Guillermo Cámara Chávez
Cardiology and Cardiovascular Medicine · 2016
A researcher in the Science corpus: 1 paper; trusts 15 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Cardiology and Cardiovascular Medicine — 24 citations into it; #632 of 906 by trust here, #699 by citations
- Cognitive Neuroscience — 16 citations into it; #8,862 of 10,852 by trust here, #8,899 by citations
- Artificial Intelligence — 10 citations into it
- Biomedical Engineering — 9 citations into it
- Signal Processing — 8 citations into it
- Pulmonary and Respiratory Medicine — 6 citations into it
- Computer Vision and Pattern Recognition — 5 citations into it
Whom Guillermo Cámara Chávez trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Stanislaw Osowski
- Radford M. Neal
- Lawrence R. Rabiner
- Paul S. Addison
- Sergios Theodoridis
- Mark John Francis Gales
- Michele Banko
- Inan Güler
- Brijraj Singh
Who trusts Guillermo Cámara Chávez overall
Papers from Guillermo Cámara Chávez's perspective
Ordered by how strongly the researchers Guillermo Cámara Chávez trusts endorse each paper. Same corpus, a different order for every researcher.
- Clustering ECG complexes using Hermite functions and self-organizing maps (IEEE Transactions on Biomedical Engineering)
- Pattern Recognition and Machine Learning
- A tutorial on hidden Markov models and selected applications in speech recognition
- ECG beat recognition using fuzzy hybrid neural network (IEEE Transactions on Biomedical Engineering)
- The principles of software QRS detection (IEEE Engineering in Medicine and Biology Magazine)
- Pattern Classification
- A wavelet-based ECG delineator: evaluation on standard databases (IEEE Transactions on Biomedical Engineering)
- Unbiased look at dataset bias
- The impact of the MIT-BIH Arrhythmia Database (IEEE Engineering in Medicine and Biology Magazine)
- Reservoir computing approaches to recurrent neural network training
Their papers
- ECG-based heartbeat classification for arrhythmia detection: A survey (2016) — 808 citations