PPG Pulse Evaluation With Predetermined Morphological Templates
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for evaluating photoplethysmography (PPG) signal quality suffer from misclassifications due to noisy signals, adaptive template distortions, and limitations in distinguishing normal and non-normal sinus rhythms, particularly when exposed to new datasets, leading to inaccurate physiological parameter estimation.
Innovation Solution
A method involving the use of multiple predetermined pulse templates adapted to the shape of individual PPG pulses, with morphological comparisons and dynamic time warping to quantify differences, and a binary quality assessment to reject bad quality pulses, while accounting for physiological and external factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If adaptive templates are used to evaluate PPG pulses, then the method can adapt to signal characteristics, but the templates become distorted by noisy signals and arrhythmic beats leading to false rejection of good pulses
Solution Approach 1:
The evaluation process is segmented into multiple independent stages: initial pulse detection, template selection from predetermined templates, adaptation to individual pulse shape, and morphological comparison. This segmentation prevents contamination of the template creation process by noisy signals and arrhythmic beats, as templates are selected from clean predetermined templates rather than being adaptively created from potentially corrupted signals.
Solution Approach 2:
Predetermined pulse templates are prepared in advance from clean, high-quality pulse data before the actual evaluation process. This preliminary action ensures that the templates start in a known good state, avoiding the need to adapt them during the evaluation process and preventing distortion by noisy or arrhythmic signals.
2Measurement precision
If machine learning methods are used for PPG quality assessment, then classification performance can be improved, but the algorithms require extensive training data and expert labelling which are not always accurate or available
Solution Approach 1:
Instead of training complex machine learning models that require extensive annotated data, the method uses copying of predetermined pulse templates that represent ideal pulse morphologies. These templates are created once from high-quality reference data and then reused for evaluation, eliminating the need for continuous training and expert labelling while maintaining high classification accuracy.
Solution Approach 2:
The method replaces expensive and time-consuming machine learning models with simple, lightweight template matching. The predetermined templates act as disposable reference patterns that can be applied repeatedly without requiring retraining or computational resources, making the system more practical for deployment.
3Measurement precision
If multiple pulse templates are used to improve evaluation accuracy, then the ability to distinguish normal and non-normal rhythms improves, but the complexity of the evaluation process increases
Solution Approach 1:
The method dynamically selects and adapts the appropriate pulse template based on the specific pulse being evaluated. Rather than using a fixed complex model, the system flexibly matches the pulse morphology against multiple predetermined templates and selects the best match, simplifying the evaluation process while maintaining high precision in distinguishing normal and abnormal rhythms.
Data Source
AI summary
The present invention relates to a method (1) of evaluating a detected pulse in a Photoplethysmography. PPG, signal (10) in a PPG signal measurement, the method comprising the steps of (a) receiving the PPG signal comprising a plurality of pulses (20): (b) extracting pulses from the received PPG signal: (c) providing one or more predetermined pulse templates (30): (d) selecting one or more pulse templates (32) out of the provided pulse templates (30): (c) adapting the selected one or more pulse templates to the shape of a first pulse (22) out of the identified pulses (20) in the PPG signal (10); and (f) performing a morphological comparison between the first pulse (22) and each of the adapted one or more pulse templates (34) to provide a quantification of the difference (AD) between the first pulse and the adapted one or more pulse templates.