SVM Based ECG Classification Using Rhythm and Morphology Features, ClusternAnalysis and Multilevel Noise Estimation

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F17%3A00487043" target="_blank" >RIV/68081731:_____/17:00487043 - isvavai.cz</a>
Result on the web
<a href="http://www.cinc.org/archives/2017/" target="_blank" >http://www.cinc.org/archives/2017/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.22489/CinC.2017.172-200" target="_blank" >10.22489/CinC.2017.172-200</a>

Result language
angličtina
Original language name
SVM Based ECG Classification Using Rhythm and Morphology Features, ClusternAnalysis and Multilevel Noise Estimation
Original language description
Smartphone-based ECG devices comprise great potential in screening for arrhythmias. However, its feasibility is limited by poor signal quality leading to incorrect rhythm classification. In this study, advanced method for automatic classification of normal rhythm (N), atrial fibrillation (A), other rhythm (O), and noisy records (P) is introduced. Methods: Two-step SVM approach followed by simple threshold based rules was used for data classification. In the first step, various features were derived from separate beats to represent particular events (normal as well as pathological and artefacts) in more detail. Output of the first classifier was used to calculate global features describing entire ECG. These features were then used to train the second classification model. Both classifiers were evaluated on training set via cross-validation technique, and additionally on hidden testing set. Results: In the Phase II of challenge, total F1 score of the method is 0.81 and 0.84 within hidden challenge dataset and training set, respectively. Particular F1 scores within hidden challenge dataset are 0.90 (N), 0.81 (A), 0.72 (O), and 0.55 (P). Particular F1 scores within training set are 0.91 (N), 0.85 (A), 0.76 (O), and 0.73 (P).
Czech name
—
Czech description
—

Project
<a href="/en/project/GAP102%2F12%2F2034" target="_blank" >GAP102/12/2034: Analysis of Relationship between Electrical Activity and Blood Flow at the Heart Ventricles</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Similar results(10)