[Biometric identification method for ECG based on the piecewise linear representation (PLR) and dynamic time warping...

Area of Science:

• Biometrics
• Signal Processing
• Cardiovascular Technology

Context:

• Electrocardiogram (ECG) analysis is crucial for biometric identification.
• Existing methods face challenges with identification performance and algorithmic complexity.
• Efficient and accurate ECG-based biometrics are needed.

Purpose:

• To propose a novel Piecewise Linear Representation and Dynamic Time Warping (PLR-DTW) method for ECG biometric identification.
• To enhance identification accuracy and reduce computational complexity compared to existing algorithms.
• To validate the effectiveness of the PLR-DTW method using established ECG databases.

Summary:

• The proposed PLR-DTW method preprocesses ECG signals by denoising and detecting R peaks.
• Piecewise Linear Representation (PLR) is employed to reduce data dimensionality while preserving key signal information.
• An improved Dynamic Time Warping (DTW) algorithm is utilized for accurate similarity measurements between ECG templates and test data.

Impact:

• The PLR-DTW method demonstrates a significant improvement in accuracy, achieving nearly an 8% increase compared to the Discrete Wavelet Transform (DWT) method.
• The method offers substantial computational savings, reducing operation time by approximately 30%.
• These findings indicate that PLR-DTW provides superior performance for ECG biometric identification, offering a more efficient and accurate solution.