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Related Concept Videos

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

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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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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.
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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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Dysrhythmias VI: Management of Dysrhythmias01:25

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Dysrhythmia management involves a multifaceted approach, incorporating pharmacological treatments, medical procedures, surgical interventions, lifestyle modifications, and patient education.Pharmacological ManagementAntiarrhythmic Drugs:Class I (Sodium Channel Blockers): This class includes quinidine and procainamide, which reduce the speed of impulse conduction in the heart, stabilize the cardiac membrane, and control arrhythmias. Quinidine and procainamide are Class IA agents that prolong the...
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Related Experiment Video

Updated: Oct 5, 2025

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
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Hybrid-Pattern Recognition Modeling with Arrhythmia Signal Processing for Ubiquitous Health Management.

Wei-Ting Hsiao1, Yao-Chiang Kan2, Chin-Chi Kuo3,4

  • 1Department and Institute of Health Service Administrations, China Medical University, Taichung 406040, Taiwan.

Sensors (Basel, Switzerland)
|January 22, 2022
PubMed
Summary

A new AI-powered ubiquitous health management system, ECG4UHM, accurately identifies hybrid arrhythmia patterns like atrial fibrillation and ventricular tachycardia using machine learning and Hilbert-Huang transform. This system enhances remote patient monitoring and cardiac care.

Keywords:
Hilbert–Huang transformempirical mode decompositionintrinsic mode functionmachine learningmarginal Hilbert spectrummulticlass recognitionubiquitous health management

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Signal Processing

Background:

  • Arrhythmia detection is crucial for cardiovascular health management.
  • Existing systems may lack comprehensive hybrid arrhythmia pattern recognition capabilities.
  • Ubiquitous health management (UHM) systems offer potential for continuous patient monitoring.

Purpose of the Study:

  • To develop and validate an AI-enabled UHM system (ECG4UHM) for recognizing hybrid cardiac arrhythmia patterns.
  • To integrate advanced signal processing and machine learning for accurate ECG analysis.
  • To establish a backend for intelligent social-health systems in remote care settings.

Main Methods:

  • Utilized Hilbert-Huang Transform (HHT) with empirical mode decomposition for ECG signal pre-processing.
  • Extracted HHT-based features, specifically area centroids of intrinsic mode functions' marginal Hilbert spectrum.
  • Employed machine learning models including MLP, RF, and SVM for hybrid arrhythmia pattern recognition.
  • Integrated MATLAB compiler and runtime server for AI computation within the ECG4UHM system.
  • Trained and validated models using the MIT-BIH arrhythmia open database.

Main Results:

  • Validated models achieved an Area Under the Curve (AUC) of approximately 0.99 for premature (APC-VPC) and fibril-rapid (AFib-VT) patterns against normal sinus rhythm (NSR).
  • Models for all hybrid patterns (excluding VPC vs. AFib and VT) demonstrated an average accuracy of approximately 90%.
  • Prediction tests showed average AUCs of 0.94 (NSR vs. APC) and 0.93 (APC vs. AFib, VPC, VT) for RF and SVM.
  • MLP, RF, and SVM models achieved an average accuracy and AUC of 0.98 for APC-VT detection.

Conclusions:

  • The developed ECG4UHM system effectively recognizes complex hybrid arrhythmia patterns using AI.
  • The system demonstrates high accuracy and AUC, indicating its potential for reliable cardiac monitoring.
  • ECG4UHM can serve as a robust backend for intelligent social-health systems, improving UHM and home-isolated care.