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Related Concept Videos

Seizures: Classification01:13

Seizures: Classification

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Epilepsy is primarily characterized by unpredictable seizures, either provoked by an identifiable factor, such as injury or illness, or unprovoked, occurring spontaneously without apparent cause.
Seizures are typically classified into two main categories: focal and generalized seizures.
Focal Seizures
Focal seizures originate from specific regions of the brain. These seizures are further sub-classified into two types:
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Dysrhythmias II: Classification of Tachyarrhythmias01:28

Dysrhythmias II: Classification of Tachyarrhythmias

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Tachyarrhythmias are a type of dysrhythmia where the heart rate exceeds 100 beats per minute. Here are some common types of tachyarrhythmias:Sinus TachycardiaSinus tachycardia originates from increased impulses from the sinus node, leading to an elevated heart rate. It is often triggered by stress, fever, or exercise.Patients may experience palpitations, a sensation of a racing heart, dizziness, and chest discomfort.Causes and Risk Factors: Common causes include physical exertion, emotional...
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Heart Failure IV: Classification and Diagnostic Evaluation01:30

Heart Failure IV: Classification and Diagnostic Evaluation

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Heart failure can be classified in various ways, with the most common classifications based on physical activity limitations, disease progression, severity, and treatment strategies.The Functional Classification of Heart Failure divides patients into four categories based on physical activity limitation due to symptom burden.Class I: Patients in this class have cardiac disease but no physical activity limitations. Ordinary activities like walking, climbing stairs, or routine tasks do not cause...
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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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Classification of Neurotransmitters01:30

Classification of Neurotransmitters

4.0K
Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
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Dysrhythmias III: Characteristics of Dysrhythmias01:29

Dysrhythmias III: Characteristics of Dysrhythmias

155
Dysrhythmias, also known as arrhythmias, are irregular heart rhythms that result from abnormal electrical activity in the heart, affecting its ability to circulate blood efficiently. Tachyarrhythmias, a subset of dysrhythmias, are characterized by abnormally fast heart rates exceeding 100 beats per minute. Here are some types of tachyarrhythmias with their distinct ECG features:Sinus Tachycardia:Sinus tachycardia presents a regular heart rhythm with an increased rate of 101-180 beats per...
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Tilt Testing with Combined Lower Body Negative Pressure: a "Gold Standard" for Measuring Orthostatic Tolerance
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Diagnosing Neurally Mediated Syncope Using Classification Techniques.

Shahadat Hussain1, Zahid Raza1, T V Vijay Kumar1

  • 1School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi 110067, India.

Journal of Clinical Medicine
|November 13, 2021
PubMed
Summary
This summary is machine-generated.

Machine learning accurately diagnoses neurally mediated syncope using head-up tilt test data. This approach aids in early detection and proactive treatment of syncope, improving patient outcomes.

Keywords:
classificationhead-up tilt (HUT) testmachine learningneuro mediated syncope

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Area of Science:

  • Cardiology
  • Medical Diagnostics
  • Artificial Intelligence in Healthcare

Background:

  • Syncope, characterized by transient loss of consciousness, presents diagnostic challenges due to symptom overlap with conditions like seizures and stroke.
  • Healthcare 4.0 and AI leverage historical data for disease diagnosis, offering new avenues for complex medical conditions.
  • Neurally mediated syncope, triggered by neurocardiogenic or cardiac factors, requires precise diagnostic methods.

Purpose of the Study:

  • To apply classification-based machine learning for diagnosing neurally mediated syncope.
  • To evaluate the effectiveness of AI in syncope diagnosis using clinical data.
  • To facilitate early diagnosis and proactive treatment strategies for syncope.

Main Methods:

  • Utilized classification-based machine learning algorithms.
  • Employed data collected from head-up tilt tests in a clinical setting.
  • Focused on diagnosing neurally mediated syncope based on specific triggers.

Main Results:

  • Demonstrated the effectiveness of machine learning in diagnosing syncope.
  • Showcased the utility of AI in analyzing clinical data for syncope detection.
  • Validated the potential for early and proactive syncope management.

Conclusions:

  • Classification-based machine learning is a viable tool for syncope diagnosis.
  • AI-driven approaches can significantly improve the accuracy and timeliness of syncope diagnosis.
  • Early diagnosis through machine learning facilitates proactive treatment and better patient management for neurally mediated syncope.