A disentangled VAE-BiLSTM model for heart rate anomaly detection
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 …
American Heart Association, approximately 19.1 million deaths were attributed to CVDs in …
Healthcare and anomaly detection: using machine learning to predict anomalies in heart rate data
E Šabić, D Keeley, B Henderson, S Nannemann - AI & SOCIETY, 2021 - Springer
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 …
while also reducing healthcare worker cognitive load. These algorithms can be used to …
Unsupervised transformer-based anomaly detection in ECG signals
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 …
problems in healthcare sensory data. Technology has made it easier to collect large and …
Learning representations from healthcare time series data for unsupervised anomaly detection
J Pereira, M Silveira - … international conference on big data and …, 2019 - ieeexplore.ieee.org
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 …
need for methods that can analyse these data, detect anomalies and provide meaningful …
[PDF][PDF] Robust Anomaly Detection in Time Series through Variational AutoEncoders and a Local Similarity Score.
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 …
analysis regarding several tasks, including anomaly detection. However, even though the …
Unsupervised anomaly detection in energy time series data using variational recurrent autoencoders with attention
J Pereira, M Silveira - 2018 17th IEEE international conference …, 2018 - ieeexplore.ieee.org
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 …
field, smart grids are enabling a unprecedented data acquisition with the integration of …
A deep learning approach for anomaly detection and prediction in power consumption data
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 …
allows them to detect unexpected power consumption values, identify unusual behaviors …
Multivariate time-series anomaly detection with contaminated data: Application to physiological signals
TKK Ho, N Armanfard - arXiv preprint arXiv:2308.12563, 2023 - arxiv.org
Mainstream unsupervised anomaly detection algorithms often excel in academic datasets,
yet their real-world performance is restricted due to the controlled experimental conditions …
yet their real-world performance is restricted due to the controlled experimental conditions …
[HTML][HTML] Personalized anomaly detection in PPG data using representation learning and biometric identification
Photoplethysmography (PPG) signals, typically acquired from wearable devices, hold
significant potential for continuous fitness-health monitoring. In particular, heart conditions …
significant potential for continuous fitness-health monitoring. In particular, heart conditions …
ARGUS: Interactive visual analysis of disruptions in smartphone-detected Bio-Behavioral Rhythms
Abstract Human Bio-Behavioral Rhythms (HBRs) such as sleep-wake cycles (Circadian
Rhythms), and the degree of regularity of sleep and physical activity have important health …
Rhythms), and the degree of regularity of sleep and physical activity have important health …