D-Index & Metrics Best Publications

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Engineering and Technology D-index 39 Citations 5,639 166 World Ranking 2584 National Ranking 70
Computer Science D-index 49 Citations 7,491 250 World Ranking 3900 National Ranking 49

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Internal medicine
  • Statistics

His primary areas of study are Artificial intelligence, Electroencephalography, Magnetoencephalography, Approximate entropy and Audiology. His Artificial intelligence study integrates concerns from other disciplines, such as Machine learning, Computer vision and Pattern recognition. His Electroencephalography research incorporates elements of Signal-to-noise ratio, Schizophrenia research, Sample entropy and Scalp.

His biological study spans a wide range of topics, including Cardiology, Anesthesia, Internal medicine, Obstructive sleep apnea and Training set. Roberto Hornero has included themes like Disease, Neuroscience and Surrogate data in his Audiology study. His study on Dementia is often connected to In patient as part of broader study in Disease.

His most cited work include:

  • Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs (347 citations)
  • Interpretation of the Lempel-Ziv Complexity Measure in the Context of Biomedical Signal Analysis (267 citations)
  • Entropy analysis of the EEG background activity in Alzheimer's disease patients. (218 citations)

What are the main themes of his work throughout his whole career to date?

His scientific interests lie mostly in Electroencephalography, Artificial intelligence, Pattern recognition, Audiology and Magnetoencephalography. His studies deal with areas such as Cognition, Dementia, Disease and Speech recognition as well as Electroencephalography. His work carried out in the field of Artificial intelligence brings together such families of science as Machine learning and Computer vision.

His Pattern recognition study combines topics in areas such as Sleep apnea and Logistic regression. In his study, Statistics is strongly linked to Control subjects, which falls under the umbrella field of Audiology. His biological study spans a wide range of topics, including Brain activity and meditation and Beta.

He most often published in these fields:

  • Electroencephalography (39.55%)
  • Artificial intelligence (28.53%)
  • Pattern recognition (22.60%)

What were the highlights of his more recent work (between 2018-2021)?

  • Electroencephalography (39.55%)
  • Artificial intelligence (28.53%)
  • Correlation (10.73%)

In recent papers he was focusing on the following fields of study:

Roberto Hornero mainly investigates Electroencephalography, Artificial intelligence, Correlation, Dementia and Pattern recognition. Electroencephalography is a subfield of Neuroscience that Roberto Hornero investigates. Roberto Hornero interconnects Sleep apnea, Machine learning and Fundus in the investigation of issues within Artificial intelligence.

The study incorporates disciplines such as Alpha and Functional connectivity in addition to Dementia. His research in Pattern recognition tackles topics such as Artificial neural network which are related to areas like Continuous wavelet transform. His Audiology research is multidisciplinary, incorporating perspectives in Healthy subjects and Coherence.

Between 2018 and 2021, his most popular works were:

  • Cloud algorithm-driven oximetry-based diagnosis of obstructive sleep apnoea in symptomatic habitually snoring children. (17 citations)
  • Evaluation of Machine-Learning Approaches to Estimate Sleep Apnea Severity From At-Home Oximetry Recordings (17 citations)
  • Computational modeling of the effects of EEG volume conduction on functional connectivity metrics. Application to Alzheimer's disease continuum. (13 citations)

In his most recent research, the most cited papers focused on:

  • Internal medicine
  • Artificial intelligence
  • Statistics

Roberto Hornero mostly deals with Electroencephalography, Dementia, Polysomnography, Correlation and Artificial intelligence. His work deals with themes such as Sample entropy and Speech recognition, which intersect with Electroencephalography. His Dementia research incorporates themes from Resting state fMRI, Neuroscience and Audiology.

His Polysomnography course of study focuses on Feature selection and Cardiology and Internal medicine. His Artificial intelligence research incorporates elements of Sleep apnea and Pattern recognition. The concepts of his Pattern recognition study are interwoven with issues in Artificial neural network, Multilayer perceptron and Deep learning.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs

Meindert Niemeijer;Bram van Ginneken;Michael J Cree;Atsushi Mizutani.
IEEE Transactions on Medical Imaging (2010)

547 Citations

Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs

Meindert Niemeijer;Bram van Ginneken;Michael J Cree;Atsushi Mizutani.
IEEE Transactions on Medical Imaging (2010)

547 Citations

Interpretation of the Lempel-Ziv Complexity Measure in the Context of Biomedical Signal Analysis

M. Aboy;R. Hornero;D. Abasolo;D. Alvarez.
IEEE Transactions on Biomedical Engineering (2006)

391 Citations

Interpretation of the Lempel-Ziv Complexity Measure in the Context of Biomedical Signal Analysis

M. Aboy;R. Hornero;D. Abasolo;D. Alvarez.
IEEE Transactions on Biomedical Engineering (2006)

391 Citations

Analysis of EEG background activity in Alzheimer's disease patients with Lempel-Ziv complexity and central tendency measure.

Daniel Abásolo;Roberto Hornero;Carlos Gómez;María García.
Medical Engineering & Physics (2006)

338 Citations

Analysis of EEG background activity in Alzheimer's disease patients with Lempel-Ziv complexity and central tendency measure.

Daniel Abásolo;Roberto Hornero;Carlos Gómez;María García.
Medical Engineering & Physics (2006)

338 Citations

Entropy analysis of the EEG background activity in Alzheimer's disease patients.

D Abásolo;R Hornero;P Espino;D Álvarez.
Physiological Measurement (2006)

334 Citations

Analysis of regularity in the EEG background activity of Alzheimer's disease patients with Approximate Entropy

Daniel Abásolo;Roberto Hornero;Pedro Espino;Jesús Poza.
Clinical Neurophysiology (2005)

261 Citations

Analysis of electroencephalograms in Alzheimer's disease patients with multiscale entropy.

J Escudero;D Abásolo;R Hornero;P Espino.
Physiological Measurement (2006)

232 Citations

Analysis of electroencephalograms in Alzheimer's disease patients with multiscale entropy.

J Escudero;D Abásolo;R Hornero;P Espino.
Physiological Measurement (2006)

232 Citations

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