A novel method of diagnosing premature ventricular contraction based on sparse auto-encoder and softmax regression

Yang, Jian; Bai, Yang; Li, Guojun; Liu, Ming; Liu, Xiuling

doi:10.3233/bme-151454

Cited by 21 publications

(14 citation statements)

References 19 publications

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“…For this application, the length of signal extracted around each beat even was 250 samples, with 89 samples before the annotation and 160 samples after the annotation. These values were selected because they were found to provide generally acceptable classification performance and allowed for a more direct comparison with the PVC detection system described by Yang et al 10 An n value of 25 provided a sufficient number of base features for the following layer to perform feature selection on. An m value of 20 provided sufficiently complex filters to extract a wide range of characteristics from the signal.…”

Section: Proposed Pvc Detection Methodsmentioning

confidence: 99%

“…6,7 In addition to these two main approaches to PVC detection, there are methods utilizing other approaches to the connected problems of feature extraction and beat classification, 8 Markov models, independent component analysis, 9 and autoencoders. 10 Geddes and Warner 3 used R-R interarrival time, QRS complex duration, and signal slope during several sections of the QRS complex as features in their detection system. They made classification decisions based on a manually constructed decision tree.…”

Section: Introductionmentioning

confidence: 99%

“…11 Yu and Chou 9 used independent component analysis to identify and extract a set of features, which were combined with QRS complex timing information to create the full feature set passed to their neural network classifier. Yang et al 10 used a sparse autoencoder (SAE) to generate a feature vector for classification. This resulted in a large number of estimated network weights, which increased the computation and data required to train the network and increased the potential for overfitting.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

PVC Detection Using a Convolutional Autoencoder and Random Forest Classifier

Gordon

Williams

2018

Biocomputing 2019

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The accurate detection of premature ventricular contractions (PVCs) in patients is an important task in cardiac care for some patients. In some cases, the usefulness to physicians in detecting PVCs stems from their long-term correlations with dangerous heart conditions. In other cases their potential as a precursor to serious cardiac events may make their detection a useful early warning mechanism. In many of these applications, the long-term nature of the monitoring required and the infrequency of PVCs make manual observation for PVCs impractical. Existing methods of automated PVC detection suffer from drawbacks such as the need to use difficult to extract morphological features, domain-specific features, or large numbers of estimated parameters. In particular, systems using large numbers of trained parameters have the potential to require large amounts of training data and computation and may have issues generalizing due to their potential to overfit. To address some of these drawbacks, we developed a novel PVC detection algorithm based around a convolutional autoencoder to address these weaknesses and validated our method using the MIT-BIH arrhythmia database.

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Section: Proposed Pvc Detection Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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PVC Detection Using a Convolutional Autoencoder and Random Forest Classifier

Gordon

Williams

2018

Biocomputing 2019

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“…Therefore, in order to ensure the uniqueness of solution, the weight decay term is employed in the loss function of the softmax classifier. A unique classification result can be obtained by minimizing the (14).…”

Section: Improved Sae Network For Intelligent Diagnosis Of Machinery mentioning

confidence: 99%

An improved deep network for intelligent diagnosis of machinery faults with similar features

Yang

Xie

Yang

et al. 2019

IEEJ Transactions Elec Engng

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Currently, fault diagnosis, especially single fault diagnosis, has yielded fruitful research results. However, for concurrent faults, which exist more widely in real industrial systems. Due to the challenges of complex model mechanism, single and coupled monitoring data, as well as difficulty distinguished similar features, causing the existing research findings on concurrent faults have not yet achieved satisfactory effects. Considering the problem and in order to extract features automatically while enhancing the diagnostic accuracy, an improved sparse autoencoder (SAE) network for intelligent diagnosis is proposed. Specifically, the main contents are as follows: firstly, the original time‐domain signals are preprocessed to eliminate the noise disturbance. The processed signals are then adopted as the input of diagnostic network. Secondly, an initial weight constraint and an adaptive loss function are constructed to improve the diversity of SAE feature learning. Thirdly, limited‐memory quasi‐newton algorithm (L‐BFGS) algorithm is used to solve the optimal solution and the machinery health condition identification is achieved by softmax classifier. Finally, the verification experiments based on the monitoring data obtained from different machinery are conducted. The results demonstrate the effectiveness and practicability of the proposed method. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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“…The schematic of the network structure is shown in Figure 3. Softmax regression is an extension of the logistic regression model on multiple classifications [29]. The category tag of the logistic regression can only take two values, whereas the softmax tag can take on multiple values [30].…”

Section: Softmax Regressionmentioning

confidence: 99%

Deep Sparse Autoencoder for Feature Extraction and Diagnosis of Locomotive Adhesion Status

Zhang

Cheng

Liu

et al. 2018

Journal of Control Science and Engineering

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The model is difficult to establish because the principle of the locomotive adhesion process is complex. This paper presents a data-driven adhesion status fault diagnosis method based on deep learning theory. The adhesion coefficient and creep speed of a locomotive constitute the characteristic vector. The sparse autoencoder unsupervised learning network studies the input vector, and the single-layer network is superimposed to form a deep neural network. Finally, a small amount of labeled data is used to fine-tune training the entire deep neural network, and the locomotive adhesion state fault diagnosis model is established. Experimental results show that the proposed method can achieve a 99.3% locomotive adhesion state diagnosis accuracy and satisfy actual engineering monitoring requirements.

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A novel method of diagnosing premature ventricular contraction based on sparse auto-encoder and softmax regression

Cited by 21 publications

References 19 publications

PVC Detection Using a Convolutional Autoencoder and Random Forest Classifier

PVC Detection Using a Convolutional Autoencoder and Random Forest Classifier

An improved deep network for intelligent diagnosis of machinery faults with similar features

Deep Sparse Autoencoder for Feature Extraction and Diagnosis of Locomotive Adhesion Status

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