José Joaquín Rieta

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Full Professor

University of Valencia

Spain

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About

José Joaquín Rieta is a Full Professor at Universidad Politénica de Valencia, Spain. His research areas focus on deep learning applications in health monitoring, cardiovascular data analysis, and the assessment of electrocardiographic signals. He has published extensively on topics such as the detection of obstructive sleep apnea, atrial fibrillation prediction, and the evaluation of wearable health devices.

Recent Grants

Grant: Close

Artificial Intelligence to Revolutionize Mobile Medicine Using Wearable Devices

Open Date: 2021-01-01

Close Date: 2023-12-01

Grant: Close

Diagnostic and Personalized Therapy Methods in Catheter Ablation of Cardiac Ar-rythmias

Open Date: 2019-01-01

Close Date: 2021-03-01

Grant: Close

Multicentric study for the evaluation of the arrhythmogenic substrate in patients with atrial fibrillation. Application to catheter ablation

Open Date: 2018-01-01

Close Date: 2021-09-01

Grant: Close

Electroanatomic Remodeling Quantification in Cardiac Arrhythmias. From Research to Tailored Therapy

Open Date: 2015-01-01

Close Date: 2017-12-01

Grant: Close

Development and application of advanced organization estimators in the therapeutic classification and following up of patients in atrial fibrillation

Open Date: 2011-01-01

Close Date: 2013-12-01

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Aurelio Quesada

HEAD OF ARRHYTHMIAS UNIT

Hospital General Universitario De Valencia

SPAIN

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Vicente Bertomeu-Gonzalez

Head

Bispebjerg Hospital

SPAIN

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Manuel Garcia Teruel

Profesor Titular de Universidad

University of Castilla-La Mancha

SPAIN

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José Joaquín Rieta

Research Interests

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About

Recent Grants

Articles (19)

Deep Learning and Recurrence Information Analysis for the Automatic Detection of Obstructive Sleep Apnea

A Novel Nonlinear Method for Cardiovascular Data Synchronization

Comparative Study on the Generalization Ability of Machine Learning and Deep Learning Algorithms for Quality Assessment of Wearable PPG Recordings

Single-lead electrocardiogram quality assessment in the context of paroxysmal atrial fibrillation through phase space plots

Combination of frequency- and time-domain characteristics of the fibrillatory waves for enhanced prediction of persistent atrial fibrillation recurrence after catheter ablation

Novel Entropy-Based Metrics for Long-Term Atrial Fibrillation Recurrence Prediction Following Surgical Ablation: Insights from Preoperative Electrocardiographic Analysis

A Novel Signal Restoration Method of Noisy Photoplethysmograms for Uninterrupted Health Monitoring

Comparison of Supervised Learning Algorithms for Quality Assessment of Wearable Electrocardiograms With Paroxysmal Atrial Fibrillation

Improved Hypertension Risk Assessment with Photoplethysmographic Recordings Combining Deep Learning and Calibration

Preoperative Prediction of Catheter Ablation Outcome in Persistent Atrial Fibrillation Patients through Spectral Organization Analysis of the Surface Fibrillatory Waves

Comparative Study of Methods for Cycle Length Estimation in Fractionated Electrograms of Atrial Fibrillation

Practical Considerations for the Application of Nonlinear Indices Characterizing the Atrial Substrate in Atrial Fibrillation

An Efficient Hybrid Methodology for Local Activation Waves Detection under Complex Fractionated Atrial Electrograms of Atrial Fibrillation

The Relevance of Heart Rate Fluctuation When Evaluating Atrial Substrate Electrical Features in Catheter Ablation of Paroxysmal Atrial Fibrillation

The Relevance of Calibration in Machine Learning-Based Hypertension Risk Assessment Combining Photoplethysmography and Electrocardiography

The Dissimilar Impact in Atrial Substrate Modificationof Left and Right Pulmonary Veins Isolation after Catheter Ablation of Paroxysmal Atrial Fibrillation

On the Generalization of Sleep Apnea Detection Methods Based on Heart Rate Variability and Machine Learning

Splitting the P-Wave: Improved Evaluation of Left Atrial Substrate Modification after Pulmonary Vein Isolation of Paroxysmal Atrial Fibrillation

Early Prediction of Students at Risk of Failing a Face-to-Face Course in Power Electronic Systems

Collaborators (10)

Jose Manuel Pastor Garcia

Davide Carneiro

Roberto Zangroniz

Juan Ródenas García

Arturo Martinez-Rodrigo

Oscar Ayo-Martin

Raúl Alcaraz

Aurelio Quesada

Vicente Bertomeu-Gonzalez

Manuel Garcia Teruel

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