Computer Aided detection for fibrillations and flutters using deep convolutional neural network
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F19%3A50015691" target="_blank" >RIV/62690094:18450/19:50015691 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0020025519301884" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0020025519301884</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.ins.2019.02.065" target="_blank" >10.1016/j.ins.2019.02.065</a>
Alternative languages
Result language
angličtina
Original language name
Computer Aided detection for fibrillations and flutters using deep convolutional neural network
Original language description
Fibrillations and flutters are serious diseases influence the normal functioning of the heart. Among the most frequently occurring heart disorders belong atrial fibrillation (Afib), atrial flutter (Afl), and ventricular fibrillation (Vfib). Nowadays, heart failures are mostly detected by electrocardiogram (ECG) device by examining the signal transferred from electrodes placed on the human body to the output display. The signal is examined by professional health personnel, who are looking for an obvious pattern representing the normal or abnormal rhythm of the heart. Nevertheless, information from ECG can be distorted by noise on data transmission. Moreover,problematic pattern does not have to be so much different from normal and it can be difficult to recognize them just by human eye even by an expert in the field. An automated computer-aided diagnosis (CAD) is an approach to make decision support for elimination of these lacks. For early diagnosis, CAD tool should work in like real-time system without big time consuming and dependency on data and measuring differences of each device. This paper proposes a novel approach of a CAD system to the detection of fibrillations and flutters by our 8-layer deep convolutional neural network. Proposed model requires only basic data normalization without pre-processing and feature extraction from raw ECG samples. We have achieved the accuracy, specificity, and sensitivity of 98.45%, 99.27%, and 99.87% respectively. Designed system can be directly implemented like decision support system in clinical environment.
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
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Information sciences
ISSN
0020-0255
e-ISSN
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Volume of the periodical
486
Issue of the periodical within the volume
červen
Country of publishing house
US - UNITED STATES
Number of pages
9
Pages from-to
231-239
UT code for WoS article
000464301300016
EID of the result in the Scopus database
2-s2.0-85062047707