Deep Regression Network With Sequential Constraint for Wearable ECG Characteristic Point Location

Accurate location of characteristic points in wearable ECG signals may be a challenge due to the high noise. Taking the time sequence of waveforms and missing waveforms into account, we design a location regression network ECG_SCRNet, combined with the sequential constraints to accurately identify characteristic points of wearable ECGs. We add a classification head to determine whether there is a P-wave or a T-wave missing. This architecture ensures that the network considers both the time sequence of physiological waveform and class information to improve the accuracy in locating characteristic points. The proposed ECG_SCRNet was evaluated on a wearable dataset and the LUDB, achieving highly accurate results compared to other state-of-the-art methods. On the wearable dataset, the average
Sen, PPV
and
F1
score are 97.13%, 99.96%, and 99.51%. On the LUDB, the average
Sen, PPV
and
F1
score are 96.86%, 99.83%, and 98.97%. These results demonstrate that the proposed ECG_SCRNet has good flexibility and reliability when applied to signal characteristic point detection, and it is a reliable method for analyzing ECG signals in real time.