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Related Concept Videos

Holter Monitor: 24-Hour Monitoring01:23

Holter Monitor: 24-Hour Monitoring

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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Assessment of apical radial pulse01:25

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Related Experiment Video

Updated: May 14, 2026

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
05:03

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Published on: December 11, 2019

Atrial fibrillation detection using a smart phone.

Jinseok Lee1, Bersain A Reyes, David D McManus

  • 1WPI, Worcester, MA 01609, USA.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|February 1, 2013
PubMed
Summary
This summary is machine-generated.

An iPhone 4s camera can detect atrial fibrillation (AF) using photoplethysmogram (PPG) signals. This mobile health approach achieved 100% accuracy in distinguishing AF from normal sinus rhythm (NSR).

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Area of Science:

  • Biomedical Engineering
  • Cardiology
  • Mobile Health Technology

Background:

  • Atrial fibrillation (AF) is a common arrhythmia requiring accurate detection.
  • Existing AF detection methods can be costly and inaccessible.
  • Smartphone-based photoplethysmogram (PPG) analysis presents a potential low-cost screening tool.

Purpose of the Study:

  • To evaluate the capability of an iPhone 4s camera to detect atrial fibrillation (AF).
  • To assess the accuracy of statistical methods for AF detection using smartphone-acquired PPG signals.

Main Methods:

  • Recruited 25 subjects with AF, collecting 2-minute PPG time series using an iPhone 4s camera.
  • Analyzed PPG signals using Root Mean Square of Successive Differences (RMSSD), Shannon entropy (ShE), and Sample entropy (SampE).
  • Calculated beat-to-beat accuracy and overall AF/NSR detection accuracy.

Main Results:

  • Beat-to-beat accuracies for RMSSD, ShE, and SampE were 0.9844, 0.8494, and 0.9552, respectively.
  • Achieved 100% accuracy in distinguishing between AF and normal sinus rhythm (NSR).

Conclusions:

  • An iPhone 4s camera can effectively detect atrial fibrillation (AF) using PPG signals.
  • Smartphone-based PPG analysis demonstrates high accuracy for clinical AF/NSR classification.
  • This technology offers a promising, accessible tool for AF screening and monitoring.