Detection of heart valve disorders from PCG signals using TQWT, FA-MVEMD, Shannon energy envelope and deterministic learning

Zeng, Wei; Lin, Zixiang; Yuan, Chengzhi; Wang, Qinghui; Liu, Fenglin; Wang, Ying

doi:10.1007/s10462-021-09969-z

Cited by 20 publications

(7 citation statements)

References 53 publications

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“…For example, Baygin et al used feature extraction based on homeomorphically irreducible tree graph pattern and feature generation based on maximum absolute pooling to nally classify grade 7 and grade 4 arrhythmia on a large ECG dataset, the accuracy was 92.92% and 97.18%, respectively [5]. Wang et al used convolution with different kernel sizes to extract multidimensional features from ECG signals, and nally used the max-pooling layer for feature screening, achieving 99.06% accuracy on the MITDB based on mixed signal processing and deterministic theory, and nally achieved 97.75%, 98.69% and 98.48% accuracy on several databases [9]. Ghosh performed time-frequency analysis on the signal and used the multi-class composite classi er to classify the three types of PCG, namely AS, MS and MR, with sensitivities of 99.44%, 98.66% and 96.22% [10].…”

Section: B Related Workmentioning

confidence: 99%

A Pooling Convolution Model for Multi-classification of ECG and PCG Signals

Wang

Zang

Xiang

et al. 2023

Preprint

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Cardiovascular disease has always been one of the major threats to human health. The recognition of ECG and PCG signals, which are physiological signals generated when the heart beats, can greatly improve the efficiency of cardiovascular disease detection using deep learning techniques. Based on this, it is proposed that a simple and effective pooling convolution model for multi-classification of ECG and PCG signals. Firstly, ECG, PCG and synchronized ECG-PCG data are pre-processed. Then, several structure blocks are designed, including the block stacked by max-pooling layer(MCM), convolution layer and max-pooling layer, its multiple variants and the residual block(REC). All network models that use these structure blocks separately are based on the ResNet-18 framework. By changing the number of structure blocks, the model can be applied to ECG and PCG data at different sampling rates. The final test shows that the accuracy of the model using the MCM structure block is 98.70%, 92.58% and 99.19% respectively on the ECG, PCG fusion dataset and synchronized ECG-PCG dataset, which is higher than all the networks using its multiple variants. At the same time, the accuracy is improved by 0.02%, 4.30% and 1.43% compared to the model using the REC structure block. In addition, this work also carries out tests on multiple ECG and PCG datasets and compares them with other published literature, further verifying that the model using the MCM structure block has a higher detection rate for ECG and PCG signals.

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Section: B Related Workmentioning

confidence: 99%

A Pooling Convolution Model for Multi-classification of ECG and PCG Signals

Wang

Zang

Xiang

et al. 2023

Preprint

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“…Study of abnormal heart rate (rhythm) has important clinical meaning. PCG signals are also used to diagnose cardiac abnormalities [3]. The heart consists of two parts related to pumping, i.e.…”

Section: Phonocardiogrammentioning

confidence: 99%

Study of Amplifier Operating Point to Improve Phonocardiogram Signal Representation in a Custom-made Electronic Stethoscope

Astono¹,

Kuswanto²,

Sumarna³

et al. 2022

TEM Journal

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The performance of a custom-made electronic stethoscope needs to be improved through a study on the operating point of the amplifier. The aim of this study is to analyse the composition and content of the frequency components contained in the phonocardiogram signal by spectral extraction. The output is a voltage signal that represents the heart rate. The operating point of the amplifier is varied. The signal is recorded on a laptop and analysed. The results show that the operating point produces a balanced swing signal. Hence, the amplifier’s operating point does not affect the component spectrum in the recorded signal.

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“…Let Assumptions 1-4 hold. Let the weight updating laws for the critic NN and the action NN be ( 29) and (32), respectively. Then we obtain that i) Υ 0 i (K), Ĵ0 i (K −1), W 0 ic and W 0 ia are all uniformly ultimately bounded; ii) W 0 ζic (K) and W 0 ζia (K) are exponentially stable, provided that when the following assumptions are hold:…”

Section: Stabilitymentioning

confidence: 99%

“…Based on the DLT, a rapid dynamical pattern recognition (DPR) approach was proposed to indicate that a time-varying dynamical pattern could be represented in a time-invariant and spatially distributed form, and rapid DPR can be achieved based on the difference between the system dynamics of the test and training patterns [29]. Utilizing the rapid DPR approach, in previous works, we have given analytical results showing that the obtained knowledge of the fault be utilized to achieve rapid FD scheme [30], with applications on the early detection of rotating stall [31], detection of heart valve disorders from phonocardiogram signals and so on [32].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for A Class of Nonlinear Systems Based on Reinforcement Learning and Deterministic Learning

Zhu¹,

Wu²,

Chen³

2022

Preprint

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In this paper a novel fault diagnosis (FD) approach combing the rein-forcement learning (RL) and the deterministic learning theory (DLT)is proposed for a class of discrete-time nonlinear system with unknowndynamics. First, a bank of DLT-based dynamical neural network (NN)identifiers are utilized to achieve locally-accurate approximations ofthe unknown system dynamics along the normal and fault trajecto-ries. Based on this, a novel feature learning method combing the RLwith DLT is proposed to further adapt the NN weights to extract dis-criminative features. The extracted features are represented by constant NNs. Finally, constant NN-based dynamical estimators are constructedto achieve rapid FD. The novelties of the proposed methods are: 1)according to the DLT, the exponential convergence of the NN weightscan be rigorously analysed based on the Lyapunov stability theory;2) a new class of strategic utility function is designed based on theconcept of the synchronization error in DLT-based dynamical pat-tern recognition approach, which is different from other RL-based FDtechniques that penalise the future wrong FD decisions. Simulationresults shows the practical significance of the proposed FD method.

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Detection of heart valve disorders from PCG signals using TQWT, FA-MVEMD, Shannon energy envelope and deterministic learning

Cited by 20 publications

References 53 publications

A Pooling Convolution Model for Multi-classification of ECG and PCG Signals

A Pooling Convolution Model for Multi-classification of ECG and PCG Signals

Study of Amplifier Operating Point to Improve Phonocardiogram Signal Representation in a Custom-made Electronic Stethoscope

Fault Diagnosis for A Class of Nonlinear Systems Based on Reinforcement Learning and Deterministic Learning

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