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Related Concept Videos

Disturbances in Heart Rhythm01:28

Disturbances in Heart Rhythm

981
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...
981
Dysrhythmias II: Classification of Tachyarrhythmias01:28

Dysrhythmias II: Classification of Tachyarrhythmias

10
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...
10
Pulse rhythm01:30

Pulse rhythm

815
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...
815

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Automated Arrhythmia Classification Using Farmland Fertility Algorithm with Hybrid Deep Learning Model on Internet of

Ahmed S Almasoud1, Hanan Abdullah Mengash2, Majdy M Eltahir3

  • 1Department of Information Systems, College of Computer and Information Sciences, Prince Sultan University, Riyadh 12435, Saudi Arabia.

Sensors (Basel, Switzerland)
|October 14, 2023
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Summary
This summary is machine-generated.

This study introduces an automated arrhythmia classification system using hybrid deep learning and the Farmland Fertility Algorithm within an IoT platform. The approach enhances remote patient monitoring and early detection of abnormal heart rhythms.

Keywords:
ECG signalsInternet of Thingsarrhythmia classificationdeep learningremote monitoring

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Area of Science:

  • Biomedical Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Internet of Things (IoT) enables centralized health record management.
  • Electrocardiogram (ECG) is crucial for diagnosing heart conditions.
  • Manual ECG analysis for arrhythmia is time-consuming and challenging.

Purpose of the Study:

  • To present an automated arrhythmia classification system using a hybrid deep learning and Farmland Fertility Algorithm (AAC-FFAHDL) on an IoT platform.
  • To improve the efficiency and accuracy of arrhythmia detection and classification.
  • To enable remote patient care and continuous monitoring of heart conditions.

Main Methods:

  • Data pre-processing to standardize ECG signals.
  • Hybrid Deep Learning (HDL) for arrhythmia detection and classification.
  • Farmland Fertility Algorithm (FFA) for hyperparameter tuning of the HDL model.
  • Validation using a benchmark ECG database.

Main Results:

  • The AAC-FFAHDL system demonstrated promising performance in automated arrhythmia classification.
  • Achieved superior results compared to other models across various evaluation metrics.
  • Efficiently diagnosed arrhythmia through hyperparameter-tuned deep learning models.

Conclusions:

  • The proposed AAC-FFAHDL approach offers an effective solution for automated arrhythmia classification in IoT environments.
  • Highlights the potential of integrating advanced AI algorithms with IoT for improved cardiac care.
  • Supports remote patient monitoring and early detection of heart rhythm abnormalities.