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Electrocardiogram (ECG) detection and delineation are key steps for numeroustasks in clinical practice, as ECG is the most performed non-invasive test forassessing cardiac condition. State-of-the-art algorithms employ digital signalprocessing (DSP), which require laborious rule adaptation to new morphologies.In contrast, deep learning (DL) algorithms, especially for classification, aregaining weight in academic and industrial settings. However, the lack of modelexplainability and small databases hinder their applicability. We demonstrateDL can be successfully applied to low interpretative tasks by embedding ECGdetection and delineation onto a segmentation framework. For this purpose, weadapted and validated the most used neural network architecture for imagesegmentation, the U-Net, to one-dimensional data. The model was trained usingPhysioNet's QT database, comprised of 105 ambulatory ECG recordings, forsingle- and multi-lead scenarios. To alleviate data scarcity, dataregularization techniques such as pre-training with low-quality data labels,performing ECG-based data augmentation and applying strong model regularizersto the architecture were attempted. Other variations in the model's capacity(U-Net's depth and width), alongside the application of state-of-the-artadditions, were evaluated. These variations were exhaustively validated in a5-fold cross-validation manner. The best performing configuration reachedprecisions of 90.12%, 99.14% and 98.25% and recalls of 98.73%, 99.94% and99.88% for the P, QRS and T waves, respectively, on par with DSP-basedapproaches. Despite being a data-hungry technique trained on a small dataset,DL-based approaches demonstrate to be a viable alternative to traditionalDSP-based ECG processing techniques.

ECG-DelNet: Delineation of Ambulatory Electrocardiograms with Mixed Quality Labeling Using Neural Networks