QRS Complex Detection in Paced and Spontaneous Ultra-high-frequency ECG

Zuzana Koscova1, Adam Ivora1, Petr Nejedly2, Josef Halamek3, Pavel Jurak1, Magdalena Matejkova4, Pavel Leinveber5, Karol Curila6, Lucie Znojilova6, Filip Plesinger1
1Institute of Scientific Instruments of the CAS, 2Institute of Scientific Instruments of the Czech Academy of Science, 3Institute of Scientific Instruments, CAS, CZ, 4International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic, 5International Clinical Research Center, St. Anne’s University Hospital, Brno, Czech Republic, 6Cardiocenter FNKV and 3rd Faculty of Medicine in Prague


Background: Analysis of ultra-high frequency ECG (UHF-ECG, sam-pled at 5, 000 Hz) informs about dyssynchrony of ventricles activation. Nowadays, this information can be evaluated in real-time when optimizing a lead position during a pacemaker implantation procedure. However, our current solution for real-time QRS detection requires complex signal pre-processing to filter and remove pacemaker stimuli.

Aim: In this study, we present a deep learning method for QRS complex detection in UHF-ECG signals.

Method: We use a UNet convolutional neural network to process a 3-second window from V1, V3, and V6 lead of UHF-ECG. Each input lead was transformed to a z-score. The output of the network is a vector of QRS probabilities. Resultant QRS positions are based on QRS probability and distance criterion.

Results: The UNet model has been trained on 2,250 ECG signals acquired from 780 patients from the FNUSA-ICRC hospital (Brno, Czechia) and tested on 300 signals from 47 subjects from FNKV hospital (Prague, Czechia). We received an overall F1-score of 97.11 % on the test set with an F1-score of 96.3% and 97.25 % for spontaneous and stimulated QRSs, respectively. The proposed approach is superior to our previous solution, with an overall F1-score of 90.43 % on the test set. Test results showed an F1-score of 93.40 % and 89.91 % for spontaneous and stimulated QRSs, respectively.

Conclusion: Our results indicate that the proposed method should improve beat detection in real-time UHF-ECG analysis; the method does not require prior elimination of pacing artifacts. Its higher sensitivity allows a reduction of measurement time if it is used during implantation.