A robust method for online heart sound localization in respiratory sound based on temporal fuzzy c-means


MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, vol.53, no.1, pp.45-56, 2015 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 53 Issue: 1
  • Publication Date: 2015
  • Doi Number: 10.1007/s11517-014-1210-6
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.45-56
  • Keywords: Localization, Heart sound, Lung sound, Logarithmic energy, Shannon entropy, Fuzzy c-means, Temporal fuzzy c-means, LUNG SOUNDS, APPROXIMATE ENTROPY, RATE-VARIABILITY, SYSTEM, SEGMENTATION, CANCELLATION, SEPARATION, REDUCTION, ALGORITHM, SIGNAL
  • Ataturk University Affiliated: Yes


This work presents a detailed framework to detect the location of heart sound within the respiratory sound based on temporal fuzzy c-means (TFCM) algorithm. In the proposed method, respiratory sound is first divided into frames and for each frame, the logarithmic energy features are calculated. Then, these features are used to classify the respiratory sound as heart sound (HS containing lung sound) and non-HS (only lung sound) by the TFCM algorithm. The TFCM is the modified version fuzzy c-means (FCM) algorithm. While the FCM algorithm uses only the local information about the current frame, the TFCM algorithm uses the temporal information from both the current and the neighboring frames in decision making. To measure the detection performance of the proposed method, several experiments have been conducted on a database of 24 healthy subjects. The experimental results show that the average false-negative rate values are 0.8 +/- A 1.1 and 1.5 +/- A 1.4 %, and the normalized area under detection error curves are and for the TFCM method in the low and medium respiratory flow rates, respectively. These average values are significantly lower than those obtained by FCM algorithm and by the other compared methods in the literature, which demonstrates the efficiency of the proposed TFCM algorithm. On the other hand, the average elapsed time of the TFCM for a data with length of s is 0.2 +/- A 0.05 s, which is slightly higher than that of the FCM and lower than those of the other compared methods.