Electrocardiography (ECG) arrhythmia heartbeat classification is essential for automatic
cardiovascular diagnosis system. However, the enormous differences of ECG signals …

Many successful algorithms for analyzing ECG signals leverage data-driven models that are
learned for each specific user. Unfortunately, a few algorithmic challenges are still to be …

Electrocardiograph (ECG) is one of the most critical physiological signals for arrhythmia
diagnosis in clinical practice. In recent years, various algorithms based on deep learning …

In recent years, the Matrix Profile has emerged as a promising approach to allow data
mining on large time series archives. By efficiently computing all of the" essential" distance …

Electrocardiography (ECG)-based arrhythmia classification intends to have a massive role in
cardiovascular disease monitoring and early diagnosis. However, ECG datasets are mostly …

When acquiring electrocardiogram (ECG) signals, the placement of electrode patches is
crucial for acquiring electrocardiographic signals. Constant displacement positions are …

This paper proposes a new method for dictionary matrix optimization with the aim of
improving the reconstruction quality of ECG signals delivered by a Compressed Sensing …

We present a prototype wearable device able to perform online and long-term monitoring of
ECG signals, and automaticallydetect anomalous heartbeats such as arrhythmias. Our …

Data-driven models trained in an end-to-end manner can reliably detect events within
optical signals. Unfortunately, event detection models poorly generalize when monitoring …

The problem of monitoring a datastream and detecting whether the data generating process
changes from normal to novel and possibly anomalous conditions has relevant applications …