Parralel Recurrent Convolutional Neural Network for Abnormal Heart Sound Classification

Gharehbaghi, Arash,1972-Högskolan i Halmstad,Akademin för informationsteknologi (författare)

This paper presents the results of a study performed on Parallel Convolutional Neural Network (PCNN) toward detecting heart abnormalities from the heart sound signals. The PCNN preserves dynamic contents of the signal in a parallel combination of the recurrent neural network and a Convolutional Neural Network (CNN). The performance of the PCNN is evaluated and compared to the one obtained from a Serial form of the Convolutional Neural Network (SCNN) as well as two other baseline studies: a Long- and Short-Term Memory (LSTM) neural network and a Conventional CNN (CCNN). We employed a well-known public dataset of heart sound signals: the Physionet heart sound. The accuracy of the PCNN, was estimated to be 87.2% which outperforms the rest of the three methods: the SCNN, the LSTM, and the CCNN by 12%, 7%, and 0.5%, respectively. The resulting method can be easily implemented in an Internet of Things platform to be employed as a decision support system for the screening heart abnormalities.

Partovi, ElahehAmirkabir University of Technology, Tehran, Iran (författare)
Babic, AnkicaLinköping University, Linköping, Sweden; University of Bergen, Bergen, Norway (författare)
Högskolan i HalmstadAkademin för informationsteknologi (creator_code:org_t)

Ingår i:Caring is sharing - exploiting the value in data for health and innovationAmsterdam : IOS Press, s. 526-5309781643683881

Av författaren/redakt...: Gharehbaghi, Ara ...; Partovi, Elaheh; Babic, Ankica

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