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Dry contact fingertip ECG-based authentication system using time, frequency domain features and support vector

Karan Singh, Akshit Singhvi, Vinod Pathangay

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 7, 2016
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel method for biometric authentication using fingertip electrocardiogram (ECG) signals. The technique effectively removes noise and interference, achieving a 3.4% equal error rate (EER) for reliable identification.

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    Area of Science:

    • Biometrics
    • Signal Processing
    • Cardiovascular Monitoring

    Background:

    • Acquiring clear fingertip electrocardiogram (ECG) signals with dry electrodes is difficult due to noise and electromyogram (EMG) interference.
    • Existing biometric authentication methods may face challenges with signal quality and noise in wearable or contact-based systems.

    Purpose of the Study:

    • To develop and validate a robust method for biometric authentication using fingertip ECG signals.
    • To address the challenges of noise and EMG interference in dry-contact ECG acquisition for identification purposes.

    Main Methods:

    • A variance-based heuristic was employed to segment and remove noisy signal segments from fingertip ECG.
    • Baseline correction and band-pass filtering were applied to clean the ECG signal for feature extraction.
    • Support Vector Machine (SVM) classifier was trained using extracted beat features for biometric authentication.

    Main Results:

    • Optimized filter parameters and feature sets were identified for maximal classification performance.
    • The proposed method achieved a beat-level Equal Error Rate (EER) of 3.4% on the CYBHi dataset.
    • The system demonstrated effective biometric authentication capabilities using fingertip ECG signals.

    Conclusions:

    • The proposed method offers a viable approach for secure biometric authentication using readily available fingertip ECG signals.
    • Effective noise reduction and feature extraction techniques are crucial for high-performance ECG-based biometrics.
    • This research contributes to the advancement of non-invasive and convenient biometric identification systems.