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Related Concept Videos

Instrumentation Amplifier01:25

Instrumentation Amplifier

An electrocardiography (ECG) machine is an essential piece of medical equipment used to monitor the electrical activity of the heart. It operates by detecting small electrical changes on the skin that result from the depolarization of the heart muscle during each heartbeat. However, these signals are in the microvolt range and can be easily overwhelmed by noise or interference.
To overcome this challenge, an ECG machine utilizes an instrumentation amplifier. This specialized amplifier is...
Electrocardiogram01:29

Electrocardiogram

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.
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Electrocardiogram Fundamentals

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 to...
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...

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A novel algorithm for Bluetooth ECG.

Utpal T Pandya1, Uday B Desai

  • 1Department of Instrumentation and Control Engineering, Sarvajanik College of Engineering and Technology, Surat 395001, India. utpal.pandya@scet.ac.in

IEEE Transactions on Bio-Medical Engineering
|September 13, 2012
PubMed
Summary

A new algorithm, peak rejection adaptive sampling modified moving average (PRASMMA), improves wireless electrocardiogram (ECG) transmission by reducing noise and data errors. This enhances ECG reconstruction for better remote patient monitoring and diagnostics.

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Wireless Communications

Background:

  • Wireless transmission of electrocardiogram (ECG) data faces challenges including data latency, battery power limitations, and transmission distance constraints.
  • Existing ECG monitoring systems are susceptible to various noise sources, including wireless transmission errors and baseline drift, impacting diagnostic accuracy.
  • Effective noise reduction and data integrity are crucial for reliable remote ECG monitoring in personalized care and home healthcare applications.

Purpose of the Study:

  • To introduce a novel filtering algorithm, the peak rejection adaptive sampling modified moving average (PRASMMA), designed to enhance the quality of wirelessly transmitted ECG signals.
  • To address joint issues of wireless transmission errors, ECG measurement noise, and baseline drift in remote monitoring scenarios.
  • To adapt filtering parameters based on different ECG signal acquisition sampling rates.

Main Methods:

  • Development and implementation of the PRASMMA algorithm, which includes error correction for bit patterns, baseline drift removal, and a modified moving average filter excluding QRS complex regions.
  • Utilizing a prototyped Bluetooth-based ECG module for wireless data acquisition at various sampling rates and patient positions.
  • Wireless transmission of ECG data to Bluetooth-enabled devices for PRASMMA algorithm application and performance comparison with Moving Average and S-Golay algorithms.

Main Results:

  • The PRASMMA algorithm effectively removes noise and corrects errors in wirelessly transmitted ECG data.
  • Visual and numerical comparisons demonstrate superior ECG reconstruction quality with PRASMMA compared to Moving Average and S-Golay algorithms.
  • The algorithm's performance is validated across different sampling rates and patient positions, indicating robustness.

Conclusions:

  • The PRASMMA algorithm significantly improves ECG reconstruction fidelity in wireless transmission by efficiently mitigating noise and artifacts.
  • The developed algorithm shows potential for broad application in diagnostic parameters where accurate peak detection is critical.
  • PRASMMA offers a viable solution for enhancing the reliability of wireless ECG monitoring in telemedicine and personalized healthcare.