Inter-patient ECG arrhythmia heartbeat classification based on unsupervised domain adaptation
Electrocardiography (ECG) arrhythmia heartbeat classification is essential for automatic
cardiovascular diagnosis system. However, the enormous differences of ECG signals …
cardiovascular diagnosis system. However, the enormous differences of ECG signals …
Online anomaly detection for long-term ECG monitoring using wearable devices
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 …
learned for each specific user. Unfortunately, a few algorithmic challenges are still to be …
Unsupervised domain adaptation for ECG arrhythmia classification
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 …
diagnosis in clinical practice. In recent years, various algorithms based on deep learning …
Matrix profile XVIII: time series mining in the face of fast moving streams using a learned approximate matrix profile
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 …
mining on large time series archives. By efficiently computing all of the" essential" distance …
[HTML][HTML] SF-ECG: Source-free intersubject domain adaptation for electrocardiography-based arrhythmia classification
Electrocardiography (ECG)-based arrhythmia classification intends to have a massive role in
cardiovascular disease monitoring and early diagnosis. However, ECG datasets are mostly …
cardiovascular disease monitoring and early diagnosis. However, ECG datasets are mostly …
[HTML][HTML] Semi-Supervised Domain Adaptation for Individual Identification from Electrocardiogram Signals
When acquiring electrocardiogram (ECG) signals, the placement of electrode patches is
crucial for acquiring electrocardiographic signals. Constant displacement positions are …
crucial for acquiring electrocardiographic signals. Constant displacement positions are …
A new method for dictionary matrix optimization in ECG compressed sensing
E Picariello, E Balestrieri, F Picariello… - 2020 IEEE …, 2020 - ieeexplore.ieee.org
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 …
improving the reconstruction quality of ECG signals delivered by a Compressed Sensing …
[PDF][PDF] A Wearable Device for Online and Long-Term ECG Monitoring.
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 …
ECG signals, and automaticallydetect anomalous heartbeats such as arrhythmias. Our …
Event Detection in Optical Signals via Domain Adaptation
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 …
optical signals. Unfortunately, event detection models poorly generalize when monitoring …
[PDF][PDF] Learning and adaptation to detect changes and anomalies in high-dimensional data
D Carrera - Special Topics in Information Technology, 2020 - library.oapen.org
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 …
changes from normal to novel and possibly anomalous conditions has relevant applications …