Deep learning-based ECG arrhythmia classification: A systematic review
Deep learning (DL) has been introduced in automatic heart-abnormality classification using
ECG signals, while its application in practical medical procedures is limited. A systematic …
ECG signals, while its application in practical medical procedures is limited. A systematic …
Wearable devices for remote monitoring of heart rate and heart rate variability—what we know and what is coming
N Alugubelli, H Abuissa, A Roka - Sensors, 2022 - mdpi.com
Heart rate at rest and exercise may predict cardiovascular risk. Heart rate variability is a
measure of variation in time between each heartbeat, representing the balance between the …
measure of variation in time between each heartbeat, representing the balance between the …
[HTML][HTML] State-of-the-art deep learning methods on electrocardiogram data: systematic review
Background Electrocardiogram (ECG) is one of the most common noninvasive diagnostic
tools that can provide useful information regarding a patient's health status. Deep learning …
tools that can provide useful information regarding a patient's health status. Deep learning …
Beyond supervised learning for pervasive healthcare
The integration of machine/deep learning and sensing technologies is transforming
healthcare and medical practice. However, inherent limitations in healthcare data, namely …
healthcare and medical practice. However, inherent limitations in healthcare data, namely …
An architecture-level analysis on deep learning models for low-impact computations
Deep neural networks (DNNs) have made significant achievements in a wide variety of
domains. For the deep learning tasks, multiple excellent hardware platforms provide efficient …
domains. For the deep learning tasks, multiple excellent hardware platforms provide efficient …
Label correlation embedding guided network for multi-label ECG arrhythmia diagnosis
In clinical practice, one patient may suffer from more than one arrhythmia simultaneously,
that is, one ECG record may be associated with multiple types of arrhythmias. In fact, there …
that is, one ECG record may be associated with multiple types of arrhythmias. In fact, there …
ECGTransForm: Empowering adaptive ECG arrhythmia classification framework with bidirectional transformer
H El-Ghaish, E Eldele - Biomedical Signal Processing and Control, 2024 - Elsevier
Cardiac arrhythmias, deviations from the normal rhythmic beating of the heart, are subtle yet
critical indicators of potential cardiac challenges. Efficiently diagnosing them requires …
critical indicators of potential cardiac challenges. Efficiently diagnosing them requires …
Analysis of a deep learning model for 12-lead ECG classification reveals learned features similar to diagnostic criteria
T Bender, JM Beinecke, D Krefting… - IEEE Journal of …, 2023 - ieeexplore.ieee.org
Despite their remarkable performance, deep neural networks remain unadopted in clinical
practice, which is considered to be partially due to their lack of explainability. In this work, we …
practice, which is considered to be partially due to their lack of explainability. In this work, we …
A novel imbalanced dataset mitigation method and ECG classification model based on combined 1D_CBAM-autoencoder and lightweight CNN model
To address the problems of the imbalanced datasets, limited computing power, and memory
capability of wearable devices, this paper proposes a new hybrid method for imbalanced …
capability of wearable devices, this paper proposes a new hybrid method for imbalanced …
The effects of Daubechies wavelet basis function (DWBF) and decomposition level on the performance of artificial intelligence-based atrial fibrillation (AF) detection …
Featured Application A potential application developed from the results obtained by this
study is a monitoring system of atrial fibrillation for stroke early warning in both healthy …
study is a monitoring system of atrial fibrillation for stroke early warning in both healthy …