Heartbeat Sound Signal Classification Using Deep Learning.

Clicks: 456
ID: 66052
2019
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Abstract
Presently, most deaths are caused by heart disease. To overcome this situation, heartbeat sound analysis is a convenient way to diagnose heart disease. Heartbeat sound classification is still a challenging problem in heart sound segmentation and feature extraction. Dataset-B applied in this study that contains three categories Normal, Murmur and Extra-systole heartbeat sound. In the purposed framework, we remove the noise from the heartbeat sound signal by applying the band filter, After that we fixed the size of the sampling rate of each sound signal. Then we applied down-sampling techniques to get more discriminant features and reduce the dimension of the frame rate. However, it does not affect the results and also decreases the computational power and time. Then we applied a purposed model Recurrent Neural Network (RNN) that is based on Long Short-Term Memory (LSTM), Dropout, Dense and Softmax layer. As a result, the purposed method is more competitive compared to other methods.
Reference Key
raza2019heartbeatsensors Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Raza, Ali;Mehmood, Arif;Ullah, Saleem;Ahmad, Maqsood;Choi, Gyu Sang;On, Byung-Won;
Journal sensors
Year 2019
DOI
E4819
URL
Keywords
Deep learning classification rnn heart sound

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