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- W2980291235 abstract "This paper presents an approach for automatic segmentation of cardiac events from non-invasive sounds recordings, without the need of having an auxiliary signal reference. In addition, methods are proposed to subsequently differentiate cardiac events which correspond to normal cardiac cycles, from those which are due to abnormal activity of the heart. The detection of abnormal sounds is based on a model built with parameters which are obtained following feature extraction from those segments that were previously identified as normal fundamental heart sounds. The proposed algorithm achieved a sensitivity of 91.79% and 89.23% for the identification of normal fundamental, S <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sub> and S <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sub> sounds, and a true positive (TP) rate of 81.48% for abnormal additional sounds. These results were obtained using the PASCAL Classifying Heart Sounds challenge (CHSC) database." @default.
- W2980291235 created "2019-10-18" @default.
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- W2980291235 date "2019-07-01" @default.
- W2980291235 modified "2023-10-18" @default.
- W2980291235 title "An Approach for Automatic Identification of Fundamental and Additional Sounds from Cardiac Sounds Recordings" @default.
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- W2980291235 doi "https://doi.org/10.1109/embc.2019.8857695" @default.
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