With the increasing usage of wearable electrocardiogram (ECG) monitoring devices, it is
necessary to develop models and algorithms that can analyze the large amounts of ECG …

To detect and identify QRS complexes and R-peak is one of the crucial steps in the field of
electrocardiogram (ECG) signals research, and their detection accuracy directly affects the …

Rate-corrected QT interval (QTc) prolongation has been suggested as a biomarker for the
risk of drug-induced torsades de pointes, and is therefore monitored during clinical trials for …

A generative adversarial network (GAN) based fully automatic electrocardiogram (ECG)
arrhythmia classification system with high performance is presented in this paper. The …

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 …

Wearable ECG devices encounter significant challenges in replicating the diagnostic
capabilities of standard 12-lead ECGs, primarily due to the complexity of electrode …

The interpretation of electrocardiograms (ECGs) is key for the diagnosis and monitoring of
cardiovascular health. Despite the progressive digital transformation in healthcare, it is still …

Binarized convolutional neural networks (bCNNs) are favored for the design of low-storage,
low-power cardiac arrhythmia classifiers owing to their high weight compression rate …

Drug safety trials require substantial ECG labelling like, in thorough QT studies,
measurements of the QT interval, whose prolongation is a biomarker of proarrhythmic risk …

ECG delineation is crucial for assessing drug-induced proarrhythmic risks, but still heavily
depends on expert cardiologists despite existing automated methods. Our study proposes a …