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

Tópicos

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Resumo

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.

Resumo Limpo

work present detail framework detect locat heart sound within respiratori sound base tempor fuzzi cmean tfcm algorithm propos method respiratori sound first divid frame frame logarithm energi featur calcul featur use classifi respiratori sound heart sound hs contain lung sound nonh lung sound tfcm algorithm tfcm modifi version fuzzi cmean fcm algorithm fcm algorithm use local inform current frame tfcm algorithm use tempor inform current neighbor frame decis make measur detect perform propos method sever experi conduct databas healthi subject experiment result show averag falseneg rate valu normal area detect error curv tfcm method low medium respiratori flow rate respect averag valu signific lower obtain fcm algorithm compar method literatur demonstr effici propos tfcm algorithm hand averag elaps time tfcm data length s s slight higher fcm lower compar method

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