ECG-to-Image Conversion for Transfer Learning Diagnosis
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Solution Overview
Problem
Existing artificial intelligence models, particularly deep learning models, are not configured to process time-series data such as electrocardiogram (ECG) waveforms effectively, and those that can process time-series data are complex and difficult to adapt across domains.
Innovation Solution
Convert ECG data into a two-dimensional digital image format that preserves temporal and spatial information, using techniques like vectorcardiography (VCG), and apply transfer learning with pre-trained image processing models like CNNs to classify health conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If time series data is directly input into machine learning models, then the model can process the data, but the model cannot capture long-term dependencies and temporal relationships effectively
Solution Approach 1:
The patent applies preliminary action by performing data aggregation and feature extraction before feeding data into the machine learning model. The system aggregates time series data at multiple time granularities (e.g., hourly, daily, weekly) and extracts statistical features (mean, variance, trends) in advance, so that when the model receives the data, it already contains compressed temporal information that captures long-term dependencies without requiring the model to process raw sequential data directly.
Solution Approach 2:
The patent introduces an intermediary layer between the raw time series data and the machine learning model. This intermediary consists of feature engineering components that transform raw data into meaningful representations including temporal aggregates, statistical measures, and domain-specific features. This intermediary layer bridges the gap by converting complex temporal patterns into formats that standard ML models can effectively process.
2Reliability
If detailed time series data is processed to capture temporal relationships, then model accuracy improves, but computational time and resources increase
Solution Approach 1:
The patent applies segmentation by dividing the time series data into multiple segments at different time granularities (e.g., hourly, daily, weekly aggregates). Instead of processing the entire detailed time series sequentially, the system segments the data and extracts features from each segment independently, then combines these features. This segmentation reduces the computational burden while preserving important temporal patterns at multiple scales.
Solution Approach 2:
The patent applies partial action by selecting and extracting only the most relevant features from the time series data rather than processing all possible temporal relationships. The system identifies key statistical features and temporal patterns that are most predictive for the specific application, extracting only those features needed for accurate prediction, thereby reducing computational overhead while maintaining prediction accuracy.
3Reliability
If multiple features are extracted from time series data, then model performance improves, but the complexity of feature engineering increases
Solution Approach 1:
The patent applies universality by creating a multi-functional feature engineering framework that serves multiple purposes simultaneously. The same feature extraction pipeline generates features for different time granularities, different statistical measures, and different model requirements all in one unified process. This universal approach reduces the need for separate feature engineering procedures for each model or application, simplifying the overall complexity while maintaining comprehensive feature coverage.
4Reliability
If time series data is aggregated at multiple time granularities, then temporal relationships are better captured, but data processing complexity increases
Solution Approach 1:
The patent applies the nested doll principle by organizing time series aggregation at multiple nested time granularities. The system creates hierarchical aggregates where coarse-grained features (weekly, monthly) contain and summarize finer-grained features (daily, hourly) within them. This nested structure allows the model to capture temporal relationships at multiple scales simultaneously while organizing the complexity in a hierarchical, manageable manner where each level builds upon the previous level.
Data Source
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AI summary
Techniques are described herein for converting time series data such as electrocardiogram ("ECG") data into forms suitable for application across machine learning models, and for applying those converted data as input across machine learning models to, for instance, determine health conditions of underlying subjects. In various embodiments, a two-dimensional image may be generated (601) based on vectorcardiography ("VCG") data, wherein the VCG data is measured directly or is based on electrocardiogram ("ECG") data measured from a subject. The two-dimensional image may be applied (612) as input across a machine learning model to generate output, wherein the machine learning model is configured for use in processing two-dimensional images. A health condition of the subject may be determined (614) based on the output.