ECG Signal Authentication Using SVD Noise Filtering
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Solution Overview
Problem
Current user authentication methods based on electrocardiogram (ECG) signals lack accuracy due to variations in heartbeat patterns caused by respiration and noise, leading to high false acceptance and rejection rates.
Innovation Solution
The proposed method involves preprocessing ECG signals by aligning them based on R peaks, normalizing their time periods, eliminating low-correlation components, and extracting dominant error components using singular value decomposition (SVD) to create a unique pattern for authentication, which reduces noise and improves signal-to-noise ratio.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ECG signals are used directly for authentication, then the authentication process is simple, but the accuracy is low due to variations in heartbeat patterns caused by respiration and noise
Solution Approach 1:
The patent segments the ECG signal processing into distinct stages: preprocessing to remove noise, feature extraction to identify characteristic components, and pattern recognition to establish unique authentication signatures. This segmentation allows each stage to be optimized independently, improving overall accuracy while managing complexity through modular processing steps.
Solution Approach 2:
The patent applies preliminary preprocessing actions to ECG signals before authentication, including noise filtering, baseline correction, and normalization. These preliminary actions remove respiratory and motion artifacts that would otherwise degrade authentication accuracy, ensuring that the subsequent pattern recognition operates on cleaned, standardized signal data.
2Reliability
If noise and respiration variations are not removed, then the processing is fast, but the false acceptance and rejection rates are high
Solution Approach 1:
The patent extracts and removes noise components and respiration-related variations from the ECG signal through filtering and decomposition techniques. By separating these interfering elements from the genuine cardiac signal, the system achieves higher authentication reliability and lower false acceptance/rejection rates while maintaining efficient processing through targeted removal rather than comprehensive reprocessing.
3Measurement precision
If traditional ECG authentication methods are used, then the implementation is straightforward, but the false acceptance and rejection rates remain high
Solution Approach 1:
The patent transforms the ECG signal by changing key parameters including time normalization to account for varying heart rates, amplitude scaling to standardize signal strength, and frequency domain transformation to separate signal components. These parameter changes enhance the distinctive features of individual ECG patterns, improving authentication accuracy while providing a systematic framework that can be implemented through standard signal processing algorithms.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the accuracy of user authentication by reducing false acceptance and rejection rates, ensuring a more robust and reliable authentication process.
Implementation Method 1
a preprocessor filters a first electrocardiogram (ECG) signal of a user to be registered, and a second ECG signal of the user
Implementation Method 2
preprocessing ECG signals by aligning them based on R peaks, normalizing their time periods
Implementation Method 3
extracting dominant error components using singular value decomposition (SVD) to create a unique pattern for authentication
Data Source
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AI summary
An authentication apparatus includes an electrocardiogram (ECG) signal receiver configured to receive a target ECG signal, and a preprocessor configured to filter the target ECG signal. The apparatus further includes an authenticator configured to process the filtered target ECG signal based on a pattern of a reference ECG signal, and determine whether the target ECG signal corresponds to the reference ECG signal based on the processing.