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
More filters
Journal ArticleDOI
Speech/non-speech discrimination based on contextual information integrated bispectrum LRT
TL;DR: This letter shows an effective statistical voice activity detection algorithm based on the integrated bispectrum, which is defined as a cross spectrum between the signal and its square and inherits the ability of higher order statistics to detect signals in noise with many other additional advantages.
Journal ArticleDOI
Projecting independent components of SPECT images for computer aided diagnosis of Alzheimer's disease
I. Álvarez Illán,Juan Manuel Górriz,Javier Ramírez,Diego Salas-Gonzalez,Miriam Romero López,Fermín Segovia,Pablo Padilla,Carlos G. Puntonet +7 more
TL;DR: This work presents a computer aided diagnosis system based on supervised learning methods, exploring two different novel approaches and was able to detect the AD perfusion pattern and classify new subjects in an unsupervised manner.
Journal ArticleDOI
Wagyromag: Wireless sensor network for monitoring and processing human body movement in healthcare applications
TL;DR: Wyromag is presented, a wireless Inertial Measurement Unit (IMU) composed of a triaxial accelerometer, gyroscope and magnetometer, which has high potential for medical applications such as telerehabilitation, nocturnal epilepsy seizure detection, fall detection and other applications in the field of sport science.
Journal ArticleDOI
Early diagnosis of Alzheimer's disease based on Partial Least Squares and Support Vector Machine
TL;DR: An effective approach for Single Photon Emission Computed Tomography feature extraction that improves the accuracy of CAD systems for AD diagnosis and yields accuracy rates over 90%, outperforming several recently reported CAD systems.
Journal ArticleDOI
Linear intensity normalization of FP-CIT SPECT brain images using the α-stable distribution.
TL;DR: The linear normalization method using the α-stable distribution outperforms those existing methods using the Gaussian distribution and the widely used specific-to-nonspecific ratio.