Open AccessHeart Sounds Analysis and Classification Based on Long-Short Term Memory
Author(s) -
Emre Çancıoğlu,
Savaş Şahin,
Yalçın İşler
Publication year - 2020
Publication title -
akıllı sistemler ve uygulamaları dergisi
Language(s) - English
Resource type - Journals
ISSN - 2667-6893
DOI - 10.54856/jiswa.202005104
Subject(s) - phonocardiogram , computer science , speech recognition , artifact (error) , term (time) , sound (geography) , long short term memory , heart sounds , pattern recognition (psychology) , waveform , sound analysis , artificial intelligence , point (geometry) , mathematics , medicine , artificial neural network , cardiology , acoustics , telecommunications , radar , physics , geometry , quantum mechanics , recurrent neural network
In this study, the development of an algorithm for the classification of heart sound phonocardiogram waveforms such as Normal, Murmur, Extrasystole, Artifact. By presenting the approach used for classification from a general machine learning application point of view, the types of classifiers used were detailed by comparing their features and their performance. The Long-Short Term Memory method which supports the classification of each cardiac cycle in sound recordings. In addition to the LSTM-based features, our method incorporates spectral features to summarize the characteristics of the entire sound recording.