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Electrocardiogram

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.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...

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Local-Global-Graph Network-Based Biokey Generation with Electrocardiogram Signal and Lightweight Authentication in

Sanjeev Kumar Adhinki Nagarathinam¹, Ramesh Naik Bhukya²

¹GITAM Deemed to be University.

Critical Reviews in Biomedical Engineering

|March 3, 2026

Summary

This study introduces LGGNet-BioKey, a novel authentication method using electrocardiogram (ECG) signals for secure cloud-based Internet of Medical Things (IoMT) systems. It enhances patient data security in telemedicine.

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Area of Science:

Biomedical Engineering
Cybersecurity
Cloud Computing

Background:

The Internet of Medical Things (IoMT) enhances telemedicine but faces security challenges.
Sensitive patient data in cloud-based IoMT requires robust authentication.
Biomedical signals like ECG offer unique, individual-specific characteristics for authentication.

Purpose of the Study:

To develop and evaluate a novel authentication approach for cloud-based IoMT systems.
To enhance the security and privacy of patient health information.
To utilize electrocardiogram (ECG) signals for unique biometric key generation.

Main Methods:

Simulation of a cloud-based IoMT network with cloud server, gateway, and patient entities.
Implementation of local-global-graph network (LGGNet) for key generation.
BioKey generation using electrocardiogram (ECG) signals for authentication.
Attribute-based encryption and decryption for data preservation.

Main Results:

The LGGNet-BioKey model achieved efficient performance.
Measured execution time: 3.772 seconds.
Measured memory usage: 9.096 MB.
Measured key generation time: 3.771 seconds.

Conclusions:

LGGNet-BioKey provides a secure and efficient authentication solution for cloud-based IoMT.
The use of ECG signals offers a promising avenue for biometric authentication in healthcare.
The proposed method contributes to safeguarding sensitive patient data in telemedicine applications.