Key result
The proposed deep-learning method achieved an F1 score of 0.99 for QRS detection on the private test set and a mean F1 score of 0.96 across twelve external public databases in one inference step.
Why the study?
Holter ECG data from wearable devices during daily activities present challenges like increased noise and motion artefacts for QRS detection and classification.
A novel deep-learning model can reliably perform QRS detection and classification in a single inference step, outperforming existing methods on noisy Holter ECG data.
Single-step DL QRS detection may aid noisy wearable Holter analysis; leaves open external validation before clinical use.
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.
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Ivora et al. (2022) studied Arrhythmia (n=2,398). Deep-learning model (ResNet + GRU) vs. Existing QRS detection algorithms was evaluated on F1 score for QRS detection on the private test set. The proposed deep-learning method achieved an F1 score of 0.99 for QRS detection on the private test set and a mean F1 score of 0.96 across twelve external public databases in one inference step.
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