Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: A review
The brain–computer interface (BCI) is an emerging technology that has the potential to
revolutionize the world, with numerous applications ranging from healthcare to human …
revolutionize the world, with numerous applications ranging from healthcare to human …
The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances
Abstract Time Series Classification (TSC) involves building predictive models for a discrete
target variable from ordered, real valued, attributes. Over recent years, a new set of TSC …
target variable from ordered, real valued, attributes. Over recent years, a new set of TSC …
MIMIC-IV, a freely accessible electronic health record dataset
AEW Johnson, L Bulgarelli, L Shen, A Gayles… - Scientific data, 2023 - nature.com
Digital data collection during routine clinical practice is now ubiquitous within hospitals. The
data contains valuable information on the care of patients and their response to treatments …
data contains valuable information on the care of patients and their response to treatments …
Anomaly detection in time series: a comprehensive evaluation
S Schmidl, P Wenig, T Papenbrock - Proceedings of the VLDB …, 2022 - dl.acm.org
Detecting anomalous subsequences in time series data is an important task in areas
ranging from manufacturing processes over finance applications to health care monitoring …
ranging from manufacturing processes over finance applications to health care monitoring …
Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations
Artificial intelligence (AI) systems have increasingly achieved expert-level performance in
medical imaging applications. However, there is growing concern that such AI systems may …
medical imaging applications. However, there is growing concern that such AI systems may …
Tranad: Deep transformer networks for anomaly detection in multivariate time series data
Efficient anomaly detection and diagnosis in multivariate time-series data is of great
importance for modern industrial applications. However, building a system that is able to …
importance for modern industrial applications. However, building a system that is able to …
Towards a general-purpose foundation model for computational pathology
Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks,
requiring the objective characterization of histopathological entities from whole-slide images …
requiring the objective characterization of histopathological entities from whole-slide images …
Making the most of text semantics to improve biomedical vision–language processing
Multi-modal data abounds in biomedicine, such as radiology images and reports.
Interpreting this data at scale is essential for improving clinical care and accelerating clinical …
Interpreting this data at scale is essential for improving clinical care and accelerating clinical …
An attention-based deep learning approach for sleep stage classification with single-channel EEG
Automatic sleep stage mymargin classification is of great importance to measure sleep
quality. In this paper, we propose a novel attention-based deep learning architecture called …
quality. In this paper, we propose a novel attention-based deep learning architecture called …
Reconfigurable perovskite nickelate electronics for artificial intelligence
Reconfigurable devices offer the ability to program electronic circuits on demand. In this
work, we demonstrated on-demand creation of artificial neurons, synapses, and memory …
work, we demonstrated on-demand creation of artificial neurons, synapses, and memory …