[HTML][HTML] A review on deep learning methods for ECG arrhythmia classification
Deep Learning (DL) has recently become a topic of study in different applications including
healthcare, in which timely detection of anomalies on Electrocardiogram (ECG) can play a …
healthcare, in which timely detection of anomalies on Electrocardiogram (ECG) can play a …
Application of deep learning techniques for heartbeats detection using ECG signals-analysis and review
Deep learning models have become a popular mode to classify electrocardiogram (ECG)
data. Investigators have used a variety of deep learning techniques for this application …
data. Investigators have used a variety of deep learning techniques for this application …
[HTML][HTML] Multi-class arrhythmia detection from 12-lead varied-length ECG using attention-based time-incremental convolutional neural network
Automatic arrhythmia detection from Electrocardiogram (ECG) plays an important role in
early prevention and diagnosis of cardiovascular diseases. Convolutional neural network …
early prevention and diagnosis of cardiovascular diseases. Convolutional neural network …
LSTM-based ECG classification for continuous monitoring on personal wearable devices
S Saadatnejad, M Oveisi… - IEEE journal of biomedical …, 2019 - ieeexplore.ieee.org
Objective: A novel electrocardiogram (ECG) classification algorithm is proposed for
continuous cardiac monitoring on wearable devices with limited processing capacity …
continuous cardiac monitoring on wearable devices with limited processing capacity …
Digital twin empowered wireless healthcare monitoring for smart home
The dramatic progresses of wireless technologies and wearable devices have significantly
promoted the development and popularity of smart home, while digital twin (DT) emerges as …
promoted the development and popularity of smart home, while digital twin (DT) emerges as …
Feature-level fusion approaches based on multimodal EEG data for depression recognition
This study aimed to construct a novel multimodal model by fusing different
electroencephalogram (EEG) data sources, which were under neutral, negative and positive …
electroencephalogram (EEG) data sources, which were under neutral, negative and positive …
Integration of artificial intelligence, blockchain, and wearable technology for chronic disease management: a new paradigm in smart healthcare
Chronic diseases are a growing concern worldwide, with nearly 25% of adults suffering from
one or more chronic health conditions, thus placing a heavy burden on individuals, families …
one or more chronic health conditions, thus placing a heavy burden on individuals, families …
Automatic cardiac arrhythmia classification using combination of deep residual network and bidirectional LSTM
Cardiac arrhythmia is associated with abnormal electrical activities of the heart, which can
be reflected by altered characteristics of electrocardiogram (ECG). Due to the simplicity and …
be reflected by altered characteristics of electrocardiogram (ECG). Due to the simplicity and …
ECG-based multi-class arrhythmia detection using spatio-temporal attention-based convolutional recurrent neural network
Automatic arrhythmia detection based on electrocardiogram (ECG) is of great significance
for early prevention and diagnosis of cardiac diseases. Recently, deep learning methods …
for early prevention and diagnosis of cardiac diseases. Recently, deep learning methods …
ECG heartbeat classification using multimodal fusion
Electrocardiogram (ECG) is an authoritative source to diagnose and counter critical
cardiovascular syndromes such as arrhythmia and myocardial infarction (MI). Current …
cardiovascular syndromes such as arrhythmia and myocardial infarction (MI). Current …