Cardi-Net: A Deep Neural Network for classification of Cardiac disease using Phonocardiogram Signal
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F20%3APU143882" target="_blank" >RIV/00216305:26220/20:PU143882 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/abs/pii/S0169260720315832" target="_blank" >https://www.sciencedirect.com/science/article/abs/pii/S0169260720315832</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.cmpb.2022.106727" target="_blank" >10.1016/j.cmpb.2022.106727</a>
Alternative languages
Result language
angličtina
Original language name
Cardi-Net: A Deep Neural Network for classification of Cardiac disease using Phonocardiogram Signal
Original language description
Background and objectives The lack of medical facilities in isolated areas makes many patients remain aloof from quick and timely diagnosis of cardiovascular diseases, leading to high mortality rates. A deep learning based method for automatic diagnosis of multiple cardiac diseases from Phonocardiogram (PCG) signals is proposed in this paper. Methods The proposed system is a combination of deep learning based convolutional neural network (CNN) and power spectrogram Cardi-Net, which can extract deep discriminating features of PCG signals from the power spectrogram to identify the diseases. The choice of Power Spectral Density (PSD) makes the model extract highly discriminatory features significant for the multi-classification of four common cardiac disorders. Results Data augmentation techniques are applied to make the model robust, and the model undergoes 10-fold cross-validation to yield an overall accuracy of 98.879% on the test dataset to diagnose multi heart diseases from PCG signals. Conclusion The proposed model is completely automatic, where signal pre-processing and feature engineering are not required. The conversion time of power spectrogram from PCG signals is very low range from 0.10 sec to 0.11 sec. This reduces the complexity of the model, making it highly reliable and robust for real-time applications. The proposed architecture can be deployed on cloud and a low cost processor, desktop, android app leading to proper access to the dispensaries in remote areas.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
ISSN
0169-2607
e-ISSN
1872-7565
Volume of the periodical
219
Issue of the periodical within the volume
1
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
11
Pages from-to
1-11
UT code for WoS article
000778631600004
EID of the result in the Scopus database
2-s2.0-85126586158