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Related Concept Videos

Disturbances in Heart Rhythm01:29

Disturbances in Heart Rhythm

1.4K
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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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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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...
148
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...
130
Mechanism of Cardiac Arrhythmias01:28

Mechanism of Cardiac Arrhythmias

1.1K
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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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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Related Experiment Video

Updated: Sep 30, 2025

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
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Compressed Deep Learning to Classify Arrhythmia in an Embedded Wearable Device.

Kwang-Sig Lee1, Hyun-Joon Park2, Ji Eon Kim3

  • 1AI Center, Korea University Anam Hospital, Seoul 02841, Korea.

Sensors (Basel, Switzerland)
|March 10, 2022
PubMed
Summary
This summary is machine-generated.

This study developed an efficient deep learning model for wearable devices to detect asymptomatic atrial fibrillation using ECG data. Mobilenet demonstrated superior efficiency over Resnet for arrhythmia classification in embedded systems.

Keywords:
MobilenetResnetarrhythmiacompressed deep learningembedded wearable device

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Healthcare
  • Cardiology

Background:

  • 15-30% of atrial fibrillation patients are asymptomatic, missing traditional diagnostics.
  • Asymptomatic patients face risks of stroke and heart failure without timely intervention.
  • COVID-19 exacerbates reluctance for hospital visits, highlighting the need for remote monitoring.

Purpose of the Study:

  • To develop an efficient deep learning model for arrhythmia classification in embedded wearable devices.
  • To address the diagnostic gap for asymptomatic atrial fibrillation.
  • To enable automatic detection and alarming for early intervention.

Main Methods:

  • Utilized ECG data from 28,308 patients (Korea University Anam Hospital).
  • Applied deep learning models (Resnets, Mobilenets) with model compression (TensorFlow Lite).
  • Compared model performance and resource efficiency for embedded deployment.

Main Results:

  • Compressed models achieved significant weight reduction (743 MB to 76 KB) with minimal performance loss.
  • Resnet and Mobilenet showed comparable accuracy (e.g., Resnet-100 Hz at 98.2%, Mobilenet-100 Hz at 97.9%).
  • Mobilenet models exhibited lower flash memory usage and faster inference times than Resnets.

Conclusions:

  • Mobilenet is a more efficient model than Resnet for classifying arrhythmia in embedded wearable devices.
  • Deep learning with model compression enables effective arrhythmia detection on resource-constrained devices.
  • This technology holds promise for early detection and prevention of complications in asymptomatic atrial fibrillation patients.