کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
386210 | 660880 | 2010 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Detection of cardiac abnormality from PCG signal using LMS based least square SVM classifier
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله

چکیده انگلیسی
Auscultation, the technique of listening to heart sounds with a stethoscope can be used as a primary detection system for diagnosing heart valve disorders. Phonocardiogram, the digital recording of heart sounds is becoming increasingly popular as it is relatively inexpensive. In this paper, a technique to improve the performance of the Least Square Support Vector Machine (LSSVM) is proposed for classification of normal and abnormal heart sounds using wavelet based feature set. In the proposed technique, the Lagrange multiplier is modified based on Least Mean Square (LMS) algorithm, which in turn modifies the weight vector to reduce the classification error. The basic idea is to enlarge the separating boundary surface, such that the separability between the clusters is increased. The updated weight vector is used at the time of testing. The performance of the proposed systems is evaluated on 64 different recordings of heart sounds comprising of normal and five different pathological cases. It is found that the proposed technique classifies the heart sounds with higher recognition accuracy than competing techniques.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 37, Issue 12, December 2010, Pages 8019-8026
Journal: Expert Systems with Applications - Volume 37, Issue 12, December 2010, Pages 8019-8026
نویسندگان
Samit Ari, Koushik Hembram, Goutam Saha,