ECG Personal Authentication Using CNN Pattern Recognition
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Solution Overview
Problem
Existing ECG biometric systems face challenges in reliable pattern recognition due to the temporal and varying nature of ECG signals, leading to high error rates and inefficiencies in personal authentication, especially when heart activity deviates from the ideal pattern.
Innovation Solution
The use of Convolutional Neural Networks (CNNs) for processing ECG signals, which learns to differentiate between individuals by ignoring noise and focusing on pattern recognition, combined with a deep learning framework that extracts unique features and transforms them into a representative encoded form for verification, allowing for high accuracy in authentication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional ECG biometric methods (fiducial points, morphology, local binary patterns) are used for personal authentication, then the system can operate with few heartbeats and maintain computational efficiency, but the error rate increases significantly (EER ranging from 0.03% to 12.63%)
Solution Approach 1:
The patent replaces conventional signal processing methods (fiducial point detection, morphology analysis, local binary patterns) with a neural network-based system. The neural network learns temporal patterns directly from raw ECG signals, substituting manual feature extraction and classification mechanisms with an adaptive learning system that achieves superior accuracy while maintaining efficiency.
Solution Approach 2:
The invention changes the fundamental parameters of ECG analysis by transitioning from static feature extraction (amplitude, duration of waves) to dynamic temporal pattern recognition. The neural network processes the entire time-series signal, capturing evolving patterns that conventional methods miss, thereby improving reliability without sacrificing computational efficiency.
2Device complexity
If ECG signals are used directly for biometric identification without deep processing, then the system remains simple and fast, but the temporal variations and noise make reliable pattern recognition impossible
Solution Approach 1:
The patent introduces a neural network as an intermediary between raw ECG signals and biometric identification. This intermediary layer processes the temporal patterns and noise, transforming the raw signals into reliable biometric features. The neural network acts as a mediator that bridges the gap between simple signal acquisition and reliable pattern recognition.
Solution Approach 2:
The system performs preliminary processing of ECG signals by training the neural network on enrollment data before actual authentication. This preliminary action creates optimized weight matrices and bias vectors that encode individual biometric characteristics, enabling reliable pattern recognition during subsequent authentication without complex real-time processing.
3Reliability
If deep learning methods are applied to ECG biometrics, then verification accuracy improves significantly, but the computational requirements and system complexity increase
Solution Approach 1:
The patent segments the deep learning process into distinct phases: enrollment (training) and authentication (verification). During enrollment, the neural network learns from multiple heartbeats to create personalized templates. During authentication, the system only performs forward propagation using the pre-trained weights, significantly reducing computational complexity while maintaining high verification accuracy.
Solution Approach 2:
The invention creates simplified copies of the complex neural network model by storing only the essential weight matrices and bias vectors after training. These copied parameters enable fast authentication without requiring the full training infrastructure, reducing device complexity while preserving verification accuracy.
4Productivity
If fiducial point-based methods are used for ECG analysis, then the algorithm can process few heartbeats efficiently, but the equal error rate remains high (FAR: 5.71%, FRR: 3.44%)
Solution Approach 1:
The patent replaces fiducial point detection and manual morphology analysis with a neural network that automatically identifies relevant features. The network substitutes mechanical point-by-point analysis with learned pattern recognition, achieving both processing efficiency and high measurement precision by capturing temporal dependencies that fiducial methods miss.
Data Source
Figure 1
AI summary
Method and device for using ECG signals for personal authentication by converting raw ECG signals into individual and unambiguous ECG signal periods, which are divided into individual sequences by heartbeat detection. These series of ECG signals are individual, unique and suitable for one-time registration of the neural network artificial intelligence algorithm, which can be used by an authentication device, either stand-alone, such as in the form of a token, or embedded in some form of chipset or hardware.