Decision support system for arrhythmia prediction using convolutional neural network structure without preprocessing

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F19%3A50015698" target="_blank" >RIV/62690094:18450/19:50015698 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/article/10.1007/s10489-019-01461-0" target="_blank" >https://link.springer.com/article/10.1007/s10489-019-01461-0</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s10489-019-01461-0" target="_blank" >10.1007/s10489-019-01461-0</a>

Result language
angličtina
Original language name
Decision support system for arrhythmia prediction using convolutional neural network structure without preprocessing
Original language description
Arrhythmia is a disease-influencing heart and is manifested by an irregular heartbeat. Atrial fibrillation (A(fib)), atrial flutter (A(fl)), and ventricular fibrillation (V-fib) are heart arrhythmias affecting predominantly senior citizens. An electrocardiogram (ECG) is a device serving to measure the ECG signal and diagnosis of an abnormal pattern which represents a heartbeat defects. Though it is possible to analyze these signals manually, in some cases it is a difficult task due to the often signal distortion by noise. Furthermore, manual analyzation of patterns is subjective and can lead to an inaccurate diagnosis. An automated computer-aided diagnosis (CAD) is a technique to eliminate these shortcomings. In this work, we present an 6-layer deep convolutional neural network (CNN) for automatic ECG pattern classification of the normal (N-r), A(fib), A(fl), and V-fib classes. This proposed CNN model requires simple feature extraction and no pre-processing of ECG signals. For two seconds ECG segments, the model obtained the accuracy of 97.78%, specificity and sensitivity of 98.82% and 99.76% respectively. This proposed system can be used as an assistant automatic tool in a clinical environment as a decision support system.
Czech name
—
Czech description
—

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)

Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Similar results(10)