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Instrumentation Amplifier01:25

Instrumentation Amplifier

500
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...
500
Pulse rhythm01:30

Pulse rhythm

786
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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Electrocardiogram01:29

Electrocardiogram

2.3K
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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Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

572
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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Related Experiment Video

Updated: Jun 26, 2025

Surgical Implant Procedure and Wiring Configuration for Continuous Long-Term EEG/ECG Monitoring in Rabbits
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Securing Internet-of-Medical-Things networks using cancellable ECG recognition.

Samia A El-Moneim Kabel1, Ghada M El-Banby2, Lamiaa A Abou Elazm3

  • 1Tanta High Institute of Engineering and Technology (THIET), Tanta, Egypt.

Scientific Reports
|May 13, 2024
PubMed
Summary

This study introduces a novel framework for securing Internet of Medical Things (IoMT) networks using Electrocardiogram (ECG) biometrics. The system employs cancellable templates derived from ECG signals, enhancing security against unauthorized access and data breaches.

Keywords:
Cancellable biometricsECG signalsIoMTSignal separation

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Area of Science:

  • Biometrics and Cybersecurity
  • Signal Processing and Machine Learning

Background:

  • Internet of Medical Things (IoMT) networks require robust security for sensitive patient data transmission.
  • Biometric authentication, particularly using Electrocardiogram (ECG) signals, offers a promising solution for secure access.
  • Protecting biometric templates from compromise is crucial, leading to the need for cancellable biometric systems.

Purpose of the Study:

  • To develop a comprehensive framework for ECG-based recognition with cancellable templates for IoMT network access.
  • To introduce an innovative methodology for non-invertible modification of ECG signals using blind signal separation and lightweight encryption.
  • To enhance security by enabling biometric templates to be altered if compromised.

Main Methods:

  • Utilized blind signal separation to create distorted, non-invertible versions of ECG signals by minimizing correlation with an auxiliary audio signal.
  • Applied a lightweight encryption stage using a user-specific pattern and XOR operation to the distorted ECG signals to generate cancellable templates.
  • Implemented a hybrid security model combining non-invertible transformations and lightweight encryption for enhanced security and reduced processing burden.

Main Results:

  • The proposed framework demonstrated high efficacy on the ECG-ID and MIT-BIH datasets.
  • Achieved an Equal Error Rate (EER) of 0.134 on the ECG-ID dataset and 0.4 on the MIT-BIH dataset.
  • Obtained an Area Under the Receiver Operating Characteristic curve (AROC) of 99.96% for both datasets, indicating excellent performance.

Conclusions:

  • The developed framework effectively secures IoMT networks using cancellable ECG biometrics.
  • The hybrid approach of non-invertible transformation and lightweight encryption provides a robust and efficient security solution.
  • The promising experimental results highlight the potential of this framework for real-world IoMT security applications.