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Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

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An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
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Electrocardiogram Fundamentals01:28

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Introduction
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
Definition
An electrocardiogram (ECG) visualizes the heart's electrical activity by tracing the electrical movement associated with each heartbeat on a graph or monitor. As the heart beats, an electrical wave passes through it, correlating with the cardiac cycle events.
Parts of an ECG
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Dysrhythmias V: Evaluating Dysrhythmias01:30

Dysrhythmias V: Evaluating Dysrhythmias

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Dysrhythmias, also known as arrhythmias, are disturbances in the heart's rhythm that range from benign to life-threatening. A thorough evaluation is crucial for appropriate management and involves a comprehensive medical history, physical examination, and various diagnostic tests.Medical HistorySymptoms: Collect detailed information on palpitations, dizziness, syncope, chest pain, and fatigue. Note their onset, frequency, and triggers.Previous Cardiac Issues: Document any history of heart...
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Pulse rhythm01:30

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Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
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Screening for Chagas disease from the electrocardiogram using a deep neural network.

Carl Jidling1, Daniel Gedon1, Thomas B Schön1

  • 1Department of Information Technology, Uppsala University, Uppsala, Sweden.

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Deep neural networks show promise in detecting Chagas disease (ChD) from electrocardiograms (ECGs), aiding early diagnosis. While effective for chronic Chagas cardiomyopathy, performance on early-stage ChD requires further improvement with better datasets.

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Infectious Diseases

Background:

  • Chagas disease (ChD) affects over 6 million people globally, often leading to severe cardiac complications in its chronic phase.
  • Early detection of ChD is crucial for timely treatment and prevention of severe outcomes, yet current detection rates remain low.
  • Electrocardiograms (ECGs) offer a non-invasive method for cardiac assessment, potentially useful for ChD screening.

Purpose of the Study:

  • To explore the efficacy of deep neural networks (DNNs) in detecting Chagas disease (ChD) using electrocardiogram (ECG) data.
  • To develop and validate a convolutional neural network (CNN) model for identifying ChD from 12-lead ECGs.
  • To assess the model's performance in distinguishing ChD, including chronic Chagas cardiomyopathy (CCC), from control groups.

Main Methods:

  • A convolutional neural network (CNN) model was developed using over two million ECG entries from Brazilian patients (SaMi-Trop and CODE studies).
  • Model performance was evaluated on two independent external datasets: REDS-II (631 ChD patients) and ELSA-Brasil (13,739 participants).
  • Performance metrics included Area Under the Receiver Operating Characteristic Curve (AUC-ROC), sensitivity, and specificity.

Main Results:

  • The model achieved an AUC-ROC of 0.80 on the validation set and 0.68 (REDS-II) and 0.59 (ELSA-Brasil) on external validation datasets.
  • For the ELSA-Brasil dataset, sensitivity was 0.52 and specificity was 0.77 when identifying ChD.
  • When focusing on patients with Chagas cardiomyopathy, the model's AUC-ROC improved to 0.82 (REDS-II) and 0.77 (ELSA-Brasil).

Conclusions:

  • Deep neural networks can detect chronic Chagas cardiomyopathy (CCC) from ECGs, but performance is weaker for early-stage ChD.
  • The study highlights the need for larger, higher-quality datasets, as limitations were observed with self-reported labels in the CODE dataset.
  • These findings offer potential for improved ChD detection and treatment, especially in regions with high disease prevalence.