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 …
Multi-disease prediction based on deep learning: a survey
S **e, Z Yu, Z Lv - Computer Modeling in Engineering & …, 2021 - ingentaconnect.com
In recent years, the development of artificial intelligence (AI) and the gradual beginning of
AI's research in the medical field have allowed people to see the excellent prospects of the …
AI's research in the medical field have allowed people to see the excellent prospects of the …
Actual bearing compound fault diagnosis based on active learning and decoupling attentional residual network
Existing deep learning methods commonly requires massive labeled data for compound
fault diagnosis, which is difficult and time-consuming to collect in the real application. This …
fault diagnosis, which is difficult and time-consuming to collect in the real application. This …
From ECG signals to images: a transformation based approach for deep learning
Provocative heart disease is related to ventricular arrhythmias (VA). Ventricular
tachyarrhythmia is an irregular and fast heart rhythm that emerges from inappropriate …
tachyarrhythmia is an irregular and fast heart rhythm that emerges from inappropriate …
Deep residual LSTM with domain-invariance for remaining useful life prediction across domains
Currently developed unsupervised domain adaptation (UDA) methods have somewhat
improved the prognostic performance of cross-domain RUL prediction, but only optimizing …
improved the prognostic performance of cross-domain RUL prediction, but only optimizing …
A novel attentional deep neural network-based assessment method for ECG quality
ECG quality assessment is of great significance to reduce false alarms in automatic
arrhythmia and other cardiovascular diseases diagnoses and reduce the workload of …
arrhythmia and other cardiovascular diseases diagnoses and reduce the workload of …
A multi-scale convolutional neural network for bearing compound fault diagnosis under various noise conditions
Recently, with the urgent demand for data-driven approaches in practical industrial
scenarios, the deep learning diagnosis model in noise environments has attracted …
scenarios, the deep learning diagnosis model in noise environments has attracted …
DTCNNMI: A deep twin convolutional neural networks with multi-domain inputs for strongly noisy diesel engine misfire detection
Although machine learning-based intelligent detection methods have made many
achievements for diesel engine misfire diagnosis, they suffer from a certain degree of …
achievements for diesel engine misfire diagnosis, they suffer from a certain degree of …
A review on atrial fibrillation detection from ambulatory ECG
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 …
stroke, heart failure, and sudden death. Due to the insidious onset and no obvious clinical …
A novel unsupervised domain adaptation framework based on graph convolutional network and multi-level feature alignment for inter-subject ECG classification
Electrocardiogram (ECG) is an effective non-invasive tool that can detect arrhythmias.
Recently, deep learning (DL) has been widely used in ECG classification algorithms …
Recently, deep learning (DL) has been widely used in ECG classification algorithms …