A review of arrhythmia detection based on electrocardiogram with artificial intelligence

J Liu, Z Li, Y Jin, Y Liu, C Liu, L Zhao… - Expert review of medical …, 2022 - Taylor & Francis
Introduction With the widespread availability of portable electrocardiogram (ECG) devices,
there will be a surge in ECG diagnoses. Traditional computer-aided diagnosis of arrhythmia …

A novel attentional deep neural network-based assessment method for ECG quality

Y Jin, Z Li, C Qin, J Liu, Y Liu, L Zhao, C Liu - Biomedical Signal Processing …, 2023 - Elsevier
ECG quality assessment is of great significance to reduce false alarms in automatic
arrhythmia and other cardiovascular diseases diagnoses and reduce the workload of …

A review on atrial fibrillation detection from ambulatory ECG

C Ma, Z Xiao, L Zhao, S Biton, JA Behar… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Atrial fibrillation (AF) is a prevalent clinical arrhythmia disease and is an important cause of
stroke, heart failure, and sudden death. Due to the insidious onset and no obvious clinical …

ECG quality assessment based on hand-crafted statistics and deep-learned S-transform spectrogram features

G Liu, X Han, L Tian, W Zhou, H Liu - Computer Methods and Programs in …, 2021 - Elsevier
Abstract Background and Objective Electrocardiogram (ECG) quality assessment is
significant for automatic diagnosis of cardiovascular disease and reducing the massive …

A deformable CNN architecture for predicting clinical acceptability of ECG signal

JP Allam, S Samantray, SP Sahoo, S Ari - Biocybernetics and Biomedical …, 2023 - Elsevier
The degraded quality of the electrocardiogram (ECG) signals is the main source of false
alarms in critical care units. Therefore, a preliminary analysis of the ECG signal is required to …

A deep learning approach for featureless robust quality assessment of intermittent atrial fibrillation recordings from portable and wearable devices

ÁH Herraiz, A Martínez-Rodrigo, V Bertomeu-González… - Entropy, 2020 - mdpi.com
Atrial fibrillation (AF) is the most common heart rhythm disturbance in clinical practice. It
often starts with asymptomatic and very short episodes, which are extremely difficult to detect …

Neural architecture search for real-time quality assessment of wearable multi-lead ECG on mobile devices

H Tan, J Lai, Y Liu, Y Song, J Wang, M Chen… - … Signal Processing and …, 2022 - Elsevier
Users need to pay for the medical services of the wearable ECG signals. The complicated
interferences of wearable ECG may cause invalid medical diagnosis. To save medical …

Electrocardiogram quality assessment with a generalized deep learning model assisted by conditional generative adversarial networks

X Zhou, X Zhu, K Nakamura, M Noro - Life, 2021 - mdpi.com
The electrocardiogram (ECG) is widely used for cardiovascular disease diagnosis and daily
health monitoring. Before ECG analysis, ECG quality screening is an essential but time …

Deep learning-based signal quality assessment for wearable ECGs

X Zhang, J Li, Z Cai, L Zhao… - IEEE Instrumentation & …, 2022 - ieeexplore.ieee.org
Nowadays, use of the dynamic electrocardiogram (ECG) has developed rapidly because of
the wide application of wearable devices [1]–[3]. Most ECG-based diagnostic algorithms …

FGSQA-Net: A weakly supervised approach to fine-grained electrocardiogram signal quality assessment

H Liu, T Gao, Z Liu, M Shu - IEEE Journal of Biomedical and …, 2023 - ieeexplore.ieee.org
Objective: Due to the lack of fine-grained labels, current research can only evaluate the
signal quality at a coarse scale. This article proposes a weakly supervised fine-grained …