Gated recurrent unit-based heart sound analysis for heart failure screening

Gao, Shan; Zheng, Yineng; Guo, Xingming

doi:10.1186/s12938-020-0747-x

Cited by 31 publications

(21 citation statements)

References 44 publications

(45 reference statements)

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“…Furthermore, the characteristics of each cardiac period may differ. Therefore, the frame length was set to 1.6 s (approximately two cardiac periods), which started with S1 onset [ 40 ]. The intervals between two frames were reserved about two periods to avoid the overlap [ 40 ].…”

Section: Methodsmentioning

confidence: 99%

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Deep Learning-Based Heart Sound Analysis for Left Ventricular Diastolic Dysfunction Diagnosis

et al. 2021

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The aggravation of left ventricular diastolic dysfunction (LVDD) could lead to ventricular remodeling, wall stiffness, reduced compliance, and progression to heart failure with a preserved ejection fraction. A non-invasive method based on convolutional neural networks (CNN) and heart sounds (HS) is presented for the early diagnosis of LVDD in this paper. A deep convolutional generative adversarial networks (DCGAN) model-based data augmentation (DA) method was proposed to expand a HS database of LVDD for model training. Firstly, the preprocessing of HS signals was performed using the improved wavelet denoising method. Secondly, the logistic regression based hidden semi-Markov model was utilized to segment HS signals, which were subsequently converted into spectrograms for DA using the short-time Fourier transform (STFT). Finally, the proposed method was compared with VGG-16, VGG-19, ResNet-18, ResNet-50, DenseNet-121, and AlexNet in terms of performance for LVDD diagnosis. The result shows that the proposed method has a reasonable performance with an accuracy of 0.987, a sensitivity of 0.986, and a specificity of 0.988, which proves the effectiveness of HS analysis for the early diagnosis of LVDD and demonstrates that the DCGAN-based DA method could effectively augment HS data.

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Section: Methodsmentioning

confidence: 99%

“…Therefore, the frame length was set to 1.6 s (approximately two cardiac periods), which started with S1 onset [ 40 ]. The intervals between two frames were reserved about two periods to avoid the overlap [ 40 ]. Table 2 describes the number of HS samples in this paper.…”

Section: Methodsmentioning

confidence: 99%

Deep Learning-Based Heart Sound Analysis for Left Ventricular Diastolic Dysfunction Diagnosis

et al. 2021

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“…Gated recurrent unit (GRU) is a type of enhanced LSTM and RNN where it can keep the essential data and relations of input sequences efficiently and purge the less important data to reduce the memory and processing time of GRU. Due to this uniqueness, GRU is widely used in sequential data prediction and reduces the processing time [5], [27]. GRU is improvised from LSTM to reduce lagging and delay processing in the neural network [27].…”

Section: Grumentioning

confidence: 99%

“…Due to this uniqueness, GRU is widely used in sequential data prediction and reduces the processing time [5], [27]. GRU is improvised from LSTM to reduce lagging and delay processing in the neural network [27]. The structure of the GRU is simplified from the LSTM, with two gates, but no separate memory cell.…”

Section: Grumentioning

confidence: 99%

Hybrid deep learning model using recurrent neural network and gated recurrent unit for heart disease prediction

Krishnan

Magalingam

Ibrahim

2021

IJECE

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<span>This paper proposes a new hybrid deep learning model for heart disease prediction using recurrent neural network (RNN) with the combination of multiple gated recurrent units (GRU), long short-term memory (LSTM) and Adam optimizer. This proposed model resulted in an outstanding accuracy of 98.6876% which is the highest in the existing model of RNN. The model was developed in Python 3.7 by integrating RNN in multiple GRU that operates in Keras and Tensorflow as the backend for deep learning process, supported by various Python libraries. The recent existing models using RNN have reached an accuracy of 98.23% and deep neural network (DNN) has reached 98.5%. The common drawbacks of the existing models are low accuracy due to the complex build-up of the neural network, high number of neurons with redundancy in the neural network model and imbalance datasets of Cleveland. Experiments were conducted with various customized model, where results showed that the proposed model using RNN and multiple GRU with synthetic minority oversampling technique (SMOTe) has reached the best performance level. This is the highest accuracy result for RNN using Cleveland datasets and much promising for making an early heart disease prediction for the patients.</span>

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“…For instance, the 1D convolutional neural networks (CNN) were proposed to learn the deep features [ 25 ] or hand-crafted features [ 24 ] of the HS and divided HS signals into normal and abnormal directly. Gated recurrent unit (GRU) is an improved recurrent neural network (RNN) proposed by Chung et al in 2014 [ 26 ], which has a good performance in the classification and prediction of HS signals [ 27 ]. Furthermore, hybrid deep learning networks can combine the spatial features extracted by the CNN and the temporal features captured by the RNN.…”

Section: Introductionmentioning

confidence: 99%

Prediction of exercise sudden death in rabbit exhaustive swimming using deep neural network

et al. 2021

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Background and objective Moderate exercise contributes to good health. However, excessive exercise may lead to cardiac fatigue, myocardial damage and even exercise sudden death. Monitoring the heart health has important implication to prevent exercise sudden death. Diagnosis methods such as electrocardiogram, echocardiogram, blood pressure and histological analysis have shown that arrhythmia and left ventricular fibrosis are early warning symptoms of exercise sudden death. Heart sounds (HS) can reflect the changes of cardiac valve, cardiac blood flow and myocardial function. Deep learning has drawn wide attention because of its ability to recognize disease. Therefore, a deep learning method combined with HS was proposed to predict exercise sudden death in New Zealand rabbits. The objective is to develop a method to predict exercise sudden death in New Zealand rabbits. Methods This paper proposed a method to predict exercise sudden death in New Zealand rabbits based on convolutional neural network (CNN) and gated recurrent unit (GRU). The weight-bearing exhaustive swimming experiment was conducted to obtain the HS of exercise sudden death and surviving New Zealand rabbits (n = 11/10) at four different time points. Then, the improved Viola integral method and double threshold method were employed to segment HS signals. The segmented HS frames at different time points were taken as the input of a combined CNN and GRU called CNN–GRU network to complete the prediction of exercise sudden death. Results In order to evaluate the performance of proposed network, CNN and GRU were used for comparison. When the fourth time point segmented HS frames were taken as input, the result shows that the proposed network has better performance with an accuracy of 89.57%, a sensitivity of 89.38% and a specificity of 92.20%. In addition, the segmented HS frames at different time points were input into CNN–GRU network, and the result shows that with the progress of the experiment, the prediction accuracy of exercise sudden death in New Zealand rabbits increased from 50.98 to 89.57%. Conclusion The proposed network shows good performance in classifying HS, which proves the feasibility of deep learning in exploring exercise sudden death. Further, it may have important implications in helping humans explore exercise sudden death.

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Gated recurrent unit-based heart sound analysis for heart failure screening

Cited by 31 publications

References 44 publications

Deep Learning-Based Heart Sound Analysis for Left Ventricular Diastolic Dysfunction Diagnosis

Deep Learning-Based Heart Sound Analysis for Left Ventricular Diastolic Dysfunction Diagnosis

Hybrid deep learning model using recurrent neural network and gated recurrent unit for heart disease prediction

Prediction of exercise sudden death in rabbit exhaustive swimming using deep neural network

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