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Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

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An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...
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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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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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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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Electrocardiogram Fundamentals01:28

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Introduction
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
Definition
An electrocardiogram (ECG) visualizes the heart's electrical activity by tracing the electrical movement associated with each heartbeat on a graph or monitor. As the heart beats, an electrical wave passes through it, correlating with the cardiac cycle events.
Parts of an ECG
An ECG utilizes electrodes on the skin...
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Disturbances in Heart Rhythm01:29

Disturbances in Heart Rhythm

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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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Efficient Deep Learning-Based Arrhythmia Detection Using Smartwatch ECG Electrocardiograms.

Herwin Alayn Huillcen Baca1, Flor de Luz Palomino Valdivia1

  • 1Faculty of Engineering, Academic Department of Engineering and Information Technology, Jose Maria Arguedas National University, Andahuaylas 03701, Peru.

Sensors (Basel, Switzerland)
|September 13, 2025
PubMed
Summary

This study introduces an efficient 1D CNN model for detecting cardiac arrhythmias from smartwatch ECGs. The model demonstrates high accuracy in multiclass detection, supporting early diagnosis and clinical application.

Keywords:
CNNECGPPGarrhythmia detectioncardiovascular diseasesdeep learningelectrocardiogramheart ratesmartwatch

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Healthcare
  • Cardiology

Background:

  • Cardiovascular diseases, including cardiac arrhythmias, are a leading global cause of death.
  • Early and accurate diagnosis of arrhythmias is critical but challenged by ECG interpretation subjectivity and noise.
  • Current deep learning models for arrhythmia detection often neglect efficiency and clinical applicability, focusing solely on open datasets.

Purpose of the Study:

  • To propose an efficient 1D Convolutional Neural Network (CNN) model for detecting cardiac arrhythmias using electrocardiograms (ECGs) from smartwatches.
  • To develop a model suitable for practical clinical deployment for continuous monitoring and early arrhythmia detection.
  • To evaluate the model's efficiency and effectiveness on both binary and multiclass arrhythmia detection tasks.

Main Methods:

  • Developed an efficient 1D CNN architecture for smartwatch ECG-based arrhythmia detection.
  • Trained and evaluated a binary arrhythmia detection model using the UMass Medical School Simband dataset.
  • Validated a multiclass arrhythmia detection model using the MIT-BIH arrhythmia database and compared it with state-of-the-art methods.

Main Results:

  • The binary model achieved 64.81% accuracy, 89.47% sensitivity, and 6.25% specificity, highlighting its reliability, particularly in specificity.
  • The model demonstrated computational efficiency with 1.2 million parameters and 68.48 MFlops.
  • The multiclass model achieved high performance with 99.57% accuracy, 99.57% sensitivity, and 99.47% specificity, positioning it among the best state-of-the-art proposals.

Conclusions:

  • The proposed 1D CNN model is efficient and reliable for detecting cardiac arrhythmias from smartwatch ECGs.
  • The model's performance, especially in multiclass detection, supports its potential for practical clinical application in early arrhythmia diagnosis and monitoring.
  • This work addresses the gap in efficient deep learning models for real-world arrhythmia detection using wearable technology.