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Related Concept Videos

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

Pulse rhythm

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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.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
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Holter Monitor: 24-Hour Monitoring01:23

Holter Monitor: 24-Hour Monitoring

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Holter monitoring is a continuous electrocardiography (ECG) recording that tracks the heart's electrical activity over an extended period, generally 24 to 48 hours. This noninvasive diagnostic tool detects irregular heart rhythms that may not be captured during a standard ECG performed in a clinical setting.DeviceThe Holter monitor is a portable, small device connected to several electrodes on the patient's chest. These electrodes detect the heart's electrical signals and transmit them to the...
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Mechanism of Cardiac Arrhythmias01:28

Mechanism of Cardiac Arrhythmias

943
Arrhythmias are irregular heart rhythms occurring when the heart's electrical impulses become abnormal. These disturbances can lead to various symptoms, depending on their severity and the underlying cause. Some common factors contributing to arrhythmias include hypoxia, ischemia, electrolyte imbalances, excessive catecholamine exposure, drug toxicity, and muscle overstretching. Arrhythmias can be classified into two main types based on the rate and site of origin of abnormal heart rhythms.
943
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

627
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
An ECG utilizes electrodes on the skin...
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Related Experiment Video

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Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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Single-lead arrhythmia detection through machine learning: cross-sectional evaluation of a novel algorithm using

Henry Mitchell1, Nicole Rosario1, Carme Hernandez1,2,3

  • 1Divison of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA.

Open Heart
|September 21, 2023
PubMed
Summary

A new hybrid machine learning model accurately classifies common cardiac arrhythmias from single-lead ECGs in acutely ill patients. This advanced algorithm shows high sensitivity and specificity, improving arrhythmia detection in real-world settings.

Keywords:
ARRHYTHMIASArrhythmias, CardiacElectrocardiography

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Informatics

Background:

  • Computer-assisted interpretation of single-lead electrocardiograms (ECG) is crucial for identifying arrhythmias in acutely ill patients.
  • There is a need for rigorous evaluation of novel ECG interpretation algorithms using external, real-world data.

Purpose of the Study:

  • To evaluate the performance of a hybrid machine learning model in classifying eight common cardiac arrhythmias from single-lead ECG signals.
  • To assess the model's accuracy in a real-world setting with acutely ill patients.

Main Methods:

  • A cross-sectional, external, retrospective evaluation was conducted.
  • A previously trained hybrid machine learning model was tested against an ECG reading team.
  • Data from 423 patients admitted to two hospitals between June 2017 and November 2019 were analyzed.

Main Results:

  • The model analyzed over 2.6 million minutes of single-lead ECG data.
  • For any arrhythmia, the model achieved 98% sensitivity, 100% specificity, and 98% accuracy.
  • The overall F1 Score was 99%, with high performance for pauses (99%) and paroxysmal supraventricular tachycardia (92%).

Conclusions:

  • A hybrid machine learning model demonstrated effectiveness in classifying common cardiac arrhythmias.
  • The model shows high accuracy and reliability for single-lead ECG interpretation in real-world clinical data.
  • This technology holds promise for improving arrhythmia detection in acutely ill patients.