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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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Disturbances in Heart Rhythm01:29

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Arrhythmia or dysrhythmia refers to an abnormal heart rhythm caused by a defect in the heart's conduction system. It can cause the heart to beat irregularly, too quickly, or too slowly, leading to symptoms like chest pain, shortness of breath, and fainting. Factors such as stress, caffeine, alcohol, nicotine, cocaine, certain drugs, congenital defects, diseases, and electrolyte abnormalities can trigger arrhythmias.
Arrhythmias are categorized by their speed, rhythm, and origin. A slow heart...
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Mechanism of Cardiac Arrhythmias01:28

Mechanism of Cardiac Arrhythmias

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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.
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ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias01:25

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Arrhythmia is a condition characterized by an irregular heart rhythm, with ECG changes that differ based on its origin and nature. The types of arrhythmias discussed below include atrial, junctional, and ventricular arrhythmias.Atrial ArrhythmiasPremature Atrial Complexes (PACs): PACs are early atrial beats caused by stress, caffeine, alcohol, electrolyte imbalances, hypoxia, hyperthyroidism, or certain medications (e.g., bronchodilators and decongestants). The ECG shows early P waves with an...
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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.
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ECG Interpretation of Arrhythmias I: Sinus Arrhythmias01:16

ECG Interpretation of Arrhythmias I: Sinus Arrhythmias

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Arrhythmias are disturbances in the heart's rhythm that lead to abnormal heartbeats. These irregularities can originate from different parts of the heart and are classified based on their origin and nature.
Types of Arrhythmias
Sinus Node Arrhythmias
Sinus Bradycardia: Originating from the sinoatrial (SA) node, sinus bradycardia involves slower impulses, resulting in a heart rate of less than 60 beats per minute (bpm). Causes include sleep, vagal stimulation, beta-blockers, hypothyroidism,...
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Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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Investigation of Applying Machine Learning and Hyperparameter Tuned Deep Learning Approaches for Arrhythmia Detection

Kogilavani Shanmugavadivel1, V E Sathishkumar2, M Sandeep Kumar3

  • 1Department of Computer Science Engineering, Kongu Engineering College, Perundurai, Erode, 638 060 Tamil Nadu, India.

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Summary

This study introduces an advanced deep learning model for diagnosing arrhythmias, significantly improving accuracy over traditional methods. The hyperparameter-tuned Convolutional Neural Network (CNN) model enhances patient care by enabling faster and more precise detection of irregular heart rhythms.

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Accurate arrhythmia diagnosis is crucial for effective patient treatment.
  • Manual detection of arrhythmias is time-consuming and relies heavily on clinical expertise.
  • Machine learning and deep learning offer automated solutions for pattern recognition in medical datasets.

Purpose of the Study:

  • To develop an automated system for classifying different types of cardiac arrhythmias.
  • To enhance diagnostic accuracy using data augmentation and deep learning techniques.
  • To compare the performance of machine learning models against a novel hyperparameter-tuned Convolutional Neural Network (CNN).

Main Methods:

  • Data augmentation techniques were employed to expand the dataset size.
  • Machine learning models including Support Vector Machine (SVM), Naïve Bayes (NB), and Logistic Regression (LR) were utilized.
  • A baseline Convolutional Neural Network (CNN) model and a novel hyperparameter-tuned CNN model were developed for arrhythmia detection.

Main Results:

  • The hyperparameter-tuned CNN model demonstrated superior performance compared to traditional machine learning algorithms.
  • The proposed deep learning models achieved accurate classification of normal heart rhythms and five different types of arrhythmias.
  • The study highlights the effectiveness of data augmentation and optimized CNNs in medical diagnosis.

Conclusions:

  • The hyperparameter-tuned CNN model represents a significant advancement in automated arrhythmia detection.
  • This approach offers a more efficient and accurate alternative to manual diagnosis.
  • The findings support the integration of advanced AI in clinical practice for improved cardiac care.