[Paper Review] An IoT Real-Time Biometric Authentication System Based on ECG Fiducial Extracted Features Using Discrete Cosine Transform

Qais Ahmed Habash, Abbas K. AlZubaidi+2 more|arXiv (Cornell University)|Aug 28, 2017

ECG Monitoring and Analysis6 references19 citations

TL;DR

This paper proposes an IoT-based real-time biometric authentication system using ECG fiducial features extracted via Discrete Cosine Transform (DCT). By leveraging DCT for efficient feature extraction, the system achieves 97.78% accuracy with a processing time of just 1.21 seconds, making it suitable for time-sensitive applications requiring high reliability and low latency.

ABSTRACT

The conventional authentication technologies, like RFID tags and authentication cards/badges, suffer from different weaknesses, therefore a prompt replacement to use biometric method of authentication should be applied instead. Biometrics, such as fingerprints, voices, and ECG signals, are unique human characters that can be used for authentication processing. In this work, we present an IoT real-time authentication system based on using extracted ECG features to identify the unknown persons. The Discrete Cosine Transform (DCT) is used as an ECG feature extraction, where it has better characteristics for real-time system implementations. There are a substantial number of researches with a high accuracy of authentication, but most of them ignore the real-time capability of authenticating individuals. With the accuracy rate of 97.78% at around 1.21 seconds of processing time, the proposed system is more suitable for use in many applications that require fast and reliable authentication processing demands.

Motivation & Objective

• To address the limitations of conventional authentication methods like RFID and badges, which are prone to theft and duplication.
• To develop a real-time biometric authentication system suitable for IoT environments with low processing delay.
• To improve upon existing ECG-based systems by emphasizing real-time performance without sacrificing accuracy.
• To explore the feasibility of using Discrete Cosine Transform (DCT) for efficient ECG feature extraction in embedded and IoT platforms.

Proposed method

• ECG signals are collected from users using wearable sensors in an IoT-enabled environment.
• Fiducial points (R-peaks, P-waves, T-waves) are detected from the ECG signals to extract biometric features.
• Discrete Cosine Transform (DCT) is applied to the segmented ECG signals to convert time-domain data into frequency-domain coefficients for feature representation.
• The DCT coefficients are used as input to a classification model for user identification.
• The system is designed for real-time operation, with processing time measured from signal acquisition to authentication decision.
• The authentication pipeline is optimized for low computational overhead, suitable for deployment on resource-constrained IoT devices.

Experimental results

Research questions

• RQ1 Can DCT-based feature extraction from ECG fiducial points enable real-time biometric authentication in IoT systems?
• RQ2 What is the trade-off between authentication accuracy and processing time in ECG-based biometric systems?
• RQ3 How does the proposed system compare to existing ECG authentication methods in terms of speed and reliability?
• RQ4 To what extent can DCT reduce computational complexity while preserving discriminative features for user identification?

Key findings

• The proposed system achieves an authentication accuracy of 97.78% on the tested dataset.
• The average processing time per authentication request is 1.21 seconds, demonstrating strong real-time performance.
• DCT-based feature extraction reduces computational load, making the system suitable for deployment on low-power IoT devices.
• The use of fiducial points enhances feature stability and discriminability across different ECG cycles.
• The system outperforms many existing ECG-based methods in terms of processing speed while maintaining high accuracy.
• The integration of DCT with fiducial-based ECG analysis provides a robust and efficient solution for real-time biometric authentication in IoT environments.