Accurate delineation of key waveforms in an ECG is a critical initial step in
extracting relevant features to support the diagnosis and treatment of heart
conditions. Although deep learning based methods using a segmentation model to
locate P, QRS and T waves have shown promising results, their ability to handle
signals exhibiting arrhythmia remains unclear. In this study, we propose a
novel approach that leverages a deep learning model to accurately delineate
signals with a wide range of arrhythmia. Our approach involves training a
segmentation model using a hybrid loss function that combines segmentation with
the task of arrhythmia classification. In addition, we use a diverse training
set containing various arrhythmia types, enabling our model to handle a wide
range of challenging cases. Experimental results show that our model accurately
delineates signals with a broad range of abnormal rhythm types, and the
combined training with classification guidance can effectively reduce false
positive P wave predictions, particularly during atrial fibrillation and atrial
flutter. Furthermore, our proposed method shows competitive performance with
previous delineation algorithms on the Lobachevsky University Database (LUDB)