Deep learning for ECG Arrhythmia detection and classification: an overview of progress for period 2017–2023
Cardiovascular diseases are a leading cause of mortality globally. Electrocardiography
(ECG) still represents the benchmark approach for identifying cardiac irregularities …
(ECG) still represents the benchmark approach for identifying cardiac irregularities …
Deep learning-based IoT system for remote monitoring and early detection of health issues in real-time
With an aging population and increased chronic diseases, remote health monitoring has
become critical to improving patient care and reducing healthcare costs. The Internet of …
become critical to improving patient care and reducing healthcare costs. The Internet of …
[HTML][HTML] RNN-LSTM: From applications to modeling techniques and beyond—Systematic review
Abstract Long Short-Term Memory (LSTM) is a popular Recurrent Neural Network (RNN)
algorithm known for its ability to effectively analyze and process sequential data with long …
algorithm known for its ability to effectively analyze and process sequential data with long …
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 …
Deep representation learning with sample generation and augmented attention module for imbalanced ECG classification
Developing an efficient heartbeat monitoring system has become a focal point in numerous
healthcare applications. Specifically, in the last few years, heartbeat classification for …
healthcare applications. Specifically, in the last few years, heartbeat classification for …
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 …
SeriesSleepNet: an EEG time series model with partial data augmentation for automatic sleep stage scoring
Introduction: We propose an automatic sleep stage scoring model, referred to as
SeriesSleepNet, based on convolutional neural network (CNN) and bidirectional long short …
SeriesSleepNet, based on convolutional neural network (CNN) and bidirectional long short …
DSCSSA: A classification framework for spatiotemporal features extraction of arrhythmia based on the Seq2Seq model with attention mechanism
X Peng, W Shu, C Pan, Z Ke, H Zhu… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
In the field of arrhythmia classification, classification accuracy has always been a research
hotspot. However, the noises of electrocardiogram (ECG) signals, the class imbalance of …
hotspot. However, the noises of electrocardiogram (ECG) signals, the class imbalance of …
Jamming Recognition of carrier-free UWB cognitive radar based on MANet
The satisfaction of various basic requirements of cognitive radar by ultra-wideband (UWB)
signals makes UWB cognitive radar attract extensive attention. The variety and large …
signals makes UWB cognitive radar attract extensive attention. The variety and large …
An automatic segmentation framework of quasi-periodic time series through graph structure
X Tang, D Zheng, GS Kebede, Z Li, X Li, C Lu, L Li… - Applied …, 2023 - Springer
The segmentation of quasi-periodic time series (QTS) is crucial for modeling analysis in
industrial and medical fields. However, it is challenging to automatically and effectively split …
industrial and medical fields. However, it is challenging to automatically and effectively split …