Cardiac inflammation and ventricular tachycardia in Chagas disease

Shapiro, Hilary; Meymandi, Sheba; Shivkumar, Kalyanam; Bradfield, Jason S.

doi:10.1016/j.hrcr.2017.05.007

Cited by 14 publications

(7 citation statements)

References 12 publications

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“…Hadeed et al proposed an automatic classification method for arrhythmia detection by implementing a robust classifier using a neural network on a Bayesian framework [9]. Shapiro et al classify electrocardiogram signals by proposing a method using a least-squares support vector machine, which can distinguish between normal and abnormal heartbeats [10]. Burchill propose a forward neural network-based backpropagation algorithm for cardiac arrhythmias by Artificial Neural Network (ANN) automatic classification recognition [11].…”

Section: Introductionmentioning

confidence: 99%

Intracavitary Electrocardiogram Guidance Aids Excavation of Rhythm Abnormalities in Patients with Occult Heart Disease

Wei

Zhu

Wen

et al. 2021

Journal of Healthcare Engineering

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In this paper, the analysis of intracavitary electrocardiograms is used to guide the mining of abnormal cardiac rhythms in patients with hidden heart disease, and the algorithm is improved to address the data imbalance problem existing in the abnormal electrocardiogram signals, and a weight-based automatic classification algorithm for deep convolutional neural network electrocardiogram signals is proposed. By preprocessing the electrocardiogram data from the MIT-BIH arrhythmia database, the experimental dataset training algorithm model is obtained, and the algorithm model is migrated into the project. In terms of system design and implementation, by comparing the advantages and disadvantages of the electrocardiogram monitoring system platform, the overall design of the system was carried out in terms of functional and performance requirements according to the system realization goal, and a mobile platform system capable of classifying common abnormal electrocardiogram signals was developed. The system is capable of long-term monitoring and can invoke the automatic classification algorithm model of electrocardiogram signals for analysis. In this paper, the functional logic test and performance test were conducted on the main functional modules of the system. The test results show that the system can run stably and monitor electrocardiogram signals for a long time and can correctly call the deep convolutional neural network-based automatic electrocardiogram signal classification algorithm to analyze the electrocardiogram signals and achieve the requirements of displaying the electrocardiogram signal waveform, analyzing the heartbeat type, and calculating the average heart rate, which achieves the goal of real-time continuous monitoring and analysis of the electrocardiogram signals.

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Section: Introductionmentioning

confidence: 99%

Intracavitary Electrocardiogram Guidance Aids Excavation of Rhythm Abnormalities in Patients with Occult Heart Disease

Wei

Zhu

Wen

et al. 2021

Journal of Healthcare Engineering

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“…Our finding is in accordance with previous reports which suggest that inflammation could be detected with 18 F-FDG PET/CT in Chagas patients. 4,5 Better understanding of the role of inflammation detected by PET/CT may provide new parameters for risk stratification for SCA.…”

Section: Discussionmentioning

confidence: 99%

Case Report: Malignant Ventricular Arrhythmias Mimicking Acute Coronary Syndrome in Chagas Disease

Moll-Bernardes

Saraiva

Oliveira

et al. 2020

The American Journal of Tropical Medicine and Hygiene

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Chronic Chagas heart disease has different clinical manifestations including arrhythmias, heart failure, and stroke. Chest pain is one of the most common symptoms and when associated with changes in the electrocardiogram, such as T-wave changes, electrically inactive areas, and segmental wall motion abnormalities, may lead to a misdiagnosis of acute coronary syndrome (ACS). Here, we describe two patients with Chagas heart disease and syncope due to sustained ventricular tachycardia who were misdiagnosed with ACS, and discuss the role of novel imaging modalities in the differential diagnosis and risk stratification.

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“… 481 , 776 , 778 , 780 , 781 In addition to fibrosis in circumscribed ventricular wall regions, the intracardiac autonomic nervous system injury, characterized by ganglionic neuronal depletion and cardiac dysautonomia, and the chronic myocardial inflammation are pathophysiological changes that can contribute to myocardial electrical instability and genesis of ventricular tachyarrhythmias. 193 , 208 , 222 , 784 - 788 …”

Section: 3 Ablation Methodsmentioning

confidence: 99%

Diretriz da SBC sobre Diagnóstico e Tratamento de Pacientes com Cardiomiopatia da Doença de Chagas – 2023

Marin-Neto¹,

Rassi²,

Oliveira³

et al. 2023

Arquivos Brasileiros De Cardiologia

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Cardiac inflammation and ventricular tachycardia in Chagas disease

Cited by 14 publications

References 12 publications

Intracavitary Electrocardiogram Guidance Aids Excavation of Rhythm Abnormalities in Patients with Occult Heart Disease

Intracavitary Electrocardiogram Guidance Aids Excavation of Rhythm Abnormalities in Patients with Occult Heart Disease

Case Report: Malignant Ventricular Arrhythmias Mimicking Acute Coronary Syndrome in Chagas Disease

Diretriz da SBC sobre Diagnóstico e Tratamento de Pacientes com Cardiomiopatia da Doença de Chagas – 2023

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