2022 HRS expert consensus statement on evaluation and management of arrhythmic risk in neuromuscular disorders

Groh, William J.; Bhakta, Deepak; Tomaselli, Gordon F.; Aleong, Ryan G.; Teixeira, Ricardo Alkmim; Amato, Anthony A.; Asirvatham, Samuel J.; Cha, Yong‐Mei; Corrado, Domenico; Duboc, Denis; Goldberger, Zachary D.; Horie, Minoru; Hornyak, Joseph E.; Jefferies, John L.; Kääb, Stefan; Kalman, Jonathan M.; Kertesz, Naomi J.; Lakdawala, Neal K.; Lambiase, Pier D.; Lubitz, Steven A.; McMillan, Hugh J.; McNally, Elizabeth M.; Milone, Margherita; Namboodiri, Narayanan; Nazarian, Saman; Patton, Kristen K.; Russo, Vincenzo; Sacher, Frédéric; Shen, Win‐Kuang; Filho, Dario C. Sobral; Stambler, Bruce S.; Stöllberger, Claudia; Wahbi, Karim; Wehrens, Xander H.T.; Weiner, Menachem M.; Wheeler, Matthew T.; Zeppenfeld, Katja

doi:10.1016/j.hrthm.2022.04.022

Cited by 26 publications

(20 citation statements)

References 227 publications

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“…Similarly, AV nodal involvement did not appear to be associated with cardiac surgical intervention and was seen in patients with and without surgical palliation. While there are a number of genetic and CHD phenotypes in which AV nodal disease can be rapidly progressive and early intervention is prudent, this was not the case in this cohort during infancy (Groh et al, 2022;Joosten et al, 2021;Vacca et al, 2014). Whether this mild conduction system disease is…”

Section: Discussionmentioning

confidence: 75%

Incidence and outcome of arrhythmias and electrical disease in patients with Trisomy 18

Czosek

Baskar

Mohan

et al. 2023

American J of Med Genetics Pt A

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Patients with Trisomy 18 have a high incidence of cardiac anomalies and are associated with early death. Because of early mortality, electrical system disease and arrhythmia has been difficult to delineate and the incidence remain unknown. We sought to describe the association and clinical outcomes of electrical system disease and cardiac tachy‐arrhythmias in patients with Trisomy 18. This was a retrospective, single institutional study. All patients with Trisomy 18 were included in the study. Patient characteristics, congenital heart disease (CHD), conduction system and clinical tachy‐arrhythmia data were collected on all patients. Outcomes including cardiac surgical interventions, electrical system interventions and death were collected until the time of study. Patients with tachy‐arrhythmias/electrical system involvement were compared to those without to identify potential associated variables. A total of 54 patients with Trisomy 18 were included in analysis. The majority of patients was female and had associated CHD. AV nodal conduction system abnormalities with either first or second degree AV block were common (15%) as was QTc prolongation (37%). Tachy‐arrhythmias were common with 22% of patients having at least one form of tachy‐arrhythmia and associated with concomitant conduction system disease (p = 0.002). Tachy‐arrhythmias were typically treatable with monitoring or medication with eventual resolution without need for procedural intervention. Although early death was common, there were no causes of death associated with tachy‐arrhythmia or conduction system disease. In conclusion, patients with Trisomy 18 have a high incidence of conduction system abnormalities and burden of clinical tachy‐arrhythmias. Although frequent, electrical system disease did not affect patient outcome or difficultly of care delivery.

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Section: Discussionmentioning

confidence: 75%

Incidence and outcome of arrhythmias and electrical disease in patients with Trisomy 18

Czosek

Baskar

Mohan

et al. 2023

American J of Med Genetics Pt A

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“…Although NMDs share the common cardiac electrophysiological changes presenting in ECGs, diversity exists among different NMDs [ 31 ], providing indications for physicians to choose optimal management according to the type of NMD and abnormality in primary examinations. For example, an invasive, or even repeated electrophysiology study could be essential for selected patients with DM1 from the time of diagnosis regardless of symptoms in guiding valuable prophylactic pacing, and QT prolongation will require specific close follow-up with ECG, Holter, or implantable device monitoring for sudden death prevention.…”

Section: Discussionmentioning

confidence: 99%

Subclinical cardiac involvement present as electrocardiographic abnormalities in various neuromuscular diseases

Xiong

Liu

et al. 2023

Heliyon

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“…The filtered ECG signal is obtained by convolving the original ECG signal with a bandpass filter function h(t) which is defined as : h(t)= (1/T) * rect((t-T/2)/T) * (sin(2πfct)/(πfct)) (1) where rect(x) = 1 for |x|<0.5 and 0 otherwise, Differentiation:…”

Section: ) Phase 1: Realtime Ctg and Fecg Data Collectionmentioning

confidence: 99%

“…It is typically performed during the latter part of pregnancy to evaluate fetal well-being and detect abnormalities, such as fetuses at risk for distress, which can lead to poor outcomes such as stillbirth or neonatal death. On the other hand, FECG is an invasive technique that records the electrical activity of the fetal heart and is usually performed during the third trimester of pregnancy [1]. The process of fetal electrocardiogram (FECG) involves inserting a small electrode into the amniotic fluid surrounding the fetus, which records the electrical activity of the fetal heart to detect any abnormalities in the fetal heart rate (FHR).…”

Section: Introductionmentioning

confidence: 99%

A Real-time ECG CTG based Ensemble Feature Extraction and Unsupervised Learning based Classification Framework for Multi-class Abnormality Prediction

Aditya¹,

Devi²,

Prasad³

2023

IJACSA

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Cardiovascular diseases (CVDs) are a leading cause of death worldwide. Early detection and diagnosis of these diseases can greatly reduce complications and improve outcomes for high-risk individuals. One method for detecting CVDs is through the use of electrocardiogram (ECG) monitoring systems, which use various technologies such as the Internet of Things (IoT), mobile applications, wireless sensor networks (WSN), and wearable devices to acquire and analyze ECG data for early diagnosis. However, despite the prevalence of these systems in the literature, there is a need for further optimization and improvement of their classification accuracy. In an effort to address this challenge, a novel heterogeneous unsupervised learning model for real-time ECG classification was proposed. The main goal of this work was to reduce the error rate and improve the classification accuracy of the system. This study presents a framework for the classification of multi-class abnormalities in electrocardiograms (ECGs) using an ensemble feature extraction technique and unsupervised learning. The framework utilizes a real-time electrocardiogramcardiotocography (ECG-CTG) system to extract features from the ECG signal, and then employs an ensemble of feature extraction techniques to enhance the discrimination of the extracted features. The extracted features are then used in an unsupervised learning-based classification algorithm to classify the ECG signals into different classes of abnormalities. The proposed framework is evaluated on a dataset of ECG signals and the results show that it can effectively classify ECG signals with high accuracy and low computational complexity.

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2022 HRS expert consensus statement on evaluation and management of arrhythmic risk in neuromuscular disorders

Cited by 26 publications

References 227 publications

Incidence and outcome of arrhythmias and electrical disease in patients with Trisomy 18

Incidence and outcome of arrhythmias and electrical disease in patients with Trisomy 18

Subclinical cardiac involvement present as electrocardiographic abnormalities in various neuromuscular diseases

A Real-time ECG CTG based Ensemble Feature Extraction and Unsupervised Learning based Classification Framework for Multi-class Abnormality Prediction

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