Time Series Segmentation for Intracardiac Electrogram Labeling
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Solution Overview
Problem
Interpreting intracardiac electrograms and electrocardiograms for cardiac abnormalities requires accurate identification of different segments, which existing methods struggle to achieve efficiently.
Innovation Solution
An apparatus and method for labeling time series data using a processor to generate segments, identify attributes, and classify them using a trained time series classifier, generating labeled segments for improved identification of cardiac events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated classification methods are used to identify segments in intracardiac electrograms, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The patent segments the intracardiac electrogram signal into distinct time-series segments corresponding to different cardiac events (atrial activation, ventricular activation, etc.). This segmentation enables automated classification of each segment while preserving the ability to precisely identify boundaries between different cardiac phases, thus resolving the contradiction between automated processing and identification precision.
Solution Approach 2:
The patent introduces an intermediary classification system that uses trained classifiers to bridge the gap between raw signal data and meaningful segment identification. The classifier acts as a mediator that translates automated pattern recognition into precise segment boundaries, maintaining both productivity and measurement precision in the segment identification process.
2Measurement precision
If manual identification of time series segments is performed, then measurement precision is improved, but productivity deteriorates
Solution Approach 1:
The patent applies preliminary actions by pre-training classification models on labeled time-series data before actual segment identification. This preliminary training enables the system to achieve high measurement precision automatically during operation, eliminating the need for manual segment identification while maintaining both accuracy and productivity.
Solution Approach 2:
The system implements self-service through automated classification where the trained model independently identifies and labels time-series segments without human intervention. This self-service capability achieves both high productivity through automation and maintains measurement precision through the accuracy of the trained classifier, resolving the contradiction between manual precision and automated efficiency.
3Measurement precision
If complex classification algorithms are used to classify time series segments, then measurement precision is improved, but device complexity deteriorates
Solution Approach 1:
The patent applies parameter changes by training classification models with adjustable parameters on labeled time-series data. The system optimizes classification accuracy by tuning model parameters during training, achieving high measurement precision while managing device complexity through parameter optimization rather than algorithmic complexity.
Solution Approach 2:
The patent uses copying by training the classification model on copies of labeled time-series data from multiple patients and conditions. This training on diverse data copies enables the model to achieve high measurement precision across different scenarios without increasing the fundamental complexity of the classification algorithm itself.
Data Source
AI summary
An apparatus for labeling a plurality of time series data is disclosed. The apparatus includes at least processor and a memory communicatively connected to the processor. The memory instructs the processor to receive a plurality of time series data. The memory instructs the processor to generate a plurality of time series segments for each time series represented within the plurality of time series data. The memory instructs the processor to identify one or more segment attributes for each time series segment. The memory instructs the processor to classify each time series segment of the plurality of time series segments to at least one time series label as a function of the one or more segment attributes. The memory instructs the processor to generate at least one labeled time series segment for each time series segment of the plurality of time series segments as a function of the classification.