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Browsing by Author "Martínez-Rodrigo, Arturo"

Now showing items 1-9 of 9

A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices

Huerta Herraiz, Álvaro; Martínez-Rodrigo, Arturo; Bertomeu-González, Vicente; Quesada, Aurelio; Rieta, J J; Alcaraz, Raúl (MDPI AG, 2020-07)

[EN] Atrial fibrillation (AF) is the most common heart rhythm disturbance in clinical practice. It often starts with asymptomatic and very short episodes, which are extremely difficult to detect without long-term monitoring ...
Blending Inverted Lectures and Laboratory Experiments to Improve Learning in an Introductory Course in Digital Systems

Alcaraz, Raúl; Martínez-Rodrigo, Arturo; Zangroniz, Roberto; Rieta, J J (Institute of Electrical and Electronics Engineers, 2020-08)
Comparison of Pre-Trained Deep Learning Algorithms for Quality Assessment of Electrocardiographic Recordings

Huerta, Álvaro; Martínez-Rodrigo, Arturo; Puchol, Alberto; Pachón, Marta I.; Alcaraz, Raúl; Rieta, J J (IEEE, 2020-10-30)

[EN] Convolutional neural networks (CNNs) have recently become popular for electrocardiogram (ECG) analysis. However, their ability for ECG quality assessment has still not been thoroughly assessed. This challenging topic, ...
Deep Learning Detection of Corrupted Segments in Recordings from Wearable Devices to Improve Atrial Fibrillation Screening

Huerta, Álvaro; Martínez-Rodrigo, Arturo; Arias, Miguel A.; Langley, Phillip; Rieta, J J; Alcaraz, Raúl (IEEE, 2020-10-30)

[EN] Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia in clinical practice. It is associated with an increased risk of cardiovascular events, but its early detection is an unresolved challenge. For ...
Early Prediction of Students at Risk of Failing a Face-to-Face Course in Power Electronic Systems

Alcaraz, Raúl; Martínez-Rodrigo, Arturo; Zangróniz, Roberto; Rieta, J J (IEEE, 2021-10-01)

[EN] Early warning systems (EWSs) have proven to be useful in identifying students at risk of failing both online and conventional courses. Although some general systems have reported acceptable ability to work in modules ...
ECG Quality Assessment via Deep Learning and Data Augmentation

Huerta, Álvaro; Martínez-Rodrigo, Arturo; Rieta, J J; Alcaraz, Raúl (2021-09-15)

[EN] Quality assessment of ECG signals acquired with wearable devices is essential to avoid misdiagnosis of some cardiac disorders. For that purpose, novel deep learning algorithms have been recently proposed. However, ...
Improved Discrimination Between Healthy and Hypertensive Individuals Combining Photoplethysmography and Electrocardiography

Cano, Jesús; Hornero, Fernando; Quesada, Aurelio; Martínez-Rodrigo, Arturo; Alcaraz, Raúl; Rieta, J J (2021-09-15)

[EN] Cardiovascular disease is one of the leading causes of death, with hypertension (HT) being its main risk factor. Its complications can be avoided with early treatment, but since these patients do not present any ...
On the Generalization of Sleep Apnea Detection Methods Based on Heart Rate Variability and Machine Learning

Padovano, Daniele; Martínez-Rodrigo, Arturo; Pastor, José M.; Rieta, J J; Alcaraz, Raul (Institute of Electrical and Electronics Engineers, 2022)

[EN] Obstructive sleep apnea (OSA) is a respiratory disorder highly correlated with severe cardiovascular diseases that has unleashed the interest of hundreds of experts aiming to overcome the elevated requirements of ...
Single-lead electrocardiogram quality assessment in the context of paroxysmal atrial fibrillation through phase space plots

Huerta, Alvaro; Martínez-Rodrigo, Arturo; Bertomeu-González, Vicente; Ayo-Martin, Oscar; Rieta, J J; Alcaraz, Raúl (Elsevier, 2024-05)

[EN] Current wearable electrocardiogram (ECG) recording systems have great potential to revolutionize early diagnosis of paroxysmal atrial fibrillation (AF). They are able to continuously acquire an ECG signal for long ...