Electrocardiogram and left atrial abnormality: Design of an observational study to clarify diagnostic criteria

Lacalzada‐Almeida, Juan; Izquierdo‐Gómez, María Manuela; Laynez‐Cerdeña, Ignacio; García‐Niebla, Javier; Bruña, Vanesa; Bayés-de-Luna, Antoni; Martínez‐Sellés, Manuel

doi:10.1111/anec.12770

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…Left atrial abnormalities are associated with ischaemic stroke, and ECG can be noninvasive for assessment, especially as the P-wave index is valuable in predicting stroke. The detection indicators include P-wave duration, the morphology of P waves in lower wall leads (II, III, aVF), the terminal potential of P-wave in V1 lead (PtfV1) reflecting atrial enlargement, and P-wave area [62]. Specifically, the P-wave duration ≥120 ms, the occurrence duration of a non-biphasic P wave or biphasic P wave ≥120 ms in the lower wall leads [63], PtfV1<-40 ms•mm [64].…”

Section: Electrocardiogram (Ecg)mentioning

confidence: 99%

Atrial Failure: Mechanisms, Diagnosis, and Potential Therapeutic Targets

Li,

Liu,

Yin

et al. 2023

austinjcardiovascdisatherosclerosis

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The normal atrial function includes ventricular systolic storage function, early ventricular diastolic conduit function, and auxiliary pump function in late ventricular diastolic function, which assists in ventricular filling and maintaining cardiac output. Atrial dysfunction can result in or increase cardiac dysfunction, impacting the patient’s quality of life and life expectancy, even in the absence of evident valve or ventricular problems. Atrial failure, which mostly refers to the left atrium, has recently been given new clinical meaning and is being treated as a separate condition [1]. The mechanism, diagnosis, and potential therapeutic targets of atrial failure will be discussed in this article to better comprehend and treat atrial failure in the future.

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Section: Electrocardiogram (Ecg)mentioning

confidence: 99%

Atrial Failure: Mechanisms, Diagnosis, and Potential Therapeutic Targets

Li,

Liu,

Yin

et al. 2023

austinjcardiovascdisatherosclerosis

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show abstract

“…Es interesante ver que un estudio realizado en población general ≥ 30 años 9 también mostró que el BIA avanzado aumenta 3 veces el riesgo de fibrilación auricular. Estos datos evidencian la necesidad de realizar estudios que profundicen en el conocimiento del BIA y de su distinción respecto al crecimiento auricular izquierdo 10 . De igual modo, sería deseable que en un ensayo clínico aleatorizado se estudie el posible beneficio en la prevención de ictus y demencia de los anticoagulantes orales de acción directa en ancianos con BIA avanzado.…”

Section: Bloqueo Interauricularunclassified

Selección de lo mejor del año 2020 en cardiología geriátrica

et al. 2021

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“…In a realistic clinical setting, 12-lead ECGs (S12L-ECGs) are the standard, where a complete ECG usually contains a 12-lead signal from six limb leads (I, II, III, aVR, aVL, aVF) and six chest leads (V1, V2, V3, V4, V5, V6) ( Wilson et al, 1954 ). The different leads exhibit distinct features of ECG signals that are associated with specific types of CA ( Ashley and Niebauer, 2004 ; Lacalzada-Almeida et al, 2020 ). Therefore, the automatic calculation and analysis of the standard S12L-ECG is becoming increasingly important for medical diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Automatic Multichannel Electrocardiogram Record Classification Using XGBoost Fusion Model

Huang

Lü

2022

Front. Physiol.

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There is an increasing demand for automatic classification of standard 12-lead electrocardiogram signals in the medical field. Considering that different channels and temporal segments of a feature map extracted from the 12-lead electrocardiogram record contribute differently to cardiac arrhythmia detection, and to the classification performance, we propose a 12-lead electrocardiogram signal automatic classification model based on model fusion (CBi-DF-XGBoost) to focus on representative features along both the spatial and temporal axes. The algorithm extracts local features through a convolutional neural network and then extracts temporal features through bi-directional long short-term memory. Finally, eXtreme Gradient Boosting (XGBoost) is used to fuse the 12-lead models and domain-specific features to obtain the classification results. The 5-fold cross-validation results show that in classifying nine categories of electrocardiogram signals, the macro-average accuracy of the fusion model is 0.968, the macro-average recall rate is 0.814, the macro-average precision is 0.857, the macro-average F1 score is 0.825, and the micro-average area under the curve is 0.919. Similar experiments with some common network structures and other advanced electrocardiogram classification algorithms show that the proposed model performs favourably against other counterparts in F1 score. We also conducted ablation studies to verify the effect of the complementary information from the 12 leads and the auxiliary information of domain-specific features on the classification performance of the model. We demonstrated the feasibility and effectiveness of the XGBoost-based fusion model to classify 12-lead electrocardiogram records into nine common heart rhythms. These findings may have clinical importance for the early diagnosis of arrhythmia and incite further research. In addition, the proposed multichannel feature fusion algorithm can be applied to other similar physiological signal analyses and processing.

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Electrocardiogram and left atrial abnormality: Design of an observational study to clarify diagnostic criteria

Abstract: The criteria applied for diagnosis of left atrial (LA) abnormality using electrocardiogram (ECG) are based on various parameters including P-wave duration ≥ 120 ms, the classic Morris index (increase in

Cited by 3 publications

References 18 publications

Atrial Failure: Mechanisms, Diagnosis, and Potential Therapeutic Targets

Atrial Failure: Mechanisms, Diagnosis, and Potential Therapeutic Targets

Selección de lo mejor del año 2020 en cardiología geriátrica

Automatic Multichannel Electrocardiogram Record Classification Using XGBoost Fusion Model

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