Automatic Classification of CAD ECG Signals With SDAE and Bidirectional Long Short-Term Network

Wang, Eric Ke; Zhang, Xun; Pan, Leyun

doi:10.1109/access.2019.2936525

Cited by 27 publications

(18 citation statements)

References 28 publications

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“…Machine learning-based approaches are frequently utilized to recognize arrhythmia [8] , [9] , [10] , [11] , [12] . Pre-processing, feature extraction, and classification tasks are the main steps involved in these approaches [13] . The feature extraction step has a critical role to achieve high classification performance.…”

Section: Introductionmentioning

confidence: 99%

Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records

Yıldırım

Talo

Ciaccio

et al. 2020

Computer Methods and Programs in Biomedicine

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Highlights DNN model is proposed to detect arrhythmia. More than 10,000 individual subject ECG records subject records are used. Two different scenarios are employed: (i) reduced rhythms (seven rhythm types) and (ii) merged rhythms (four rhythm types). Achieved 92.24% and 96.13% accuracies for the reduced and merged rhythm classes, respectively. System can aid cardiologists in the accurate detection of arrhythmia accurately.

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Section: Introductionmentioning

confidence: 99%

Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records

Yıldırım

Talo

Ciaccio

et al. 2020

Computer Methods and Programs in Biomedicine

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“…A SDAE is a stack of single-layer DAE, which takes a corrupted version of data as input to reconstruct or denoise original input. Figure 2 presents the construction of the SDAE [16]. Above all, a DAE randomly breaks the original input based on q D , so that the original input turns to x.…”

Section: Proposed Modelmentioning

confidence: 99%

Dissolved Gas Analysis of Insulating Oil in Electric Power Transformers: A Case Study Using SDAE-LSTM

Luo

Zhang

et al. 2020

Mathematical Problems in Engineering

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Dissolved gas analysis (DGA) is the most important tool for fault diagnosis in electric power transformers. To improve accuracy of diagnosis, this paper proposed a new model (SDAE-LSTM) to identify the dissolved gases in the insulating oil of power transformers and perform parameter analysis. The performance evaluation is attained by the case studies in terms of recognition accuracy, precision ratio, and recall ratio. Experiment results show that the SDAE-LSTM model performs better than other models under different input conditions. As evidenced from the analyses, the proposed model achieves considerable results of recognition accuracy (95.86%), precision ratio (95.79%), and recall ratio (97.51%). It can be confirmed that the SDAE-LSTM model using the dissolved gas in the power transformer for fault diagnosis and analysis has great research prospect.

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“…However, DAE reconstructs the input signal by corrupting one to get a more robust system ( Fig. 4(a)) [30].…”

Section: B} {W = θ Fmentioning

confidence: 99%

Short-Term Wind-Speed Forecasting Based on Multiscale Mathematical Morphological Decomposition, K-Means Clustering, and Stacked Denoising Autoencoders

Dong

Sun

et al. 2020

IEEE Access

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Wind energy plays an increasingly important role in economic development. In this study, we propose a hybrid short-term wind-speed forecasting model comprising multiscale mathematical morphological decomposition (MMMD), K-means clustering algorithm, and stacked denoising autoencoder (SDAE) networks. First, in contrast to traditional signal-decomposing tools, the original wind-speed sequence is decomposed into a series of subsequences with different frequencies and fluctuant levels using the adaptive multiscale mathematical morphological algorithm directly in the time domain. The signal does not need to be transferred from the time domain to the frequency domain; hence, the accuracy can be considerably improved. Moreover, this is the first study that uses a time domain signal-decomposing tool in a hybrid wind forecasting model. Next, the data are split into different clusters of similar frequencies and fluctuant level subsequences using the K-means algorithm. The characteristics of each cluster are then captured using the SDAE as the core forecasting unit. Finally, the predictions of all subsequences are aggregated to obtain the final wind speed. The data from two real wind turbines are used to evaluate the performance of the proposed model, and the forecasting results are compared with five different benchmark models, namely, backpropagation neural network (BPNN), stacked denoising autoencoder (SDAE), mathematical morphology-backpropagation, mathematical morphology-SDAE, and K-means-SDAE for multiple scales, and two novel hybrid wind forecasting models namely, wavelet transform (WT)-K-means-SDAE and variation mode decomposition (VMD)-K-means-long short-term memory networks (LSTMs). The results of the comparison demonstrate that the proposed model provides a short-term wind-speed forecasting method whose prediction accuracy decreases with time; however, the proposed model achieves a better performance in comparison with other exiting models. At same time, the proposed model significantly increases the prediction accuracy of wind-speed forecasting and can be a reference for future research in this area. INDEX TERMS K-means clustering, multiscale mathematical morphological decomposition, short-term wind-speed forecasting, stacked denoising autoencoders.

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Automatic Classification of CAD ECG Signals With SDAE and Bidirectional Long Short-Term Network

Cited by 27 publications

References 28 publications

Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records

Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records

Dissolved Gas Analysis of Insulating Oil in Electric Power Transformers: A Case Study Using SDAE-LSTM

Short-Term Wind-Speed Forecasting Based on Multiscale Mathematical Morphological Decomposition, K-Means Clustering, and Stacked Denoising Autoencoders

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