2,324 research outputs found
Combined Nonlinear Analysis of Atrial and Ventricular Series for Automated Screening of Atrial Fibrillation
[EN] Atrial fibrillation (AF) is the most common cardiac arrhythmia in clinical practice. It often starts with asymptomatic and short episodes, which are difficult to detect without the assistance of automatic monitoring tools. The vast majority of methods proposed for this purpose are based on quantifying the irregular ventricular response (i.e., RR series) during the arrhythmia. However, although AF totally alters the atrial activity (AA) reflected on the electrocardiogram(ECG), replacing stable P-waves by chaotic and time-variant fibrillatory waves, this information has still not been explored for automated screening of AF. Hence, a pioneering AF detector based on quantifying the variability over time of the AA morphological pattern is here proposed. Results from two public reference databases have proven that the proposed method outperforms current state-of-the-art algorithms, reporting accuracy higher than 90%. A less false positive rate in the presence of other arrhythmias different from AF was also noticed. Finally, the combination of this algorithm with the classical analysis of RR series variability also yielded a promising trade-off between AF accuracy and detection delay. Indeed, this combination provided similar accuracy than RR-based methods, but with a significantly shorter delay of 10 beats.This work was supported by the Spanish Ministry of Economy and Competitiveness (Project TEC2014-52250-R).Rodenas, J.; Garcia, M.; Alcaraz, R.; Rieta, JJ. (2017). Combined Nonlinear Analysis of Atrial and Ventricular Series for Automated Screening of Atrial Fibrillation. Complexity. (2163610):1-13. doi:10.1155/2017/2163610S113216361
Sobre la presencia de Sternbergial Lutea (L.) Ker-Gawler (Amaryllidaceae) en extremadura
On the presence of Sternbergia iutea (L.) Ker-Gawler (Amaryllidaceae) in Extremadura. Palabras clave. Sternbergia, Amaryllidaceae, corología, Extremadura, España. Key words. Stern bergia, Amaryllidaceae, chorology, Extremadura, Spain
An Efficient Algorithm Based on Wavelet Transform to Reduce Powerline Noise From Electrocardiograms
Nowadays, the electrocardiogram (ECG) is still the most widely used signal
for the diagnosis of cardiac pathologies. However, this recording is often
disturbed by the powerline interference (PLI), its removal being mandatory to
avoid misdiagnosis. Although a broad variety of methods have been proposed for
that purpose, often they substantially alter the original signal morphology or
are computationally expensive. Hence, the present work introduces a simple and
efficient algorithm to suppress the PLI from the ECG. Briefly, the input signal
is decomposed into four Wavelet levels and the resulting coefficients are
thresholded to remove the PLI estimated from the TQ intervals. The denoised ECG
signal is then reconstructed by computing the inverse Wavelet transform. The
method has been validated making use of fifty 10-min length clean ECG segments
obtained from the MIT BIH Normal Sinus Rhythm database, which were contaminated
with a sinusoidal signal of 50 Hz and variable harmonic content. Comparing the
original and denoised ECG signals through a signed correlation index,
improvements between 10 - 72% have been observed with respect to common
adaptive notch filtering, implemented for comparison. These results suggest
that the proposed method is featured by an enhanced trade-off between noise
reduction and signal morphology preservation
A novel wavelet-based filtering strategy to remove powerline interference from electrocardiograms with atrial fibrillation
This is an author-created, un-copyedited versíon of an article published in Physiological Measurement. IOP Publishing Ltd is not responsíble for any errors or omissíons in this versíon of the manuscript or any versíon derived from it. The Versíon of Record is available online at http://doi.org/10.1088/1361-6579/aae8b1[EN] Objective: The electrocardiogram (ECG) is currently the most widely used recording to diagnose cardiac disorders, including the most common supraventricular arrhythmia, such as atrial fibrillation (AF). However, different types of electrical disturbances, in which power-line interference (PLI) is a major problem, can mask and distort the original ECG morphology. This is a significant issue in the context of AF, because accurate characterization of fibrillatory waves (f-waves) is unavoidably required to improve current knowledge about its mechanisms. This work introduces a new algorithm able to reduce high levels of PLI and preserve, simultaneously, the original ECG morphology. Approach: The method is based on stationary wavelet transform shrinking and makes use of a new thresholding function designed to work successfully in a wide variety of scenarios. In fact, it has been validated in a general context with 48 ECG recordings obtained from pathological and non-pathological conditions, as well as in the particular context of AF, where 380 synthesized and 20 long-term real ECG recordings were analyzed. Main results: In both situations, the algorithm has reported a notably better performance than common methods designed for the same purpose. Moreover, its effectiveness has proven to be optimal for dealing with ECG recordings affected by AF, sincef-waves remained almost intact after removing very high levels of noise. Significance: The proposed algorithm may facilitate a reliable characterization of thef-waves, preventing them from not being masked by the PLI nor distorted by an unsuitable filtering applied to ECG recordings with AF.Research supported by grants DPI2017-83952-C3 MINECO/AEI/FEDER, UE and SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-La Mancha.García, M.; Martínez, M.; Ródenas, J.; Rieta, JJ.; Alcaraz, R. (2018). A novel wavelet-based filtering strategy to remove powerline interference from electrocardiograms with atrial fibrillation. Physiological Measurement. 39(11):1-15. https://doi.org/10.1088/1361-6579/aae8b1S115391
The Stationary Wavelet Transform as an Efficient Reductor of Powerline Interference for Atrial Bipolar Electrograms in Cardiac Electrophysiology
Objective: The most relevant source of signal contamination in the cardiac
electrophysiology (EP) laboratory is the ubiquitous powerline interference
(PLI). To reduce this perturbation, algorithms including common fixed bandwidth
and adaptive notch filters have been proposed. Although such methods have
proven to add artificial fractionation to intra atrial electrograms (EGMs),
they are still frequently used. However, such morphological alteration can
conceal the accurate interpretation of EGMs, specially to evaluate the
mechanisms supporting atrial fibrillation (AF), which is the most common
cardiac arrhythmia. Given the clinical relevance of AF, a novel algorithm aimed
at reducing PLI on highly contaminated bipolar EGMs and, simultaneously,
preserving their morphology is proposed. Approach: The method is based on the
wavelet shrinkage and has been validated through customized indices on a set of
synthesized EGMs to accurately quantify the achieved level of PLI reduction and
signal morphology alteration. Visual validation of the algorithms performance
has also been included for some real EGM excerpts. Main results: The method has
outperformed common filtering-based and wavelet based strategies in the
analyzed scenario. Moreover, it possesses advantages such as insensitivity to
amplitude and frequency variations in the PLI, and the capability of joint
removal of several interferences. Significance: The use of this algorithm in
routine cardiac EP studies may enable improved and truthful evaluation of AF
mechanisms
Application of Joint Notch Filtering and Wavelet Transform for Enhanced Powerline Interference Removal in Atrial Fibrillation Electrograms
Analysis of intra-atrial electrograms (EGMs) nowadays constitutes the most
common way to gain new insights about the mechanisms triggering and maintaining
atrial fibrillation (AF). However, these recordings are highly contaminated by
powerline interference (PLI) due to the large amount of electrical devices
operating simultaneously in the electrophysiology laboratory. To remove this
perturbation, conventional notch filtering has been widely used. However, this
method adds artificial fractionation to the EGMs, thus concealing their
accurate interpretation. Hence, the development of novel algorithms for PLI
suppression in EGMs is still an unresolved challenge. Within this context, the
present work introduces the joint application of common notch filtering and
Wavelet denoising for enhanced PLI removal in AF EGMs. The algorithm was
validated on a set of 100 unipolar EGM signals, which were synthesized with
different noise levels. Original and denoised EGMs were compared in terms of a
signed correlation index (SCI), computed both in time and frequency domains.
Compared with the single use of notch filtering, improvements between 4 and 15%
were reached with Wavelet denoising in both domains. As a consequence, the
proposed algorithm was able to efficiently reduce high levels of PLI and
simultaneously preserve the original morphology of AF EGMs
Novel Entropy-Based Metrics for Long-Term Atrial Fibrillation Recurrence Prediction Following Surgical Ablation: Insights from Preoperative Electrocardiographic Analysis
[EN] Atrial fibrillation (AF) is a prevalent cardiac arrhythmia often treated concomitantly with other cardiac interventions through the Cox-Maze procedure. This highly invasive intervention is still linked to a long-term recurrence rate of approximately 35% in permanent AF patients. The aim of this study is to preoperatively predict long-term AF recurrence post-surgery through the analysis of atrial activity (AA) organization from non-invasive electrocardiographic (ECG) recordings. A dataset comprising ECGs from 53 patients with permanent AF who had undergone Cox-Maze concomitant surgery was analyzed. The AA was extracted from the lead V1 of these recordings and then characterized using novel predictors, such as the mean and standard deviation of the relative wavelet energy (RWEm and RWEs) across different scales, and an entropy-based metric that computes the stationary wavelet entropy variability (SWEnV). The individual predictors exhibited limited predictive capabilities to anticipate the outcome of the procedure, with the SWEnV yielding a classification accuracy (Acc) of 68.07%. However, the assessment of the RWEs for the seventh scale (RWEs7), which encompassed frequencies associated with the AA, stood out as the most promising individual predictor, with sensitivity (Se) and specificity (Sp) values of 80.83% and 67.09%, respectively, and an Acc of almost 75%. Diverse multivariate decision tree-based models were constructed for prediction, giving priority to simplicity in the interpretation of the forecasting methodology. In fact, the combination of the SWEnV and RWEs7 consistently outperformed the individual predictors and excelled in predicting post-surgery outcomes one year after the Cox-Maze procedure, with Se, Sp, and Acc values of approximately 80%, thus surpassing the results of previous studies based on anatomical predictors associated with atrial function or clinical data. These findings emphasize the crucial role of preoperative patient-specific ECG signal analysis in tailoring post-surgical care, enhancing clinical decision making, and improving long-term clinical outcomes.This research has received financial support from public grants PID2021-123804OB-I00, PID2021-
00X128525-IV0, and TED2021-130935B-I00 of the Spanish Government, 10.13039/501100011033, in conjunction with the European Regional Development Fund (EU), SBPLY/21/180501/000186, from Junta
de Comunidades de Castilla-La Mancha, and AICO/2021/286 from Generalitat Valenciana. Pilar
Escribano holds the 2020-PREDUCLM-15540 scholarship co-financed by the European Social Fund
(ESF) operating program 2014 2020 of Castilla-La Mancha.Escribano, P.; Ródenas, J.; García, M.; Hornero, F.; Gracia-Baena, JM.; Alcaraz, R.; Rieta, JJ. (2024). Novel Entropy-Based Metrics for Long-Term Atrial Fibrillation Recurrence Prediction Following Surgical Ablation: Insights from Preoperative Electrocardiographic Analysis. Entropy. 26(1). https://doi.org/10.3390/e2601002826
A novel wavelet-based filtering strategy to remove powerline interference from electrocardiograms with atrial fibrillation
"Objective: The electrocardiogram (ECG) is currently the most widely used
recording to diagnose cardiac disorders, including the most common
supraventricular arrhythmia, such as atrial fibrillation (AF). However,
different types of electrical disturbances, in which power-line interference
(PLI) is a major problem, can mask and distort the original ECG morphology.
This is a significant issue in the context of AF, because accurate
characterization of fibrillatory waves (f-waves) is unavoidably required to
improve current knowledge about its mechanisms. This work introduces a new
algorithm able to reduce high levels of PLI and preserve, simultaneously, the
original ECG morphology. Approach: The method is based on stationary wavelet
transform shrinking and makes use of a new thresholding function designed to
work successfully in a wide variety of scenarios. In fact, it has been
validated in a general context with 48 ECG recordings obtained from
pathological and non-pathological conditions, as well as in the particular
context of AF, where 380 synthesized and 20 long-term real ECG recordings were
analyzed. Main results: In both situations, the algorithm has reported a
notably better performance than common methods designed for the same purpose.
Moreover, its effectiveness has proven to be optimal for dealing with ECG
recordings affected by AF, since f-waves remained almost intact after removing
very high levels of noise. Significance: The proposed algorithm may facilitate
a reliable characterization of the f-waves, preventing them from not being
masked by the PLI nor distorted by an unsuitable filtering applied to ECG
recordings with AF.
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