Cardiovascular diseases (CVDs) remain a leading cause of death globally. According to the
American Heart Association, approximately 19.1 million deaths were attributed to CVDs in …

Today, accurate and automated abnormality diagnosis and identification have become of
paramount importance as they are involved in many critical and life-saving scenarios. To …

The application of machine learning algorithms to healthcare data can enhance patient care
while also reducing healthcare worker cognitive load. These algorithms can be used to …

Anomaly detection is one of the basic issues in data processing that addresses different
problems in healthcare sensory data. Technology has made it easier to collect large and …

Anomaly detection in time series is a well-studied subject, and it is well-documented in the
literature such as ECG signal. Many successful algorithms for analyzing ECG signals are …

The amount of time series data generated in Healthcare is growing very fast and so is the
need for methods that can analyse these data, detect anomalies and provide meaningful …

The rise of time series data availability has demanded new techniques for its automated
analysis regarding several tasks, including anomaly detection. However, even though the …

In the age of big data, time series are being generated in massive amounts. In the energy
field, smart grids are enabling a unprecedented data acquisition with the integration of …

Anomaly detection in power consumption data can be very useful to building managers. It
allows them to detect unexpected power consumption values, identify unusual behaviors …

Mainstream unsupervised anomaly detection algorithms often excel in academic datasets,
yet their real-world performance is restricted due to the controlled experimental conditions …