Tachycardias Classification via the Generalized Mean Frequency and Generalized Frequency Variance of Electrocardiograms

Ho, Charlotte Yuk-Fan; Ling, Bingo Wing‐Kuen; Deng, Danny Xinghua; Liu, Yuwei

doi:10.1007/s00034-021-01819-1

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…It is well known that biomedical signals can provide useful information for diagnosis of many diseases [7,14,17]. However, many biomedical signals acquired from physical sensors are inevitably contaminated by noise interference that imposes uncertainty to signals [4,22].…”

Section: Introductionmentioning

confidence: 99%

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Biomedical Signal Denoising Via Permutating, Thresholding and Averaging Noise Components Obtained from Hierarchical Multiresolution Analysis-Based Empirical Mode Decomposition

Zhou

Ling

Zhou

2022

Circuits Syst Signal Process

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Biomedical signals are usually contaminated with interfering noise, which may result in misdiagnosis of diseases. Additive white Gaussian noise (AWGN) is a common interfering noise, and much work has been proposed to suppress AWGN. Recently, hierarchical multiresolution analysis-based empirical mode decomposition (EMD) denoising method is proposed and shows potential performance. In order to further improve performance of hierarchical multiresolution analysis-based EMD denoising, this paper combines hierarchical multiresolution analysis-based EMD, thresholding operation and averaging operation together. In particular, EMD is applied to the first intrinsic mode function (IMF) in the first level of decomposition to obtain IMFs in the second level of decomposition. The first IMF in the second level of decomposition is chosen as the noise component. For each realization, this noise component is segmented into various pieces, and these segments are permutated. By summing up this permutated IMF to the rest of IMFs in both the first level of decomposition and the second level of decomposition, new realization of the noisy signal is obtained. Next, for original signal and each realization of newly generated noisy signal, EMD is performed again. IMFs in the first level of decomposition are obtained. Then, consecutive mean squared errors-based criterion is used to classify IMFs in the first level of decomposition into the information group of IMFs or the noise group of IMFs. Next, EMD is applied to IMFs in the noise group in the first level of decomposition and IMFs in the second level of decomposition are obtained. Detrended fluctuation analysis is used to classify IMFs in the second level of decomposition into the information group of IMFs or the noise group of IMFs. After that, thresholding is applied to IMFs in the noise group in the second level of the decomposition to obtain denoised signal. Finally, the above procedures are repeated, and several realizations of denoised signals are obtained. Then, denoised signal obtained by applying thresholding to each realization is averaged together to obtain final denoised signal. The extensive numerical simulations are conducted and the results show that our proposed method outperforms existing EMD-based denoising methods.

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Section: Introductionmentioning

confidence: 99%

“…As signal quality is reduced, diagnostic performance may be degraded. Hence, suppressing noise while enhancing the important features of signals plays a very important role in many biomedical signal processing applications such as medical diagnosis and estimation of index of well-being based on biomedical signals [7,14,17,27].…”

Section: Introductionmentioning

confidence: 99%

Biomedical Signal Denoising Via Permutating, Thresholding and Averaging Noise Components Obtained from Hierarchical Multiresolution Analysis-Based Empirical Mode Decomposition

Zhou

Ling

Zhou

2022

Circuits Syst Signal Process

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Transformer-based intelligent fault diagnosis methods of mechanical equipment: A survey

Wang,

Dong,

Cheng

et al. 2024

Open Physics

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Transformer is extensively employed in natural language processing, and computer vision (CV), with the self-attention structure. Due to its outstanding long-range dependency modeling and parallel computing capability, some leading researchers have recently attempted to apply Transformer to intelligent fault diagnosis tasks for mechanical equipment, and have achieved remarkable results. Physical phenomena such as changes in vibration, sound, and heat play a crucial role in the research of mechanical equipment fault diagnosis, which directly reflects the operational status and potential faults of mechanical equipment. Currently, intelligent fault diagnosis of mechanical equipment based on monitoring signals such as vibration, sound, and temperature using Transformer-based models remains a popular research topic. While some review literature has explored the related principles and application scenarios of Transformer, there is still a lack of research on its application in intelligent fault diagnosis tasks for mechanical equipment. Therefore, this work begins by examining the current research status of fault diagnosis methods for mechanical equipment. This study first provides a brief overview of the development history of Transformer, outlines its basic structure and principles, and analyzes the characteristics and advantages of its model structure. Next it focuses on three model variants of Transformer that have generated a significant impact in the field of CV. Following that, the research progress and current challenges of Transformer-based intelligent fault diagnosis methods for mechanical equipment are discussed. Finally, the future development direction of Transformer in the field of mechanical equipment fault diagnosis is proposed.

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The Machine-Learning-Empowered Gesture Recognition Glove

et al. 2023

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Tachycardias Classification via the Generalized Mean Frequency and Generalized Frequency Variance of Electrocardiograms

Cited by 3 publications

References 21 publications

Biomedical Signal Denoising Via Permutating, Thresholding and Averaging Noise Components Obtained from Hierarchical Multiresolution Analysis-Based Empirical Mode Decomposition

Biomedical Signal Denoising Via Permutating, Thresholding and Averaging Noise Components Obtained from Hierarchical Multiresolution Analysis-Based Empirical Mode Decomposition

Transformer-based intelligent fault diagnosis methods of mechanical equipment: A survey

The Machine-Learning-Empowered Gesture Recognition Glove

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