Journal ArticleDOI
The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
Norden E. Huang,Zheng Shen,Steven R. Long,Man-Li C. Wu,Hsing H. Shih,Quanan Zheng,Nai-Chyuan Yen,C. C. Tung,Henry H. Liu +8 more
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In this paper, a new method for analysing nonlinear and nonstationary data has been developed, which is the key part of the method is the empirical mode decomposition method with which any complicated data set can be decoded.Abstract:
A new method for analysing nonlinear and non-stationary data has been developed. The key part of the method is the empirical mode decomposition method with which any complicated data set can be dec...read more
Citations
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Evidence for a Rapid Global Climate Shift across the Late 1960s
Peter G. Baines,Chris K. Folland +1 more
TL;DR: It is shown that a number of important characteristics of the global atmospheric circulation and climate changed in a near-monotonic fashion over the decade, or less, centered on the late 1960s as discussed by the authors.
Journal ArticleDOI
Empirical Mode Decomposition vs. Wavelet Decomposition for the Extraction of Respiratory Signal From Single-Channel ECG: A Comparison
Domenico Labate,Fabio La Foresta,Gianluigi Occhiuto,Francesco Carlo Morabito,Aime Lay-Ekuakille,P. Vergallo +5 more
TL;DR: Two techniques of decomposition of the ECG signal into suitable bases of functions are proposed, such as the empirical mode decomposition (EMD) and the wavelet analysis, and performance achieved by applying these algorithms to extract the respiratory waveform shape from single-channel ECG is presented.
Journal ArticleDOI
Constructing the mode shapes of a bridge from a passing vehicle: a theoretical study
TL;DR: In this paper, a theoretical algorithm for constructing the mode shapes of a bridge from the dynamic responses of a test vehicle moving over the bridge is presented, where only one or few sensors are required to be installed on the test vehicle.
Journal ArticleDOI
Rotational Machine Health Monitoring and Fault Detection Using EMD-Based Acoustic Emission Feature Quantification
TL;DR: A methodology for rotational machine health monitoring and fault detection using empirical mode decomposition (EMD)-based AE feature quantification is presented and incorporates a threshold-based denoising technique into EMD to increase the signal-to-noise ratio of the AE bursts.
Journal ArticleDOI
Application of empirical mode decomposition to heart rate variability analysis.
TL;DR: The results demonstrate the ability of empirical mode decomposition to isolate the two main components of one chirp series and three signals simulated by the integral pulse frequency modulation model, and consistently to isolate at least four main components localised in the autonomic bands of 14 real signals under controlled breathing manoeuvres.
References
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