By Topic

Robustly separating sound components in human body based on 2-ch ICA and EM algorithm with dirichlet distribution

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

6 Author(s)
Hashiodani, K. ; Sci. & Eng., Ritsumeikan Univ., Kusatsu, Japan ; Takada, S. ; Fukumizu, Y. ; Yamauchi, H.
more authors

An algorithm to separate breath sounds (BS), blood stream sounds (BSS), and heart sounds (HS) from sound components in the human body (biosignals) is introduced as a pre-process for detecting circulatory disease such as auricular fibrillation (AF), arteriosclerosis and apnea syndrome. Existing methods in the time-frequency model have been proposed to analyze biosignals with microphone sensors to obtain BS, BSS and HS. However, these methods have negative points. Thus, we propose band pass filter, 2-ch independent component analysis (ICA) and expectation-maximization (EM) algorithm with Dirichlet distribution to solve these problems. Experimental results show that our method performs better than existing methods.

Published in:

Biomedical and Health Informatics (BHI), 2012 IEEE-EMBS International Conference on

Date of Conference:

5-7 Jan. 2012