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Disturbances in Heart Rhythm

Disturbances in Heart Rhythm

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...

Pulse rhythm

Pulse rhythm

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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Related Experiment Video

Updated: Mar 6, 2026

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

GPU based cloud system for high-performance arrhythmia detection with parallel k-NN algorithm.

Tae Joon Jun, Hyun Ji Park, Hyuk Yoo

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|March 9, 2017

Summary

This summary is machine-generated.

This study introduces a GPU-accelerated cloud system for high-performance arrhythmia detection. The system achieves comparable accuracy while being 2.5 times faster than CPU-based methods.

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Area of Science:

Biomedical Engineering
Computer Science
Cardiology

Background:

Arrhythmia detection is crucial for cardiovascular health monitoring.
Existing methods often face performance limitations.

Purpose of the Study:

To develop a high-performance cloud system for arrhythmia detection.
To accelerate beat classification using GPU parallelization.

Main Methods:

Utilized the Pan-Tompkins algorithm for QRS detection.
Optimized beat classification with K-Nearest Neighbor (K-NN).
Parallelized the K-NN algorithm using CUDA for virtualized GPU execution.

Main Results:

Achieved a 93.5% detection rate on the MIT-BIH Arrhythmia database.
Demonstrated a 2.5 times faster execution time compared to CPU-only algorithms.
Validated the effectiveness of GPU-based cloud computing for arrhythmia analysis.

Conclusions:

The proposed GPU-based cloud system offers a significant speed improvement for arrhythmia detection.
This approach provides a scalable and efficient solution for real-time cardiovascular monitoring.
Optimized K-NN with CUDA parallelization enhances classification performance.