia and PTB Diagnostics datasets. Based on the obtained results, the CNN-LSTM method can improve the accuracy rate, such that 98.1 % and 98.66 % on Myocardial Infarction (MI) and arrhythmia classification, respectively.

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Section: Introductionmentioning

confidence: 99%

CNN-LSTM Based Model for ECG Arrhythmias and Myocardial Infarction Classification

Abdullah¹,

Al-Ani²

2020

Adv. sci. technol. eng. syst. j.

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“…By combining both networks, a better performance is usually achieved in training a model to predict signals based on their spatial and time-domain characteristics (Zheng et al. 2020b ). Furthermore, the performance of the model was evaluated for the combined network as well as when operating individually as CNN or BDLSTM.…”

Section: Methodsmentioning

confidence: 99%

Recognition of pulmonary diseases from lung sounds using convolutional neural networks and long short-term memory

Fraiwan

Alkhodari

et al. 2021

J Ambient Intell Human Comput

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In this paper, a study is conducted to explore the ability of deep learning in recognizing pulmonary diseases from electronically recorded lung sounds. The selected data-set included a total of 103 patients obtained from locally recorded stethoscope lung sounds acquired at King Abdullah University Hospital, Jordan University of Science and Technology, Jordan. In addition, 110 patients data were added to the data-set from the Int. Conf. on Biomedical Health Informatics publicly available challenge database. Initially, all signals were checked to have a sampling frequency of 4 kHz and segmented into 5 s segments. Then, several preprocessing steps were undertaken to ensure smoother and less noisy signals. These steps included wavelet smoothing, displacement artifact removal, and z -score normalization. The deep learning network architecture consisted of two stages; convolutional neural networks and bidirectional long short-term memory units. The training of the model was evaluated based on a k-fold cross-validation scheme of tenfolds using several performance evaluation metrics including Cohen’s kappa, accuracy, sensitivity, specificity, precision, and F1-score. The developed algorithm achieved the highest average accuracy of 99.62% with a precision of 98.85% in classifying patients based on the pulmonary disease types using CNN + BDLSTM. Furthermore, a total agreement of 98.26% was obtained between the predictions and original classes within the training scheme. This study paves the way towards implementing deep learning models in clinical settings to assist clinicians in decision making related to the recognition of pulmonary diseases. Supplementary Information The online version supplementary material available at 10.1007/s12652-021-03184-y.

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“…The applicability of the model proposed in this paper is further verified by combining the diagnosis and analysis of different clinical diseases [36,37]. According to the characteristics of the existing samples, using the waveform parameters identified by the algorithm model proposed in this paper, some clinical examples of disease diagnosis are shown in Table 5.…”

Section: Analysis and Diagnosismentioning

confidence: 99%

A New Method for Refined Recognition for Heart Disease Diagnosis Based on Deep Learning

Song

2020

Information

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The proper evaluation of heart health requires professional medical experience. Therefore, in clinical diagnosis practice, the development direction is to reduce the high dependence of the diagnosis process on medical experience and to more effectively improve the diagnosis efficiency and accuracy. Deep learning has made remarkable achievements in intelligent image analysis technology involved in the medical process. From the aspect of cardiac diagnosis, image analysis can extract more profound and abundant information than sequential electrocardiogram (ECG) signals. Therefore, a new region recognition and diagnosis method model of a two-dimensional ECG (2D-ECG) signal based on an image format is proposed. This method can identify and diagnose each refined waveform in the cardiac conduction cycle reflected in the image format ECG signal, so as to realize the rapid and accurate positioning and visualization of the target recognition area and finally get the analysis results of specific diseases. The test results show that compared with the results obtained by a one-dimensional sequential ECG signal, the proposed model has higher average diagnostic accuracy (98.94%) and can assist doctors in disease diagnosis with better visualization effect.

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An Automatic Diagnosis of Arrhythmias Using a Combination of CNN and LSTM Technology

Cited by 71 publications

References 33 publications

CNN-LSTM Based Model for ECG Arrhythmias and Myocardial Infarction Classification

CNN-LSTM Based Model for ECG Arrhythmias and Myocardial Infarction Classification

Recognition of pulmonary diseases from lung sounds using convolutional neural networks and long short-term memory

A New Method for Refined Recognition for Heart Disease Diagnosis Based on Deep Learning

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