Počet záznamů: 1
QRS detection and classification in Holter ECG data in one inference step
- 1.
SYSNO ASEP 0559662 Druh ASEP J - Článek v odborném periodiku Zařazení RIV J - Článek v odborném periodiku Poddruh J Článek ve WOS Název QRS detection and classification in Holter ECG data in one inference step Tvůrce(i) Ivora, Adam (UPT-D)
Viščor, Ivo (UPT-D) RID, ORCID, SAI
Nejedlý, Petr (UPT-D) RID, SAI
Smíšek, Radovan (UPT-D) RID, ORCID, SAI
Koščová, Zuzana (UPT-D)
Bulková, V. (CZ)
Halámek, Josef (UPT-D) RID, ORCID, SAI
Jurák, Pavel (UPT-D) RID, ORCID, SAI
Plešinger, Filip (UPT-D) RID, ORCID, SAICelkový počet autorů 9 Číslo článku 12641 Zdroj.dok. Scientific Reports. - : Nature Publishing Group - ISSN 2045-2322
Roč. 12, č. 1 (2022)Poč.str. 9 s. Forma vydání Online - E Jazyk dok. eng - angličtina Země vyd. GB - Velká Británie Klíč. slova ECG ; Machine learning ; Deep learning ; QRS detection ; QRS classification Vědní obor RIV FS - Lékařská zařízení, přístroje a vybavení Obor OECD Medical engineering CEP FW01010305 GA TA ČR - Technologická agentura ČR Způsob publikování Open access Institucionální podpora UPT-D - RVO:68081731 UT WOS 000830116000055 EID SCOPUS 85134781113 DOI 10.1038/s41598-022-16517-4 Anotace While various QRS detection and classification methods were developed in the past, the Holter ECG data acquired during daily activities by wearable devices represent new challenges such as increased noise and artefacts due to patient movements. Here, we present a deep-learning model to detect and classify QRS complexes in single-lead Holter ECG. We introduce a novel approach, delivering QRS detection and classification in one inference step. We used a private dataset (12,111 Holter ECG recordings, length of 30 s) for training, validation, and testing the method. Twelve public databases were used to further test method performance. We built a software tool to rapidly annotate QRS complexes in a private dataset, and we annotated 619,681 QRS complexes. The standardised and down-sampled ECG signal forms a 30-s long input for the deep-learning model. The model consists of five ResNet blocks and a gated recurrent unit layer. The model's output is a 30-s long 4-channel probability vector (no-QRS, normal QRS, premature ventricular contraction, premature atrial contraction). Output probabilities are post-processed to receive predicted QRS annotation marks. For the QRS detection task, the proposed method achieved the F1 score of 0.99 on the private test set. An overall mean F1 cross-database score through twelve external public databases was 0.96 +/- 0.06. In terms of QRS classification, the presented method showed micro and macro F1 scores of 0.96 and 0.74 on the private test set, respectively. Cross-database results using four external public datasets showed micro and macro F1 scores of 0.95 +/- 0.03 and 0.73 +/- 0.06, respectively. Presented results showed that QRS detection and classification could be reliably computed in one inference step. The cross-database tests showed higher overall QRS detection performance than any of compared methods. Pracoviště Ústav přístrojové techniky Kontakt Martina Šillerová, sillerova@ISIBrno.Cz, Tel.: 541 514 178 Rok sběru 2023 Elektronická adresa https://www.nature.com/articles/s41598-022-16517-4
Počet záznamů: 1