Electrocardiogram Comparison as a Biometric Identifier: A Review
A Kadi, A Oubelaid, SK Towfek - Full Length Article, 2023 - americaspg.com
The electrocardiogram (ECG) is a type of biometric data that has recently attracted a lot of
attention as a potentially useful biometric trait due to its high discriminatory power. However …
attention as a potentially useful biometric trait due to its high discriminatory power. However …
A comprehensive survey on ECG signals as new biometric modality for human authentication: Recent advances and future challenges
AN Uwaechia, DA Ramli - IEEE Access, 2021 - ieeexplore.ieee.org
Electrocardiogram (ECG) has extremely discriminative characteristics in the biometric field
and has recently received significant interest as a promising biometric trait. However, ECG …
and has recently received significant interest as a promising biometric trait. However, ECG …
Stationary wavelet transform based ECG signal denoising method
Electrocardiogram (ECG) signals are used to diagnose cardiovascular diseases. During
ECG signal acquisition, various noises like power line interference, baseline wandering …
ECG signal acquisition, various noises like power line interference, baseline wandering …
Arrhythmia classification algorithm based on multi-head self-attention mechanism
Cardiovascular disease is a major illness that causes human death, especially in the elderly.
Timely and accurate diagnosis of arrhythmia types is the key to early prevention and …
Timely and accurate diagnosis of arrhythmia types is the key to early prevention and …
[HTML][HTML] Self-supervised representation learning from 12-lead ECG data
T Mehari, N Strodthoff - Computers in biology and medicine, 2022 - Elsevier
Abstract Clinical 12-lead electrocardiography (ECG) is one of the most widely encountered
kinds of biosignals. Despite the increased availability of public ECG datasets, label scarcity …
kinds of biosignals. Despite the increased availability of public ECG datasets, label scarcity …
Enhancing dynamic ECG heartbeat classification with lightweight transformer model
Arrhythmia is a common class of Cardiovascular disease which is the cause for over 31% of
all death over the world, according to WHOs' report. Automatic detection and classification of …
all death over the world, according to WHOs' report. Automatic detection and classification of …
Automatic cardiac arrhythmia classification based on hybrid 1-D CNN and Bi-LSTM model
Cardiovascular diseases (CVDs) are a group of heart and blood vessel ailments that can
cause chest pain and trouble breathing, especially while active. However, some patients …
cause chest pain and trouble breathing, especially while active. However, some patients …
A Comparative Study of ECG-derived Respiration in Ambulatory Monitoring using the Single-lead ECG
Cardiorespiratory monitoring is crucial for the diagnosis and management of multiple
conditions such as stress and sleep disorders. Therefore, the development of ambulatory …
conditions such as stress and sleep disorders. Therefore, the development of ambulatory …
Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time series
Detecting anomalies in time series data is important in a variety of fields, including system
monitoring, healthcare and cybersecurity. While the abundance of available methods makes …
monitoring, healthcare and cybersecurity. While the abundance of available methods makes …
[HTML][HTML] Deep learning for comprehensive ECG annotation
BA Teplitzky, M McRoberts, H Ghanbari - Heart rhythm, 2020 - Elsevier
Background Increasing utilization of long-term outpatient ambulatory electrocardiographic
(ECG) monitoring continues to drive the need for improved ECG interpretation algorithms …
(ECG) monitoring continues to drive the need for improved ECG interpretation algorithms …