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[EN] In the last years, atrial fibrillation (AF) has become one of the most remarkable health problems in the developed world. This arrhythmia is associated with an increased risk of cardiovascular events, being its early detection an unresolved challenge. To palliate this issue, long-term wearable electrocardiogram (ECG) recording systems are used, because most of AF episodes are asymptomatic and very short in their initial stages. Unfortunately, portable equipments are very susceptible to be contaminated with different kind of noises, since they work in highly dynamics and ever-changing environments. Within this scenario, the correct identification of free-noise ECG segments results critical for an accurate and robust AF detection. Hence, this work presents a deep learning-based algorithm to identify high-quality intervals in single-lead ECG recordings obtained from patients with paroxysmal AF. The obtained results have provided a remarkable ability to classify between high- and low-quality ECG segments about 92%, only misclassifying around 7% of clean AF intervals as noisy segments. These outcomes have overcome most previous ECG quality assessment algorithms also dealing with AF signals by more than 20%.

[EN] Despite its limited long-term success rate, catheter ablation (CA) is still the first-line treatment for patients suffering from persistent atrial fibrillation (AF). Preoperative prediction on the procedure¿s outcome is gaining clinical interest to provide optimized patient selection, limit repeated procedures, hospitalization rates, and treatment costs. To this respect, dominant frequency (DF) and amplitude of fibrillatory waves (f-waves) reflected on the electrocardiogram (ECG) recording have provided promising results. Hence, this work explores the ability of a novel set of frequency and amplitude features of the f-waves to improve CA outcome prediction. More precisely, in addition to DF and normalized f-wave amplitude (NFWA), three indices such as spectral entropy (SE), spectral flatness measure (SFM), and amplitude spectrum area (AMSA) were also analyzed. The results obtained for 204 6 s-length ECG intervals extracted from 51 persistent AF patients showed that all indices found statistically significant differences between the patients who maintained sinus rhythm and those who relapsed to AF after a follow-up of 9 months. However, they only obtained limited a discriminant ability ranging between 55 and 62%, which was overcome by 15¿23% when NFWA, SE and AMSA were combined. Consequently, this combination of frequency and amplitude features of the f-waves seems to provide new insights about the atrial substrate remodeling, which could be helpful in improving preoperative CA outcome prediction. Nonetheless, further studies are required in the future

[EN] Although catheter ablation (CA) is still the first-line treatment for persistent atrial fibrillation (AF) patients, its limited long-term success rate has motivated clinical interest in preoperative prediction on the procedure¿s outcome to provide optimized patient selection, limit repeated procedures, hospitalization rates, and treatment costs. To this respect, dominant frequency (DF) and amplitude of fibrillatory waves (f-waves) reflected on the ECG have provided promising results. Hence this work explores the ability of a novel set of frequency and amplitud f-waves features, such as spectral entropy (SE), spectral flatness measure (SFM), and amplitud spectrum area (AMSA), along with DF and normalized f-wave amplitude (NFWA), to improve CA outcome prediction. Despite all single indices reported statistically significant differences between patients who relapsed to AF and those who maintained sinus rhythm after a follow-up of 9 months for 204 6 s-length ECG intervals extracted from 51 persistent AF patients, they obtained a limited discriminant ability ranging between 55 and 62%, which was overcome by 15¿23% when NFWA, SE and AMSA were combined. Consequently, this combination of frequency and amplitude features of the fwaves seems to provide new insights about the atrial substrate remodeling, which could be helpful in improving preoperative CA outcome prediction.

[EN] Detailed analysis of the electrocardiogram (ECG) recording allows to obtain relevant information about the heart state and its behavior. Indeed, the P-wave study has proven to be helpful in diagnosing many atrial electrical conduction disorders. However, manual delineation of this wave is a very time-consuming task, and a wide variety of automatic algorithms have been proposed for that purpose. The performance of these methods has been mostly validated on the QT database, since it contains manual annotations from clinical experts and is freely available at PhysioNet. Unfortunately, in the last years several authors have shown some major limitations of this database and this work hence presents a more robust validation of a previously published P-wave delineator. Thus, two novel databases which have been very recently made available to the scienti¿c community have been analyzed. The results show that the methods ability to detect P-waves is close to 100% in stable sinus rhythm episodes, but it decreases to 94% when the heart rhythm is irregular or consecutive P-waves exhibit notable morphological variations. Regarding the error between the annotations obtained automatically and those available as a reference, no relevant differences have been observed in terms of standard deviation, thus suggesting that the algorithm achieves a consistent delineation of the P-waves. Contrarily, a notable error has been noticed in average values, thus highlighting the need of a more extensive training for the P-wave delineator than that offered by the QT database.