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Related Concept Videos

Pulse rhythm01:30

Pulse rhythm

832
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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Disturbances in Heart Rhythm01:28

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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.
Arrhythmias are categorized by their speed, rhythm, and origin. A slow...
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Design and Implementation of an Atrial Fibrillation Detection Algorithm on the ARM Cortex-M4 Microcontroller.

Marek Żyliński1, Amir Nassibi1, Danilo P Mandic1

  • 1Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK.

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Summary
This summary is machine-generated.

This study demonstrates effective atrial fibrillation detection on wearable devices using microcontrollers. The Support Vector Machine classifier achieved 96.9% accuracy, enabling efficient edge computing for cardiac arrhythmia monitoring.

Keywords:
atrial fibrillation detectionedge computingmachine learningwearable devices

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Area of Science:

  • Biomedical Engineering
  • Embedded Systems
  • Machine Learning

Background:

  • Medium-level microcontrollers can now perform edge computing, including neural network computations.
  • This enables end-to-end solutions for signal acquisition, processing, and machine learning on wearable devices.

Purpose of the Study:

  • To design and implement classifiers for atrial fibrillation detection on an ARM Cortex-M4 microcontroller.
  • To evaluate the performance of Naïve Bayes and Support Vector Machine (SVM) classifiers using the CMSIS-DSP library.
  • To develop a Python-to-C environment transfer script for machine learning models.

Main Methods:

  • Utilized PhysioNet/Computing in Cardiology Challenge 2020 data for training and evaluation.
  • Implemented Naïve Bayes and SVM classifiers with various kernels via CMSIS-DSP library.
  • Tested classifier performance on an STM32WB55RG microcontroller, focusing on heart-rate irregularity for atrial fibrillation classification.

Main Results:

  • The SVM classifier with a Radial Basis Function (RBF) kernel achieved the highest accuracy (96.9%), sensitivity (98.4%), and specificity (95.8%).
  • The RBF SVM classifier had an execution time of 720 μs per recording.
  • Demonstrated advantages of edge computing: increased power efficiency, enhanced data privacy, and reduced operational costs.

Conclusions:

  • Edge computing on microcontrollers is feasible for real-time cardiac arrhythmia detection.
  • The RBF SVM classifier offers a highly accurate and efficient solution for atrial fibrillation detection on wearable devices.
  • Further research is needed to address false-positive detection and the clinical significance of device-detected atrial fibrillation.