Application of Deep Learning for Quality Assessment of Atrial Fibrillation ECG Recordings. - Álvaro Huerta; Arturo Martínez-Rodrigo; Miguel A. Arias; Philip Langley; José Joaquín Rieta; Raúl Alcaraz 001 (CinC, 2020)

A Deep Learning Solution for Automatized Interpretation of 12-Lead ECGs. - Álvaro Huerta; Arturo Martínez-Rodrigo; José Joaquín Rieta; Raúl Alcaraz 001 (CinC, 2020)

Comparative Study of Convolutional Neural Networks for ECG Quality Assessment. - Álvaro Huerta; Arturo Martínez-Rodrigo; Alberto Puchol; Marta I Pachón; José Joaquín Rieta; Raúl Alcaraz 001 (CinC, 2020)

A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices. - Álvaro Huerta Herraiz; Arturo Martínez-Rodrigo; Vicente Bertomeu-González; Aurelio Quesada; José Joaquín Rieta; Raúl Alcaraz 001 (Entropy, 2020)

ECG Quality Assessment via Deep Learning and Data Augmentation. - Álvaro Huerta; Arturo Martínez-Rodrigo; José Joaquín Rieta; Raúl Alcaraz 001 (CinC, 2021)

Detection of Negative Stress through Spectral Features of Electroencephalographic Recordings and a Convolutional Neural Network. - Arturo Martínez-Rodrigo; Beatriz García-Martínez; Álvaro Huerta; Raúl Alcaraz 001 (Sensors, 2021)

Relevance of Pre-Training in the Development of a Light Convolutional Neural Network for ECG Quality Assessment. - Álvaro Huerta; Arturo Martínez-Rodrigo; José Joaquín Rieta; Raúl Alcaraz 001 (CinC, 2023)

Comparison of Supervised Learning Algorithms for Quality Assessment of Wearable Electrocardiograms With Paroxysmal Atrial Fibrillation. - Álvaro Huerta; Arturo Martínez-Rodrigo; Davide Carneiro; Vicente Bertomeu-González; José Joaquín Rieta; Raúl Alcaraz 001 (IEEE Access, 2023)

Single-lead electrocardiogram quality assessment in the context of paroxysmal atrial fibrillation through phase space plots. - Álvaro Huerta; Arturo Martínez-Rodrigo; Vicente Bertomeu-González; Óscar Ayo-Martin; José Joaquín Rieta; Raúl Alcaraz 001 (Biomed. Signal Process. Control., 2024)