78 research outputs found
A novel wavelet-based filtering strategy to remove powerline interference from electrocardiograms with atrial fibrillation
This is an author-created, un-copyedited versÃon of an article published in Physiological Measurement. IOP Publishing Ltd is not responsÃble for any errors or omissÃons in this versÃon of the manuscript or any versÃon derived from it. The VersÃon 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.GarcÃa, M.; MartÃnez, 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
Application of Deep Learning for Quality Assessment of Atrial Fibrillation ECG Recordings
[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%.This research has been supported by the grants DPI2017-83952-C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-La Mancha, AICO/2019/036 from Generalitat Valenciana and FEDER 2018/11744.Huerta, A.; Martinez-Rodrigo, A.; Arias, MA.; Langley, P.; Rieta, JJ.; Alcaraz, R. (2020). Application of Deep Learning for Quality Assessment of Atrial Fibrillation ECG Recordings. IEEE. 1-4. https://doi.org/10.22489/CinC.2020.367S1
Estrazione non invasiva del segnale elettrocardiografico fetale da registrazioni con elettrodi posti sull’addome della gestante (Non-invasive extraction of the fetal electrocardiogram from abdominal recordings by positioning electrodes on the pregnant woman’s abdomen)
openIl cuore è il primo organo che si sviluppa nel feto, particolarmente nelle primissime settimane di
gestazione. Rispetto al cuore adulto, quello fetale ha una fisiologia ed un’anatomia significativamente
differenti, a causa della differente circolazione cardiovascolare. Il benessere fetale si valuta
monitorando l’attività cardiaca mediante elettrocardiografia fetale (ECGf). L’ECGf invasivo (acquisito
posizionando elettrodi allo scalpo fetale) è considerato il gold standard, ma l’invasività che lo
caratterizza ne limita la sua applicabilità . Al contrario, l’uso clinico dell’ECGf non invasivo (acquisito
posizionando elettrodi sull’addome della gestante) è limitato dalla scarsa qualità del segnale risultante.
L’ECGf non invasivo si estrae da registrazioni addominali, che sono corrotte da differenti tipi di rumore,
fra i quali l’interferenza primaria è rappresentata dall’ECG materno. Il Segmented-Beat Modulation
Method (SBMM) è stato da me recentemente proposto come una nuova procedura di filtraggio basata
sul calcolo del template del battito cardiaco. SBMM fornisce una stima ripulita dell’ECG estratto da
registrazioni rumorose, preservando la fisiologica variabilità ECG del segnale originale. Questa
caratteristica è ottenuta grazie alla segmentazione di ogni battito cardiaco per indentificare i segmenti
QRS e TUP, seguito dal processo di modulazione/demodulazione (che include strecciamento e
compressione) del segmento TUP, per aggiustarlo in modo adattativo alla morfologia e alla durata di
ogni battito originario. Dapprima applicato all’ECG adulto al fine di dimostrare la sua robustezza al
rumore, l’SBMM è stato poi applicato al caso fetale. Particolarmente significativi sono i risultati relativi
alle applicazioni su ECGf non invasivo, dove l’SBMM fornisce segnali caratterizzati da un rapporto
segnale-rumore comparabile a quello caratterizzante l’ECGf invasivo. Tuttavia, l’SBMM può
contribuire alla diffusione dell’ECGf non invasiva nella pratica clinica.The heart is the first organ that develops in the fetus, particularly in the very early stages
of pregnancy. Compared to the adult heart, the physiology and anatomy of the fetal heart
exhibit some significant differences. These differences originate from the fact that the fetal
cardiovascular circulation is different from the adult circulation. Fetal well-being
evaluation may be accomplished by monitoring cardiac activity through fetal
electrocardiography (fECG). Invasive fECG (acquired through scalp electrodes) is the
gold standard but its invasiveness limits its clinical applicability. Instead, clinical use of
non-invasive fECG (acquired through abdominal electrodes) has so far been limited by its
poor signal quality. Non-invasive fECG is extracted from the abdominal recording and is
corrupted by different kind of noise, among which maternal ECG is the main interference.
The Segmented-Beat Modulation Method (SBMM) was recently proposed by myself as a
new template-based filtering procedure able to provide a clean ECG estimation from a
noisy recording by preserving physiological ECG variability of the original signal. The
former feature is achieved thanks to a segmentation procedure applied to each cardiac
beat in order to identify the QRS and TUP segments, followed by a
modulation/demodulation process (involving stretching and compression) of the TUP
segments to adaptively adjust each estimated cardiac beat to the original beat morphology
and duration. SBMM was first applied to adult ECG applications, in order to demonstrate
its robustness to noise, and then to fECG applications. Particularly significant are the
results relative to the non-invasive applications, where SBMM provided fECG signals
characterized by a signal-to-noise ratio comparable to that characterizing invasive fECG.
Thus, SBMM may contribute to the spread of this noninvasive fECG technique in the
clinical practice.INGEGNERIA DELL'INFORMAZIONEAgostinelli, AngelaAgostinelli, Angel
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