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صفحه اصلی
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چهاردهمین کنفرانس بین المللی فناوری اطلاعات و دانش
Heart Sound Classification based on Group-based Sparse Features of PCG Signal
نویسندگان :
Zahra Hossein-Nejad
1
Mehdi Nasri
2
1- دانشگاه آزاد اسلامی واحد سیرجان
2- دانشگاه آزاد اسلامی واحد خمینی شهر
کلمات کلیدی :
Classification of heart sounds،Feature extraction،Group based sparse،Discrete wavelet transform
چکیده :
Heart sounds play an important role in diagnosing heart diseases. The phonocardiogram (PCG), is the recording of the sounds produced by the heart and the main heart valves. The PCG signal can be internal microphones that are inserted into the heart and valves are recorded invasively as well as non-invasively by placing the microphone on the surface of the body. Environmental factors reduce the signal-to-noise ratio (SNR). This has made it difficult and time-consuming for experts to detect heart sounds. Therefore, automatic classification of heart sounds is essential. In this article, the features are extracted by the Discrete Wavelet Transform (DWT) and group-based sparse to distinguish the main heart sounds and abnormal heart sounds. Then the Support Vector Machine (SVM) algorithm is used to classify the heart sounds. The simulation results on the PhysioNet dataset affirm the suggested method outperforms classical methods in terms of sensitivity, precision, F1-score and, specificity.
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بیشتر
ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 44.5.0
