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.

Institute of Scientific Instruments of the Czech Academy of Sciences Brno Czech Republic

Medical Data Transfer s r o Brno Czech Republic

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Pan J, Tompkins WJ. A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. 1985;32(3):230–236. doi: 10.1109/TBME.1985.325532. PubMed DOI

Elgendi M. Fast QRS detection with an optimised knowledge-based method: Evaluation on 11 standard ECG databases. PLoS ONE. 2013;8(9):e73557. doi: 10.1371/journal.pone.0073557. PubMed DOI PMC

Malik J, Soliman EZ, Wu HT. An adaptive QRS detection algorithm for ultra-long-term ECG recordings. J. Electrocardiol. 2020;60:165–171. doi: 10.1016/j.jelectrocard.2020.02.016. PubMed DOI

Hamilton, P. & Limited, E. P. Open source ECG analysis. (2002).

Engelse WA, Zeelenberg C. A single scan algorithm for QRS detection and feature extraction. IEEE Comput. Cardiol. 1979;2:37–42.

A. Lourenço, H. Silva, P. Leite, R. Lourenço, and A. Fred, "REAL TIME ELECTROCARDIOGRAM SEGMENTATION FOR FINGER BASED ECG BIOMETRICS," in Proceedings of the International Conference on Bio-inspired Systems and Signal Processing, 2012, pp. 49–54.

V. Kalidas and L. Tamil, "Real-time QRS detector using stationary wavelet transform for automated ECG analysis," in Proceedings - 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering, BIBE 2017, 2017, vol. 2018-January, pp. 457–461.

Mehta SS, Shete DA, Lingayat NS, Chouhan VS. K-means algorithm for the detection and delineation of QRS-complexes in electrocardiogram. IRBM. 2010;31(1):48–54. doi: 10.1016/j.irbm.2009.10.001. DOI

Saini I, Singh D, Khosla A. QRS detection using K-Nearest Neighbor algorithm (KNN) and evaluation on standard ECG databases. J. Adv. Res. 2013;4(4):331–344. doi: 10.1016/j.jare.2012.05.007. PubMed DOI PMC

Cai W, Hu D. QRS complex detection using novel deep learning neural networks. IEEE Access. 2020;8:97082–97089. doi: 10.1109/ACCESS.2020.2997473. DOI

Silva P, et al. Towards better heartbeat segmentation with deep learning classification. Sci. Rep. 2020;10:1–13. doi: 10.1038/s41598-019-56847-4. PubMed DOI PMC

Moody GB, Mark RG. The impact of the MIT-BIH arrhythmia database. IEEE Eng. Med. Biol. Mag. 2001;20(3):45–50. doi: 10.1109/51.932724. PubMed DOI

De Chazal P, O'Dwyer M, Reilly RB. Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Trans. Biomed. Eng. 2004;51(7):1196–1206. doi: 10.1109/TBME.2004.827359. PubMed DOI

Nascimento NMM, Marinho LB, Peixoto SA, et al. Heart arrhythmia classification based on statistical moments and structural co-occurrence. Circuits Syst. Signal Process. 2020;39(2):631–650. doi: 10.1007/s00034-019-01196-w. DOI

Hammad M, Iliyasu AM, Subasi A, Ho ESL, El-Latif AAA. A multitier deep learning model for arrhythmia detection. IEEE Trans. Instrum. Meas. 2021;70:63. doi: 10.1109/TIM.2020.3033072. DOI

Wang R, Fan J, Li Y. Deep multi-scale fusion neural network for multi-class arrhythmia detection. IEEE J. Biomed. Heal. Informatics. 2020;24(9):2461–2472. doi: 10.1109/JBHI.2020.2981526. PubMed DOI

Ferretti J, Randazzo V, Cirrincione G, Pasero E. Smart Innovation, Systems and Technologies. Springer; 2021. 1-D convolutional neural network for ECG arrhythmia classification; pp. 269–279.

Oh SL, Ng EYK, Tan RS, Acharya UR. Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats. Comput. Biol. Med. 2018;102:278–287. doi: 10.1016/j.compbiomed.2018.06.002. PubMed DOI

F. Murat, O. Yildirim, M. Talo, U. B. Baloglu, Y. Demir, and U. R. Acharya, "Application of deep learning techniques for heartbeats detection using ECG signals-analysis and review," Computers in Biology and Medicine, vol. 120. Elsevier Ltd, 01-May-2020. PubMed

Chen A, et al. Multi-information fusion neural networks for arrhythmia automatic detection. Comput. Methods Programs Biomed. 2020;193:2. doi: 10.1016/j.cmpb.2020.105479. PubMed DOI

Moody G, Moody B, Silva I. Robust detection of heart beats in multimodal data: The physionet/computing in cardiology challenge 2014. Comput. Cardiol. 2014;41:549–552.

Gao H, et al. An open-access ECG database for algorithm evaluation of QRS detection and heart rate estimation. J. Med. Imaging Heal. Inf. 2019;9:9. doi: 10.1166/jmihi.2019.2563. DOI

da Silva HP, Lourenço A, Fred A, Raposo N, Aires-de-Sousa M. Check Your Biosignals Here: A new dataset for off-the-person ECG biometrics. Comput. Methods Programs Biomed. 2014;113(2):503–514. doi: 10.1016/j.cmpb.2013.11.017. PubMed DOI

Taddei A, et al. The European ST-T database: Standard for evaluating systems for the analysis of ST-T changes in ambulatory electrocardiography. Eur. Heart J. 1992;13:9. doi: 10.1093/oxfordjournals.eurheartj.a060332. PubMed DOI

Goldberger AL, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation. 2000;101(23):E215–E220. doi: 10.1161/01.CIR.101.23.e215. PubMed DOI

Kalyakulina, A.I. et al., "LU electrocardiography database: A new open-access validation tool for delineation algorithms," arXiv. 2018.

Laguna P, Mark RG, Goldberg A, Moody GB. Database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG. Comput. Cardiol. 1997;2:5.

Greenwald SD, Patil RS, Mark RG. Improved detection and classification of arrhythmias in noise-corrupted electrocardiograms using contextual information within an expert system. Biomed. Instrum. Technol. 1992;26:2. PubMed

Moody GB. The physionet/computers in cardiology challenge 2008: T-wave Alternans. Comput. Cardiol. 2008;35:25. PubMed PMC

Silva I, Moody GB. An open-source toolbox for analysing and processing physionet databases in MATLAB and octave. J. Open Res. Softw. 2014;2:2. doi: 10.5334/jors.bi. PubMed DOI PMC

Paszke A, et al. PyTorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. 2019;32:2.

Automatically optimized vectorcardiographic features are associated with recurrence of atrial fibrillation after electrical cardioversion