Biophysical Signal Cycle Analysis for Disease Severity Scoring
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Solution Overview
Problem
Current methods for diagnosing cardiac diseases and conditions often require invasive procedures, radiation, exercise, or pharmacological agents, posing risks and disadvantages, and lack effective non-invasive alternatives for assessing diseases like diastolic heart failure and pulmonary hypertension.
Innovation Solution
A system that utilizes non-invasive biophysical signals, such as cardiac and photoplethysmography signals, to extract cycle variability features for diagnosing diseases using machine-learned classifiers, enabling estimation of disease presence, severity, and localization without invasive techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If invasive procedures or radiation are used for diagnosing cardiac diseases, then diagnostic accuracy is improved, but patient safety and comfort deteriorate due to risks and disadvantages
Solution Approach 1:
The patent replaces invasive mechanical procedures (catheterization, imaging with radiation) with non-invasive biophysical signal monitoring. Surface sensors detect cardiac electrical activity and photoplethysmographic signals to extract cycle variability features, eliminating the need for invasive mechanical intervention while maintaining diagnostic capability
Solution Approach 2:
The patent introduces cycle variability features as an intermediary metric that bridges non-invasive signal measurement and disease diagnosis. By analyzing variations in cardiac cycle characteristics from surface sensors, the system indirectly assesses cardiac function and disease state without direct invasive measurement
2Reliability
If invasive procedures are used for disease diagnosis, then diagnostic capability is improved, but device complexity and procedure invasiveness worsen
Solution Approach 1:
The patent extracts diagnostic information from routine non-invasive biophysical signals by identifying and analyzing cycle variability features. Instead of requiring complex invasive procedures, the system extracts meaningful diagnostic patterns from ordinary cardiac electrical activity and photoplethysmographic waveforms captured by surface sensors
Solution Approach 2:
The patent creates a virtual model of cardiac function through cycle variability analysis of surface sensor signals. This computational copy of cardiac behavior patterns provides diagnostic insight equivalent to invasive measurement without requiring physical intrusion into the body
3Object-affected harmful factors
If non-invasive biophysical signals are used for diagnosis, then patient safety is improved, but diagnostic precision may deteriorate due to signal limitations
Solution Approach 1:
The patent transforms routine biophysical signals into diagnostic information by changing the analysis parameter from average values to cycle variability metrics. By measuring variations in cardiac cycle characteristics rather than static parameters, the system extracts enhanced diagnostic precision from non-invasive signals
Solution Approach 2:
The patent focuses intensively on specific cycle variability features within the biophysical signals rather than attempting to analyze all signal characteristics. By concentrating diagnostic effort on the most informative variability parameters, the system achieves high diagnostic precision from limited non-invasive measurements
Data Source
AI summary
The exemplified methods and systems facilitate the use, for diagnostics, monitoring, or treatment, of one or more cycle variability based features or parameters determined from biophysical signals such as cardiac or photoplethysmography signals that are acquired non-invasively from surface sensors placed on a patient while the patient is at rest. The estimated metric may be used to assist a physician or other healthcare provider in diagnosing the presence or non-presence and/or severity and/or localization of diseases or conditions or in the treatment of said diseases or conditions.