To achieve optimal performance in practical applications, the electrocardiogram (ECG)
diagnosis models have to be personalized using the ECG data of specific patients. Most …

Integrating artificial intelligence (AI) into biomedical signal analysis represents a significant
breakthrough in enhanced precision and efficiency of disease diagnostics and therapeutics …

A substantial amount of variability in ECG manifested due to patient characteristics hinders
the adoption of automated analysis algorithms in clinical practice. None of the ECG …

The multi-lead electrocardiogram (ECG) is widely utilized in clinical diagnosis and
monitoring of cardiac conditions. The advancement of deep learning has led to the …

With the advancement in digital communication and artificial intelligence-based
applications, the emphasis on information security has intensified. Traditional authentication …

Electrocardiograms can reveal irregular cardiac cycles, ie, arrhythmia and detecting
arrhythmia from its morphology is challenging. This article proposes a novel approach for …

Random telegraph signals (RTSs) are specific time-fluctuating signal patterns marked by a
series of distinctive switching events between well-defined signal levels. These signals are …

Detecting anomalies in electrocardiogram data is crucial to identifying deviations from
normal heartbeat patterns and providing timely intervention to at-risk patients. Various …

With the development of Internet of Things (IoT) technology, a large amount of data with
temporal characteristics is collected and stored. How to efficiently and accurately identify …

Coronary artery disease (CAD) is an irreversible and fatal disease. It necessitates timely and
precise diagnosis to slow CAD progression. Electrocardiogram (ECG) and …