How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management
There has been an exponential growth of artificial intelligence (AI) and machine learning
(ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has …
(ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has …
Recent advances in heart sound analysis
Heart sounds have been widely studied and have been demonstrated to have value for
detecting pathologies in clinical applications. Over the last few decades, the use of heart …
detecting pathologies in clinical applications. Over the last few decades, the use of heart …
Assessment of compressed and decompressed ECG databases for telecardiology applying a convolution neural network
Incalculable numbers of patients in hospitals as a result of COVID-19 made the screening of
heart patients arduous. Patients who need regular heart monitoring were affected the most …
heart patients arduous. Patients who need regular heart monitoring were affected the most …
Classification of short single-lead electrocardiograms (ECGs) for atrial fibrillation detection using piecewise linear spline and XGBoost
Objective: Detection of atrial fibrillation is important for risk stratification of stroke. We
developed a novel methodology to classify electrocardiograms (ECGs) to normal, atrial …
developed a novel methodology to classify electrocardiograms (ECGs) to normal, atrial …
Quality guaranteed ECG signal compression using tunable-Q wavelet transform and Möbius transform-based AFD
Long-term electrocardiogram (ECG) signal monitoring necessitates a large amount of
memory space for storage, which affects the transmission channel efficiency during real-time …
memory space for storage, which affects the transmission channel efficiency during real-time …
AF detection from ECG recordings using feature selection, sparse coding, and ensemble learning
M Rizwan, BM Whitaker… - Physiological …, 2018 - iopscience.iop.org
Objective: The objective of this paper is to provide an algorithm for accurate, automated
detection of atrial fibrillation (AF) from ECG signals. Four types of ECG signals are …
detection of atrial fibrillation (AF) from ECG signals. Four types of ECG signals are …
TP-CNN: A Detection Method for atrial fibrillation based on transposed projection signals with compressed sensed ECG
H Zhang, Z Dong, M Sun, H Gu, Z Wang - Computer Methods and Programs …, 2021 - Elsevier
Abstract Background and Objective: Atrial fibrillation (AF) is the most prevalent arrhythmia,
which increases the mortality of several complications. The use of wearable devices to …
which increases the mortality of several complications. The use of wearable devices to …
Detection of atrial fibrillation in compressively sensed electrocardiogram measurements
Atrial fibrillation (AF) is a serious cardiovascular condition that can lead to complications,
including but not limited to stroke, heart attack, and death. AF can be diagnosed using an …
including but not limited to stroke, heart attack, and death. AF can be diagnosed using an …
Preserving abnormal beat morphology in long-term ECG recording: An efficient hybrid compression approach
In long-term electrocardiogram (ECG) recording for arrhythmia monitoring, using a uniform
compression strategy throughout the entire data to achieve high compression efficiency may …
compression strategy throughout the entire data to achieve high compression efficiency may …
Automatic screening method for atrial fibrillation based on lossy compression of the electrocardiogram signal
H Zhang, Z Dong, J Gao, P Lu… - Physiological …, 2020 - iopscience.iop.org
Objective: Compressed sensing (CS) is a low-complexity compression technology that has
recently been proposed. It can be applied to long-term electrocardiogram (ECG) monitoring …
recently been proposed. It can be applied to long-term electrocardiogram (ECG) monitoring …