ECG-Based Identity Verification Using PQRST Cycle Analysis
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Solution Overview
Problem
Current methods for authentication using biometric indicia do not effectively utilize physiological characteristics like cardiac bioelectric signals, and there is a lack of implementation for behavioral characteristics, which are difficult to measure and integrate into authentication systems.
Innovation Solution
A system and method for authenticating individuals by analyzing PQRST cycles from electrocardiogram (ECG) signals, involving signal processing to normalize heart signals, constructing Gaussian mixture models for reference and candidate persons, and comparing statistical parameters to verify identity, with the option to adjust comparison thresholds to balance error likelihoods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional biometric indicia (fingerprints, voiceprints, retinal scans) are used for authentication, then identity verification can be performed, but the system does not effectively utilize physiological characteristics like cardiac bioelectric signals which could provide unique discrimination
Solution Approach 1:
The patent applies universality by integrating multiple biometric approaches - combining traditional biometric indicia with cardiac bioelectric signal analysis. The system can process various types of biometric data through a unified authentication framework, allowing the same system to handle both conventional biometrics and the novel ECG-based physiological characteristics, thereby improving overall authentication reliability while maintaining versatility
2Reliability
If behavioral characteristics are used as biometric indicia, then additional authentication dimensions are provided, but the difficulty of measuring and implementing such characteristics increases system complexity
Solution Approach 1:
The patent replaces complex mechanical measurement systems with electrical signal processing. Instead of using cumbersome mechanical sensors to measure behavioral characteristics, the system utilizes electrocardiogram signals that can be obtained through standard medical-grade electrical sensors. The behavioral characteristics are captured through electrical field measurements of cardiac activity, which are then processed using signal processing techniques and statistical analysis, significantly reducing measurement and implementation complexity while maintaining high authentication reliability
3Measurement precision
If cardiac bioelectric signals are analyzed for authentication, then unique physiological discrimination is achieved, but signal processing and normalization requirements increase computational complexity
Solution Approach 1:
The patent applies parameter changes by transforming the raw cardiac bioelectric signals into standardized statistical parameters. The system normalizes ECG signals by adjusting amplitude and timing parameters, converts variable-duration cardiac cycles into fixed-length feature vectors, and transforms physiological measurements into comparable statistical metrics. These parameter transformations enable precise physiological discrimination while managing computational complexity through standardized processing procedures
4Measurement precision
If Gaussian mixture models and statistical parameter comparisons are used, then accurate identity verification is achieved, but the computational requirements and processing time increase
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing statistical parameters from cardiac bioelectric signals during an enrollment phase. The system performs signal processing, normalization, and feature extraction in advance to create reference profiles. During actual authentication, the system only needs to compare newly acquired signals against these pre-established profiles, significantly reducing processing time while maintaining high verification accuracy through the use of pre-computed Gaussian mixture models and statistical comparisons
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
The system effectively discriminates between individuals based on unique PQRST cycle patterns, allowing for reliable authentication and reducing the likelihood of false positives or negatives, while accommodating changes over time and considering cardiac anomalies.
Implementation Method 1
Simultaneous contraction of the large number of fibers in the ICS generates an electrical field that can be measured at the body surface using an electrocardiograph (ECG)
Data Source
AI summary
A method of authenticating or declining to authenticate an asserted identity of a candidate-person. In an enrollment phase, a reference PQRST heart action graph is provided or constructed from information obtained from a plurality of graphs that resemble each other for a known reference person, using a first graph comparison metric. In a verification phase, a candidate-person asserts his/her identity and presents a plurality of his/her heart cycle graphs. If a sufficient number of the candidate-person's measured graphs resemble each other, a representative composite graph is constructed from the candidate-person's graphs and is compared with a composite reference graph, for the person whose identity is asserted, using a second graph comparison metric. When the second metric value lies in a selected range, the candidate-person's assertion of identity is accepted.