QRS detection and classification in Holter ECG data in one inference step

0559662 - ÚPT 2023 RIV GB eng J - Článek v odborném periodiku
Ivora, Adam - Viščor, Ivo - Nejedlý, Petr - Smíšek, Radovan - Koščová, Zuzana - Bulková, V. - Halámek, Josef - Jurák, Pavel - Plešinger, Filip
QRS detection and classification in Holter ECG data in one inference step.
Scientific Reports. Roč. 12, č. 1 (2022), č. článku 12641. ISSN 2045-2322. E-ISSN 2045-2322
Grant CEP: GA TA ČR(CZ) FW01010305
Institucionální podpora: RVO:68081731
Klíčová slova: ECG * Machine learning * Deep learning * QRS detection * QRS classification
Obor OECD: Medical engineering
Impakt faktor: 4.6, rok: 2022
Způsob publikování: Open access
https://www.nature.com/articles/s41598-022-16517-4

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.
Trvalý link: https://hdl.handle.net/11104/0335256