Automated Heart Rate Variability Curve Generation
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Solution Overview
Problem
Existing methods for analyzing heart rate variability do not automate the production of a curve representing the evolution of an individual's or competition animal's state of form or fatigue, making it difficult to monitor and detect periods of fitness or fatigue across multiple individuals.
Innovation Solution
A method for automated production of a heart rate variability curve, involving normalization, interpolation, and filtering of measurements, which allows for the detection of periods of fatigue without requiring individual-specific detection thresholds, enabling monitoring of multiple individuals' state of form and fatigue.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated production of heart rate variability curve is implemented, then monitoring consistency and productivity are improved, but device complexity increases
Solution Approach 1:
The automated curve production method segments the heart rate variability analysis into distinct processing stages: raw data acquisition, artifact filtering, normalization, interpolation, and curve generation. Each stage handles specific computational tasks independently, making the complex overall process more manageable and automatable while maintaining monitoring consistency across multiple individuals.
Solution Approach 2:
The method applies parameter changes through normalization (transforming raw HRV values to a standardized scale) and interpolation (generating intermediate values to fill gaps in measurements). These parameter transformations enable consistent comparison across individuals and automate the curve production process without requiring manual threshold configuration for each subject.
2Measurement precision
If individual-specific detection thresholds are required, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The automated curve production method creates a universal framework that can process and compare heart rate variability data from multiple individuals simultaneously. By using standardized normalization and interpolation procedures, the system achieves operational simplicity while maintaining detection precision through consistent application of the same processing pipeline across all subjects.
Solution Approach 2:
The method enables self-service operation by automatically adjusting to individual characteristics through the normalization process. The system adapts to each individual's baseline HRV patterns without requiring manual threshold configuration, making the operation simple while preserving measurement precision through data-driven adaptation.
Data Source
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AI summary
A method for the automated development of a trend curve for a representative indicator of heart rate variability in an individual or a competition animal, comprising the following steps: - Determining (101) a sequence of measurements of a representative indicator of heart rate variability, said sequence comprising, for each time occurrence of said period, either a measurement or a zero value corresponding to the absence of a measurement at the associated time occurrence, - Normalizing (102) the measurements of said sequence by excluding zero values from the normalization, - Performing at least one interpolation (103, 104) of the normalized sequence by taking a moving average over a predetermined period P of the values in the sequence, with zero values excluded from the average, - If at least one zero value, corresponding to an absent measurement, remains in the interpolated sequence,filter (105) said sequence by setting the P-1 values preceding said at least one zero value and the P-1 values following said at least one zero value to zero.