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Fast Detection of Ventricular Fibrillation and Ventricular Tachycardia in 1-Lead ECG from Three-Second Blocks
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SYSNO ASEP 0509014 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Fast Detection of Ventricular Fibrillation and Ventricular Tachycardia in 1-Lead ECG from Three-Second Blocks Author(s) Plešinger, Filip (UPT-D) RID, ORCID, SAI
Andrla, Petr (UPT-D)
Viščor, Ivo (UPT-D) RID, ORCID, SAI
Halámek, Josef (UPT-D) RID, ORCID, SAI
Jurák, Pavel (UPT-D) RID, ORCID, SAINumber of authors 5 Article number 8743881 Source Title Computing in Cardiology 2018, 45. - New York : IEEE, 2018 Number of pages 4 s. Publication form Print - P Action Computing in Cardiology Conference (CinC) /45./ Event date 23.09.2018 - 26.09.2018 VEvent location Maastricht Country NL - Netherlands Event type WRD Language eng - English Country US - United States Keywords cardiology ; automated detection ; ventricular fibrillation ; ventricular tachycardia Subject RIV JB - Sensors, Measurment, Regulation OECD category Medical engineering R&D Projects LO1212 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) Institutional support UPT-D - RVO:68081731 UT WOS 000482598700147 EID SCOPUS 85068797206 DOI 10.22489/CinC.2018.037 Annotation Background: Ventricular tachycardia (VT) is dangerous irregularity of heart rhythm. VT may evolve into ventricular fibrillation (VF) which often leads to cardiac death. Therefore, fast automated detection of VF/VT events is of the utmost importance. Here, we present a method detecting VT and ventricular fibrillation (VF) events suitable for real-time application on continuously incoming ECG data.
Method: We designed a method for detection of VF/VT events in short-time (3 s), 1-lead ECG blocks. Five features are extracted from this block using analysis of ECG spectra, derivatives, amplitude measures and auto-correlation. The extracted features are fed into a logistic regression model showing the probability of a VF/VT event. The model was trained on the public PhysioNet CUDB dataset consisting of 393 automatically selected blocks.
Results: The model (AUC 0.99) showed a sensitivity and specificity of 95 % and 97 %, respectively (5-fold cross-validation). The model was tested on the public PhysioNet VFDB dataset, showing specificity and sensitivity of 95 % and 83 %, respectively. Both the feature extraction code (Matlab format) and the model are publicly accessible and easy implementation of the logistic regression model predetermines it for real-time applications.Workplace Institute of Scientific Instruments Contact Martina Šillerová, sillerova@ISIBrno.Cz, Tel.: 541 514 178 Year of Publishing 2020
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