Paper Info
Title
Second Heart Sound (S2) Decomposition by Hilbert Vibration Decomposition (HVD) for Affective Signal Modeling and Learning.
Abstract
This article presents a novel signal decomposition method, Hilbert vibration decomposition (HVD), for analyzing one of the major heart sound components second heart sound (S2) signal for affective signal modeling. In this proposed method, three kinds of simulated S2 signals are generated and the typical one is chosen for decomposition. For HVD method, a FIR filter is designed to separate each of the decomposed components. Finally, performance indicators, including the number of decomposed components, Hilbert spectrum, and spectral centroids, are measured. To evaluate the performance of HVD, the decomposed components are compared with those generated by empirical mode decomposition (EMD) method. The experimental result shows that the number of meaningful decomposed components and frequency resolutions by using HVD method are better than those by using EMD. Such results also reveal the HVD method is superior to the normal EMD method, especially for low frequency narrow band bio-signals such second heart sound, thereby facilitating generating discriminant features for model learning.
Year
DOI
Venue
2013
10.1007/978-3-662-46315-4_23
ADVANCES IN WEB-BASED LEARNING
Keywords
Field
DocType
Hilbert vibration decomposition (HVD),Empirical mode decomposition (EMD),Second heart sound (S2),Non-stationary signal decomposition
Hilbert spectrum,Signal modeling,Pattern recognition,Computer science,Speech recognition,Decomposition method (constraint satisfaction),Artificial intelligence,Vibration,Finite impulse response,Multimedia,Centroid,Decomposition
Conference
Volume
ISSN
Citations 
8390
0302-9743
0
PageRank 
References 
Authors
0.34
6
5
Name
Order
Citations
PageRank
Shovan Barma1143.74
Bo-Wei Chen226230.12
Hung-Ming Wang300.34
Hung-Jui Wang400.34
Jhing-fa Wang5982114.31