A survey on Hilbert-Huang transform: Evolution, challenges and solutions

Souza, Uender Barbosa de; Escola, João Paulo Lemos; Brito, Leonardo da Cunha

doi:10.1016/j.dsp.2021.103292

Cited by 60 publications

(30 citation statements)

References 161 publications

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“…Instead of solving IF equations (see (7) in [31] for STFT-SST or (2.3) in [6] for wavelet-SST), the LMSST directly uses the local maximum as the reference location to which we can reassign the T-F coefficients.…”

Section:    mentioning

confidence: 99%

“…(1) represents many real-world signals that arise naturally in practical applications of a wide range of fields [1]- [7]. The amplitude-modulated frequency-modulated (AM-FM) function k s is called intrinsic mode function (IMF), and contains important time-varying information.…”

Section: Introductionmentioning

confidence: 99%

“…The EMD algorithm is a popular tool for decomposing MCS into its IMFs via a data-driven manner. Though attractive due to its simplicity and effectiveness, the lack of a solid mathematical foundation makes it hard to guarantee the accuracy or applicability of EMD [7]. Synchrosqueezing, therefore, has become very popular among the research community and various extensions of this method has been proposed [1]- [3], [5], [10]- [18].…”

Section: Introductionmentioning

confidence: 99%

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Robustness Properties of Local Maximum Synchrosqueezing Transform

Zhang¹

2022

Preprint

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<p>The development of the synchrosqueezing techniques has been a very active research area in recent years. The synchrosqueezing transform (SST) is a powerful tool for time-frequency analysis of multicomponent nonstationary signals. The local maximum synchrosqueezing transform (LMSST) was recently introduced as a variant of SST. It directly uses the local maximum to reassign the time-frequency coefficient in order to obtain an energy-concentrated representation. However the theoretical foundation of LMSST is yet to be properly established. In this paper, we study the linear-chirp model based LMSST with the objective of obtaining its error bounds of mode separation/retrieval under noise-free and noisy conditions. Our theorems demonstrate the applicability of LMSST mathematically. Numerical experiments including synthetic and real-world signals are provided to illustrate the general theory.</p>

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Section:    mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robustness Properties of Local Maximum Synchrosqueezing Transform

Zhang¹

2022

Preprint

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“…Therefore, there is an urgent need to realize a quick, accurate, and robust analysis of ECG data. Due to the time variation and unique individual characteristics of the ECG, the feature of the same kind of ECG signal varies in different patients under different physical conditions, which poses considerable difficulties for recognition of ECG patterns [5]. Many works have been carried out to advance the automatic analysis of ECG signals.…”

Section: Introductionmentioning

confidence: 99%

Two-stream Network for ECG Signal Classification

Hou¹,

Qin²,

Su³

2022

Preprint

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Electrocardiogram (ECG), a technique for medical monitoring of cardiac activity, is an important method for identifying cardiovascular disease. However, analyzing the increasing quantity of ECG data consumes a lot of medical resources. This paper explores an effective algorithm for automatic classifications of multi-classes of heartbeat types based on ECG. Most neural network based methods target the individual heartbeats, ignoring the secrets embedded in the temporal sequence. And the ECG signal has temporal variation and unique individual characteristics, which means that the same type of ECG signal varies among patients under different physical conditions. A two-stream architecture is used in this paper and presents an enhanced version of ECG recognition based on this. The architecture achieves classification of holistic ECG signal and individual heartbeat and incorporates identified and temporal stream networks. Identified networks are used to extract features of individual heartbeats, while temporal networks aim to extract temporal correlations between heartbeats. Results on the MIT-BIH Arrhythmia Database demonstrate that the proposed algorithm performs an accuracy of 99.38%. In addition, the proposed algorithm reaches an 88.07% positive accuracy on massive data in real life, showing that the proposed algorithm can efficiently categorize different classes of heartbeat with high diagnostic performance.

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“…Over the last few years, empirical mode decomposition (EMD) has been considered one of the efficient approaches for the improvement of financial market forecasting ( Huang et al, 1998 ; Nava, Matteo & Aste, 2018 ). The EMD approach decomposes the original signal within a finite set of approximately orthogonal oscillating elements, they are called intrinsic mode functions (IMFs) ( Souza, Escola & Brito, 2022 ). IMFs have particular time scales of oscillations determined by the own maximum and minimum of the data, which are retrieved by the information itself without depending on any other function.…”

Section: Introductionmentioning

confidence: 99%

Empirical mode decomposition using deep learning model for financial market forecasting

Jin

Jin²,

Chen

2022

PeerJ Computer Science

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Financial market forecasting is an essential component of financial systems; however, predicting financial market trends is a challenging job due to noisy and non-stationary information. Deep learning is renowned for bringing out excellent abstract features from the huge volume of raw data without depending on prior knowledge, which is potentially fascinating in forecasting financial transactions. This article aims to propose a deep learning model that autonomously mines the statistical rules of data and guides the financial market transactions based on empirical mode decomposition (EMD) with back-propagation neural networks (BPNN). Through the characteristic time scale of data, the intrinsic wave pattern was obtained and then decomposed. Financial market transaction data were analyzed, optimized using PSO, and predicted. Combining the nonlinear and non-stationary financial time series can improve prediction accuracy. The predictive model of deep learning, based on the analysis of the massive financial trading data, can forecast the future trend of financial market price, forming a trading signal when particular confidence is satisfied. The empirical results show that the EMD-based deep learning model has an excellent predicting performance.

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A survey on Hilbert-Huang transform: Evolution, challenges and solutions

Cited by 60 publications

References 161 publications

Robustness Properties of Local Maximum Synchrosqueezing Transform

Robustness Properties of Local Maximum Synchrosqueezing Transform

Two-stream Network for ECG Signal Classification

Empirical mode decomposition using deep learning model for financial market forecasting

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