title: A framework for automatic heart sound analysis without segmentation.
abstract: BACKGROUND
  A new framework for heart sound analysis is proposed. One of the most difficult processes in heart sound analysis is segmentation, due to interference form murmurs.
  
  
  METHODS
  Equal number of cardiac cycles were extracted from heart sounds with different heart rates using information from envelopes of autocorrelation functions without the need to label individual fundamental heart sounds (FHS). The complete method consists of envelope detection, calculation of cardiac cycle lengths using auto-correlation of envelope signals, features extraction using discrete wavelet transform, principal component analysis, and classification using neural network bagging predictors.
  
  
  RESULTS
  The proposed method was tested on a set of heart sounds obtained from several on-line databases and recorded with an electronic stethoscope. Geometric mean was used as performance index. Average classification performance using ten-fold cross-validation was 0.92 for noise free case, 0.90 under white noise with 10 dB signal-to-noise ratio (SNR), and 0.90 under impulse noise up to 0.3 s duration.
  
  
  CONCLUSIONS
  The proposed method showed promising results and high noise robustness to a wide range of heart sounds. However, more tests are needed to address any bias that may have been introduced by different sources of heart sounds in the current training set, and to concretely validate the method. Further work include building a new training set recorded from actual patients, then further evaluate the method based on this new training set.
authors.affiliation: Department of Communication and Integrated Systems, Tokyo Institute of Technology, Japan 2-12-1-W9-108 Ookayama, Meguro-ku, Tokyo, Japan. toey123@gmail.com
authors.forename: Sumeth
authors.initials: S
authors.lastname: Yuenyong
authors.forename: Akinori
authors.initials: A
authors.lastname: Nishihara
authors.forename: Waree
authors.initials: W
authors.lastname: Kongprawechnon
authors.forename: Kanokvate
authors.initials: K
authors.lastname: Tungpimolrut
mesh_terms: D001331:Automation; D006347:Heart Sounds / Q000502:physiology; D006801:Humans; D016571:Neural Networks, Computer; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D013223:Statistics as Topic / Q000379:methods*