Physiological Data Validation Using Statistical Analysis
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Solution Overview
Problem
Medical devices struggle with unreliable results due to unfiltered measurement values and lack of statistical validation of physiological data from multiple body parts, leading to subjective data collection and potential errors in diagnosis.
Innovation Solution
A method for validating and comparing physiological data from multiple body parts using sensors connected to a medical device, involving verification of sensor connections, statistical analysis, and application of tests like the t-Student test to ensure data reliability and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If visual data collection by operator is used, then ease of operation is improved, but reliability of measurement results deteriorates
Solution Approach 1:
The system performs automatic validation of physiological data without requiring operator intervention. The medical device autonomously checks sensor connections, validates data quality parameters, and determines measurement reliability, eliminating subjective operator decisions while maintaining ease of operation.
Solution Approach 2:
The system implements continuous feedback loops where physiological data is automatically validated against predefined criteria. The device provides real-time feedback on data quality and measurement reliability, allowing operators to trust the results without manual verification.
2Reliability
If statistical validation is implemented, then reliability of measurement results is improved, but device complexity increases
Solution Approach 1:
The system applies statistical validation selectively to critical physiological parameters rather than all data points. By focusing validation efforts on the most important measurements, the system achieves high reliability without the computational overhead of validating every piece of data.
Solution Approach 2:
The system changes validation parameters dynamically based on the specific measurement context. Different physiological parameters use different validation criteria and statistical thresholds, optimizing reliability while minimizing unnecessary computational complexity.
3Measurement precision
If data from multiple body parts is collected, then accuracy of study is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The system segments the validation process into distinct modules for each body part and sensor type. Each sensor connection and data stream is validated independently through dedicated routines, making the overall process more manageable and less error-prone than a monolithic validation approach.
Solution Approach 2:
The system performs preliminary validation checks on sensor connections and data quality before full measurement begins. This preliminary action ensures that all sensors are properly connected and functioning, preventing measurement errors before they occur.
Data Source
AI summary
A method for the validation of physiological data, acquired in two or more parts of the human body by sensors connected to a medical device, for the statistical validation of the relationship among the signals retrieved by the medical devices, in order to select information that increases the accuracy of the study being carried out, with a high statistical certainty. The purpose of the actual invention is to make this statistically validated data available for use by protocols or reference values, according to the field of application, which increase accuracy of the studies being carried out.