Heart Cycle Variability Screening for Noisy Cardiac Signals
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Solution Overview
Problem
Existing methods for acquiring biophysical signals, such as cardiac signals, are invasive, costly, and risky, leading to incorrect assessments and increased healthcare costs due to noise interference, and often require repeated signal acquisition, causing inconvenience to patients.
Innovation Solution
A method for quantifying cardiac cycle-variability as a metric of signal quality to reject noisy data and predict cardiovascular conditions like coronary artery disease and pulmonary hypertension, using photoplethysmographic signals and other biophysical signals, with automated rejection of asynchronous noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If invasive or minimally invasive techniques are used to acquire biophysical signals, then signal quality improves, but patient risk and cost increase
Solution Approach 1:
The patent replaces invasive mechanical signal acquisition methods with non-invasive photoplethysmography (PPG) sensing. The optical sensor detects blood volume changes through tissue without penetrating the skin, eliminating infection and trauma risks while maintaining sufficient signal quality for cardiovascular assessment.
Solution Approach 2:
The patent introduces photoplethysmographic signals as an intermediary measurement that indirectly reflects cardiac function. Instead of directly measuring cardiac electrical or mechanical properties invasively, the system uses optical detection of blood volume changes as a mediator to infer cardiovascular status non-invasively.
2Ease of operation
If non-invasive methods are used to acquire biophysical signals, then patient convenience improves, but signal quality deteriorates due to noise
Solution Approach 1:
The patent implements real-time signal quality assessment using heart rate variability analysis as feedback. The system continuously monitors HRV metrics during signal acquisition and provides immediate feedback to identify poor quality segments, enabling automated rejection of noisy data and triggering re-acquisition only when necessary, thus maintaining high signal quality while preserving patient convenience.
Solution Approach 2:
The patent performs preliminary signal quality assessment during the acquisition process itself, rather than after analysis. By evaluating HRV characteristics in real-time, the system can identify and reject noisy segments before they compromise the final diagnosis, ensuring high measurement precision without requiring repeated patient visits.
3Measurement precision
If signal quality assessment is performed manually, then accuracy improves, but time required increases
Solution Approach 1:
The patent transforms the manual signal quality assessment process into an automated parameter-based evaluation. By converting subjective manual assessment into objective quantitative HRV parameter measurement (such as SDNN, RMSSD, and other variability metrics), the system achieves both high accuracy and rapid processing through computer algorithms that automatically analyze signal characteristics.
Solution Approach 2:
The system performs self-assessment of signal quality through automated HRV analysis without requiring manual intervention. The algorithm independently evaluates signal characteristics, identifies poor quality segments, and makes rejection decisions autonomously, eliminating time-consuming manual review while maintaining assessment accuracy through standardized computational criteria.
4Measurement precision
If repeated signal acquisition is performed, then signal quality improves, but patient inconvenience increases
Solution Approach 1:
The patent uses real-time HRV-based feedback to continuously monitor signal quality during acquisition. This feedback mechanism allows the system to identify poor quality segments immediately and reject them automatically, eliminating the need for repeated acquisitions and reducing patient inconvenience while maintaining high signal quality through selective acceptance of valid measurements.
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
Enables non-invasive, cost-effective prediction and diagnosis of cardiovascular conditions by improving signal quality assessment, reducing the need for repeated acquisitions and minimizing patient inconvenience.
Implementation Method 1
obtaining, by one or more processors, a biophysical signal data set of a subject associated with a photoplethysmographic signal or a cardiac signal
Data Source
AI summary
The exemplified methods and systems facilitate the quantification of cardiac cycle-variability as a metric of signal quality of an acquired signal data set and the rejection, based on that quantification, of said acquired signal data set from one or more subsequent analyses that can predict and/or estimate a metric associated with the presence, non-presence, severity, and/or localization of abnormal cardiovascular conditions or disease, including, for example, but not limited to, coronary artery disease, abnormal left ventricular end-diastolic pressure disease (LVEDP), pulmonary hypertension and subcategories thereof, heart failure (HF), among others as discussed herein. The quantification of levels of cycle-variability assessed noise such as skeletal-muscle-related-signal contamination and muscle-artifact-noise contamination, and other asynchronous-noise contamination in an acquired signal can be subsequently used for the automated rejection of such asynchronous noise from measurements of biophysical signals.