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

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...

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Publicado en:Medical & Biological Engineering & Computing Vol. 53; no. 1; pp. 45 - 57
Autores principales: Shamsi, Hamed, Ozbek, I Yucel
Formato: Journal Article
Publicado: Springer Nature Jan2015
Acceso en línea:Ver este registro en EBSCOhost
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      jtl: Medical & Biological Engineering & Computing
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      dt: Jan2015
      vid: 53
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      pub: Springer Nature
      place: New York, New York
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        atl: A robust method for online heart sound localization in respiratory sound based on temporal fuzzy c-means.
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        au:
          Shamsi, Hamed
          Ozbek, I Yucel
      sug:
        subj:
          Algorithms
          Logic
          Heart Sounds Physiology
          Online Systems
          Respiratory Sounds Physiology
          Sound Localization
          Adolescence
          Adult
          Pharmacokinetics
          Child
          Female
          Male
          Middle Age
          Time Factors
          Young Adult
          Adolescent: 13-18 years
          Adult: 19-44 years
          Child: 6-12 years
          Middle Aged: 45-64 years
          Female
          Male
      ab: 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 ± 1.1 and 1.5 ± 1.4 %, and the normalized area under detection error curves are 0.0145 and 0.0269 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 0.2 ± 0.05 s is 0.2 ± 0.05 s, which is slightly higher than that of the FCM and lower than those of the other compared methods.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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