Dynamic Cardiovascular Waveform Template Adaptation
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Solution Overview
Problem
Existing algorithms for filtering and evaluating cardiovascular waveforms often incorrectly classify atypical electrical activity as noise or artifacts, missing true cardiovascular abnormalities due to mismatch with existing templates.
Innovation Solution
A method and system for template-based analysis and classification of cardiovascular waveforms that updates templates with newly-identified features, allowing for better identification and reclassification of periodic components in both electrical and hemodynamic waveforms, ensuring accurate classification of cardiovascular abnormalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing template-based algorithms are used to classify cardiovascular waveforms, then classification speed is maintained, but classification accuracy deteriorates due to mismatch with abnormal waveforms
Solution Approach 1:
The patent implements dynamic template adaptation where the template library is continuously updated with newly identified abnormal waveform patterns. The system transitions from static templates to dynamic templates that evolve with new discoveries, allowing the classification algorithm to adapt to previously unrecognized abnormal patterns while maintaining classification performance
Solution Approach 2:
The system performs self-learning by automatically identifying new abnormal waveform patterns from clinical data and incorporating them into the template library without requiring manual template creation. The algorithm autonomously expands its knowledge base by detecting patterns it previously couldn't classify, enabling continuous improvement of classification accuracy
2Reliability
If traditional filtering algorithms are applied to cardiovascular waveforms, then noise reduction is achieved, but true abnormalities are lost due to over-filtering
Solution Approach 1:
The system uses feedback from hemodynamic waveform analysis to validate or correct ECG-based classifications. When the hemodynamic waveform shows abnormalities that contradict the ECG classification, the system investigates further and may reclassify the signal, preventing false noise classification and improving abnormality detection reliability
Solution Approach 2:
The patent introduces hemodynamic waveform analysis as an intermediary validation layer between ECG signal processing and final classification. This intermediary step cross-checks ECG-based classifications against independent hemodynamic measurements, reducing false positives caused by over-filtering or artifact misclassification
3Measurement precision
If manual waveform analysis is performed by physicians, then detection accuracy improves, but analysis time increases significantly
Solution Approach 1:
The system performs automated self-analysis of cardiovascular waveforms using machine learning algorithms that continuously learn from clinical data. The algorithm autonomously identifies, classifies, and flags abnormal patterns without requiring manual physician review for every waveform, dramatically reducing analysis time while maintaining high detection accuracy through continuous learning
Data Source
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AI summary
In various embodiments, a first classification assigned to a periodic component of an electrical waveform that represents electrical activity in a patient's heart may be identified (302). A corresponding periodic component of a hemodynamic waveform that represents hemodynamic activity in the patient's cardiovascular system may be analyzed (306, 318, 328). The corresponding periodic component may be causally related to the periodic component of the electrical waveform. Based on the analysis, the previously-assigned classification may be assigned (312, 324) to the corresponding periodic component of the hemodynamic waveform in response to a determination, based on the analyzing, that the previously-assigned classification also applies to the corresponding periodic component. In a database (130) of hemodynamic templates, a hemodynamic template associated with the previously-assigned classification may be updated (314) to include one or more features of the corresponding periodic component of the hemodynamic waveform.