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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

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • 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

  • 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