Automatic sleep staging of EEG signals: recent development, challenges, and future directions
H Phan, K Mikkelsen - Physiological Measurement, 2022 - iopscience.iop.org
Modern deep learning holds a great potential to transform clinical studies of human sleep.
Teaching a machine to carry out routine tasks would be a tremendous reduction in workload …
Teaching a machine to carry out routine tasks would be a tremendous reduction in workload …
A review of feature reduction techniques in neuroimaging
B Mwangi, TS Tian, JC Soares - Neuroinformatics, 2014 - Springer
Abstract Machine learning techniques are increasingly being used in making relevant
predictions and inferences on individual subjects neuroimaging scan data. Previous studies …
predictions and inferences on individual subjects neuroimaging scan data. Previous studies …
On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Understanding and interpreting classification decisions of automated image classification
systems is of high value in many applications, as it allows to verify the reasoning of the …
systems is of high value in many applications, as it allows to verify the reasoning of the …
Multivariate lesion‐symptom mapping using support vector regression
Y Zhang, DY Kimberg, HB Coslett… - Human brain …, 2014 - Wiley Online Library
Lesion analysis is a classic approach to study brain functions. Because brain function is a
result of coherent activations of a collection of functionally related voxels, lesion‐symptom …
result of coherent activations of a collection of functionally related voxels, lesion‐symptom …
Deep convolutional neural networks for interpretable analysis of EEG sleep stage scoring
A Vilamala, KH Madsen… - 2017 IEEE 27th …, 2017 - ieeexplore.ieee.org
Sleep studies are important for diagnosing sleep disorders such as insomnia, narcolepsy or
sleep apnea. They rely on manual scoring of sleep stages from raw polisomnography …
sleep apnea. They rely on manual scoring of sleep stages from raw polisomnography …
[图书][B] Statistical analysis of fMRI data
FG Ashby - 2019 - books.google.com
A guide to all aspects of experimental design and data analysis for fMRI experiments,
completely revised and updated for the second edition. Functional magnetic resonance …
completely revised and updated for the second edition. Functional magnetic resonance …
[HTML][HTML] Patient similarity networks for precision medicine
Clinical research and practice in the 21st century is poised to be transformed by analysis of
computable electronic medical records and population-level genome-scale patient profiles …
computable electronic medical records and population-level genome-scale patient profiles …
Metabolomics and multi-omics integration: a survey of computational methods and resources
As researchers are increasingly able to collect data on a large scale from multiple clinical
and omics modalities, multi-omics integration is becoming a critical component of …
and omics modalities, multi-omics integration is becoming a critical component of …
Multimodal analysis of functional and structural disconnection in A lzheimer's disease using multiple kernel SVM
Alzheimer's disease (AD) patients exhibit alterations in the functional connectivity between
spatially segregated brain regions which may be related to both local gray matter (GM) …
spatially segregated brain regions which may be related to both local gray matter (GM) …
Challenges of data integration and interoperability in big data
The enormous volumes of data created and maintained by industries, research institutions
are on the verge of outgrowing its infrastructure. The advancements in the organization's …
are on the verge of outgrowing its infrastructure. The advancements in the organization's …