J
Juan Manuel Górriz
Researcher at University of Granada
Publications - 403
Citations - 8595
Juan Manuel Górriz is an academic researcher from University of Granada. The author has contributed to research in topics: Support vector machine & Computer science. The author has an hindex of 43, co-authored 360 publications receiving 6429 citations. Previous affiliations of Juan Manuel Górriz include University of Cádiz & University of Cambridge.
Papers
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Journal ArticleDOI
BOLD Coupling between Lesioned and Healthy Brain Is Associated with Glioma Patients’ Recovery
Rafael Romero-Garcia,Rafael Romero-Garcia,Michael G Hart,Richard A. I. Bethlehem,Ayan Mandal,Moataz Assem,Benedicto Crespo-Facorro,Juan Manuel Górriz,Juan Manuel Górriz,G. A. Amos Burke,Stephen J. Price,Thomas Santarius,Yaara Erez,Yaara Erez,John Suckling +14 more
TL;DR: In this paper, the spatial correlation pattern between regional and global BOLD signals (also known as global signal topography) was associated with tumour occurrence, and the coupling between the BOLD signal from within the tumour and the signal extracted from different brain tissues was found associated with cognitive recovery.
Journal ArticleDOI
Real time QRS detection based on M-ary likelihood ratio test on the DFT coefficients.
Juan Manuel Górriz,Javier Ramírez,Alberto Olivares,Pablo Padilla,Carlos G. Puntonet,Manuel Canton,Pablo Laguna +6 more
TL;DR: An adaptive statistical test for QRS detection of electrocardiography (ECG) signals based on a M-ary generalized likelihood ratio test defined over a multiple observation window in the Fourier domain is shown.
Book ChapterDOI
New Method for Filtered ICA Signals Applied To Volatile Time Series.
TL;DR: This paper proposes a new method for volatile time series forecasting using techniques like Independent Component Analysis (ICA) or Savitzky-Golay filtering as preprocessing tools and the preprocessed data will be introduced in a based radial basis functions (RBF) Artificial Neural Network (ANN).
Book ChapterDOI
Bayesian segmentation of magnetic resonance images using the α-stable distribution
TL;DR: Performance of the segmentation approaches using spatial prior information, intensity values via the likelihood and combining both using the Bayes' Rule are compared and better segmentation results are obtained when the latter is used.
Posted Content
Applications of Epileptic Seizures Detection in Neuroimaging Modalities Using Deep Learning Techniques: Methods, Challenges, and Future Works.
Afshin Shoeibi,Navid Ghassemi,Marjane Khodatars,Mahboobeh Jafari,Parisa Moridian,Roohallah Alizadehsani,Ali Khadem,Yinan Kong,Assef Zare,Juan Manuel Górriz,Javier Ramírez,Maryam Panahiazar,Abbas Khosravi,Saeid Nahavandi +13 more
TL;DR: A comprehensive overview of the types of DL methods exploited to diagnose epileptic seizures from various neuroimaging modalities has been studied in this paper, where rehabilitation systems and cloud computing in epileptic seizure diagnosis applications have been exactly investigated using various modalities.