A survey: From shallow to deep machine learning approaches for blood pressure estimation using biosensors
Over the past two decades, machine learning systems have been proliferating in the
healthcare industry domains, such as digital health, fitness tracking, patient monitoring, and …
healthcare industry domains, such as digital health, fitness tracking, patient monitoring, and …
[HTML][HTML] Computational diagnostic techniques for electrocardiogram signal analysis
Cardiovascular diseases (CVDs), including asymptomatic myocardial ischemia, angina,
myocardial infarction, and ischemic heart failure, are the leading cause of death globally …
myocardial infarction, and ischemic heart failure, are the leading cause of death globally …
Fnet: Mixing tokens with fourier transforms
We show that Transformer encoder architectures can be sped up, with limited accuracy
costs, by replacing the self-attention sublayers with simple linear transformations that" mix" …
costs, by replacing the self-attention sublayers with simple linear transformations that" mix" …
Reservoir computing with biocompatible organic electrochemical networks for brain-inspired biosignal classification
Early detection of malign patterns in patients' biological signals can save millions of lives.
Despite the steady improvement of artificial intelligence–based techniques, the practical …
Despite the steady improvement of artificial intelligence–based techniques, the practical …
[HTML][HTML] Optimal multi-stage arrhythmia classification approach
Arrhythmia constitutes a problem with the rate or rhythm of the heartbeat, and an early
diagnosis is essential for the timely inception of successful treatment. We have jointly …
diagnosis is essential for the timely inception of successful treatment. We have jointly …
Fcnn: Fourier convolutional neural networks
The Fourier domain is used in computer vision and machine learning as image analysis
tasks in the Fourier domain are analogous to spatial domain methods but are achieved …
tasks in the Fourier domain are analogous to spatial domain methods but are achieved …
A novel technique for cardiac arrhythmia classification using spectral correlation and support vector machines
Cardiac disorders are one of the main causes leading to death. Therefore, they require
continuous and efficient detection techniques. ECG is one of the main tools to diagnose …
continuous and efficient detection techniques. ECG is one of the main tools to diagnose …
[HTML][HTML] Effect of multiscale PCA de-noising in ECG beat classification for diagnosis of cardiovascular diseases
E Alickovic, A Subasi - Circuits, Systems, and Signal Processing, 2015 - Springer
Current trends in clinical applications demand automation in electrocardiogram (ECG)
signal processing and heart beat classification. This paper examines the design of an …
signal processing and heart beat classification. This paper examines the design of an …
Time adaptive ECG driven cardiovascular disease detector
Electrocardiograms (ECGs) are widely used to detect cardiovascular disease (CVD). Deep
learning is a topic of interest in healthcare, in which timely detection of ECG anomalies can …
learning is a topic of interest in healthcare, in which timely detection of ECG anomalies can …
On handling negative transfer and imbalanced distributions in multiple source transfer learning
Transfer learning has benefited many real‐world applications where labeled data are
abundant in source domains but scarce in the target domain. As there are usually multiple …
abundant in source domains but scarce in the target domain. As there are usually multiple …