The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Artificial Intelligence Applications on Classification of Heart Sounds
Abstract
The aim of this chapter is to classify normal and extra systole heart sounds using artificial intelligence methods. Initially, both heart sounds have been passed from Butterworth, Chebyshev, Elliptic digital filter in specific frequency values to remove noise. Afterwards, features of heart sounds have been obtained for classification. For this process, wavelet transform and Mel-frequency cepstral coefficients (MFCC) methods have been applied. Training and test data have been created for classifier by taking means and standard deviation of gained feature. Support vector machine (SVM) and artificial neural network (ANN) methods have been used for classification of these heart sounds. Using wavelet and MFCC features, classification success of SVM has been obtained as 93.33% and 100%, respectively. Using wavelet and MFCC features, classification success of ANN has been obtained as 83.33% and 90%, respectively.
Related Content
|
Utku Kose.
© 2018.
26 pages.
|
|
Kamalanand Krishnamurthy, Mannar Jawahar Ponnuswamy.
© 2018.
24 pages.
|
|
Omer Deperlioglu.
© 2018.
27 pages.
|
|
Orhan Bölükbaş, Harun Uğuz.
© 2018.
25 pages.
|
|
Aydın Çetin, Tuba Gökhan.
© 2018.
27 pages.
|
|
Pandian Vasant.
© 2018.
16 pages.
|
|
Huseyin Coskun, Tuncay Yigit.
© 2018.
38 pages.
|
|
|