Medical time series are sequential data collected over time that measures health-related
signals, such as electroencephalography (EEG), electrocardiography (ECG), and intensive …

Medical data refers to health-related information associated with regular patient care or as
part of a clinical trial program. There are many categories of such data, such as clinical …

Electrocardiogram (ECG) is a universal diagnostic tool for heart disease, which can provide
data for deep learning. The scarcity of labeled data is a major challenge for medical artificial …

Over the last decade, deep learning applications in biomedical research have exploded,
demonstrating their ability to often outperform previous machine learning approaches in …

The visual interpretation of electrocardiogram (ECG) data is driven by human pattern
recognition and requires in-depth medical knowledge. Although state-of-the-art deep …

深度学习因其强大的数据表征能力已被广泛应用于心电(ECG) 信号分析领域,
但有监督方法的训练过程需要大量标签, 而心电数据标注通常是耗时且成本高昂的 …

Cardiovascular diseases are a significant cause of mortality worldwide, and the accurate
diagnosis of these conditions is essential for effective treatment and management …

Self-supervised learning has emerged as a promising paradigm for enhancing the analysis
of physiological signals, particularly Electrocardiogram (ECG) and Photoplethysmogram …

Cardiovascular diseases are the primary cause of death globally. With the prevalence of
electrocardiogram (ECG) machines within and outside the clinical environment, it is now …

This thesis presents novel methods for two ECG-related tasks, improving diagnostic
efficiency and accuracy:(1) the selection of optimalsubsets of ECG leads using Deep …