Ballistocardiogram Model for Hemorrhage Detection
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Solution Overview
Problem
Current methods for diagnosing internal hemorrhage at the bedside are limited by the need for baseline ballistocardiogram measurements, which can be challenging to obtain, especially in emergency situations, due to inter-person and intra-person variability factors such as age, anatomy, and physiological processes, making it difficult to detect fluid accumulation without a prior baseline.
Innovation Solution
A mechanical model of the body as a system of masses joined by springs and dampers is used to predict ballistic forces, allowing for the detection of internal bleeding by comparing measured ballistocardiogram measurements to predicted values, eliminating the need for a baseline measurement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If baseline ballistocardiogram measurements are used to detect internal hemorrhage, then measurement precision is improved, but device complexity and difficulty of operation worsen due to the need for baseline measurements in emergency situations
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing multiple ballistocardiogram signals in real-time to establish a dynamic baseline automatically, eliminating the need for manual baseline measurements before emergency procedures. The processor continuously processes BCG signals to detect hemorrhage indicators without requiring pre-procedure baseline data.
Solution Approach 2:
The system serves itself by automatically establishing baselines through real-time signal processing and comparison algorithms. The processor autonomously distinguishes between normal physiological variations and pathological changes without requiring external baseline data or manual calibration, making the system self-sufficient in emergency settings.
2Measurement precision
If multiple factors influencing BCG variability are accounted for, then measurement precision improves, but device complexity increases due to the need to model inter-person and intra-person variability
Solution Approach 1:
The system segments the sources of BCG variability into distinct categories: inter-person factors (age, anatomy, physiology) and intra-person factors (natural variations, chronic conditions). The processor applies different analysis methods to each segment, comparing signals against appropriate reference ranges rather than requiring a single comprehensive model.
Solution Approach 2:
The system changes parameters dynamically by adjusting detection thresholds and reference values based on patient-specific characteristics and real-time signal quality. The processor modifies analysis parameters adaptively rather than using fixed models, simplifying the system while maintaining precision across diverse patient populations.
Data Source
AI summary
According to an aspect, there is provided an apparatus for predicting a ballistic force on a subject, the apparatus comprising: a memory comprising instruction data representing a set of instructions; and a processor configured to communicate with the memory and to execute the set of instructions, wherein the set of instructions, when executed by the processor, cause the processor to: model the body of the subject as a mechanical system comprising a plurality of masses joined by springs and dampers, wherein each of the masses represents a different region of the body of the subject, and predict the ballistic force on the subject, based on oscillations of the mechanical system.