Ventricular Fibrillation detection using time-frequency and the KNN classifier without parameter extraction

Autor: Azeddine Mjahad, Alfredo Rosado Muñoz, Manuel Bataller Mompeán, Jose V. Francés Víllora, Juan F. Guerrero Martínez

Publikováno v: Revista Iberoamericana de Automática e Informática Industrial RIAI, Vol 15, Iss 1, Pp 124-132 (2017)

This work describes new techniques to improve VF detection and its separation from Ventricular Tachycarida (VT) and other rhythms. It is based on time-frequency representation of the ECG and its use as input in an automatic classifier (K-nearest neig

Externí odkaz: https://doaj.org/article/6a20b6964bfe46bcbf8fea7d72c50170

Detection of Ventricular Fibrillation Using the Image from Time-Frequency Representation and Combined Classifiers without Feature Extraction

Autor: Azeddine Mjahad, Alfredo Rosado-Muñoz, Juan F. Guerrero-Martínez, Manuel Bataller-Mompeán, Jose V. Francés-Villora, Malay Kishore Dutta

Publikováno v: Applied Sciences, Vol 8, Iss 11, p 2057 (2018)

Due the fact that the required therapy to treat Ventricular Fibrillation (V F) is aggressive (electric shock), the lack of a proper detection and recovering therapy could cause serious injuries to the patient or trigger a ventricular fibrillation, or

Externí odkaz: https://doaj.org/article/e20340004b314a6d86c48382be6c9a9d

Real-Time Localization of Epileptogenic Foci EEG Signals: An FPGA-Based Implementation

Autor: Manuel Bataller-Mompeán, Jose V. Frances-Villora, Juan F. Guerrero-Martinez, Antonio Gutierrez Martin, Vicente Villanueva, Azeddine Mjahad, Vicent Teruel-Martí, Alfredo Rosado-Muñoz, Kevin G. Hampel

Publikováno v: Applied Sciences-Basel
r-IIS La Fe. Repositorio Institucional de Producción Científica del Instituto de Investigación Sanitaria La Fe
instname
Francés Villora, José Vicente Bataller Mompean, Manuel Mjahad, Azeddine Rosado Muñoz, Alfredo Gutiérrez Martín, Antonio Teruel Martí, Vicent Villanueva, Vicente Hampel, Kevin G. Guerrero Martínez, Juan Francisco 2020 Real-Time Localization of Epileptogenic Foci EEG Signals: An FPGA-Based Implementation Applied Sciences-Basel 10 827
RODERIC: Repositorio Institucional de la Universitat de Valéncia
RODERIC. Repositorio Institucional de la Universitat de Valéncia
Applied Sciences, Vol 10, Iss 3, p 827 (2020)
Applied Sciences
Volume 10
Issue 3

The epileptogenic focus is a brain area that may be surgically removed to control of epileptic seizures. Locating it is an essential and crucial step prior to the surgical treatment. However, given the difficulty of determining the localization of th

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ab4c1419098772986634a868961b1150
https://fundanet.iislafe.san.gva.es/publicaciones/ProdCientif/PublicacionFrw.aspx?id=12918

An Scalable matrix computing unit architecture for FPGA and SCUMO user design interface

Autor: Manuel Bataller-Mompeán, Taras Iakymchuk, Alfredo Rosado-Muñoz, Asgar Abbaszadeh, Jose V. Frances-Villora

Publikováno v: Abbaszadeh, Asgar Iakymchuk, Taras Bataller Mompean, Manuel Francés Villora, José Vicente Rosado Muñoz, Alfredo 2019 An Scalable matrix computing unit architecture for FPGA and SCUMO user design interface Electronics 8 1 94-1 94-20
RODERIC. Repositorio Institucional de la Universitat de Valéncia
instname
Electronics
Volume 8
Issue 1
Electronics, Vol 8, Iss 1, p 94 (2019)

High dimensional matrix algebra is essential in numerous signal processing and machine learning algorithms. This work describes a scalable square matrix-computing unit designed on the basis of circulant matrices. It optimizes data flow for the comput

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bb0491dd6d8ee3879779e1e310278171
https://doi.org/10.3390/electronics8010094

Hardware implementation of real-time Extreme Learning Machine in FPGA: Analysis of precision, resource occupation and performance

Autor: Alfredo Rosado-Muñoz, Manuel Bataller-Mompeán, Jose V. Frances-Villora, Marek Wegrzyn, Juan Guerrero, José M. Martínez-Villena

Publikováno v: Computers & Electrical Engineering. 51:139-156

Extreme Learning Machine (ELM) on-chip learning is implemented on FPGA.Three hardware architectures are evaluated.Parametrical analysis of accuracy, resource occupation and performance is carried out. Display Omitted Extreme Learning Machine (ELM) pr

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::af2e3569b0f4a903ed04b9a05c00f071
https://doi.org/10.1016/j.compeleceng.2016.02.007