ECG Autoencoder Disease Profiling for Multi-Disease Risk Scoring
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Solution Overview
Problem
Current ECG analysis methods are limited in their ability to identify a wide range of medical conditions, particularly non-cardiac diseases, and lack scalability and efficiency in processing large volumes of data, hindering effective disease detection and risk assessment.
Innovation Solution
An electrocardiogram analysis module using a deep learning autoencoder to derive disease vectors and generate disease risk scores by encoding electrocardiogram data into a lower-dimensional latent space representation, enabling automated detection and monitoring of various health issues across a broad spectrum of medical conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional ECG analysis methods are used, then the analysis process is simple and easy to implement, but the ability to identify a wide range of medical conditions is limited
Solution Approach 1:
The patent applies multi-functionality by enabling a single ECG analysis system to detect multiple types of diseases simultaneously. The machine learning model is trained to identify patterns associated with various cardiac and non-cardiac conditions (e.g., hypertension, diabetes, kidney disease) from a single ECG input, transforming the traditional single-purpose ECG analyzer into a multi-functional diagnostic tool that can screen for numerous medical conditions across different organ systems
Solution Approach 2:
The patent replaces traditional manual or rule-based ECG analysis methods with machine learning-based automated analysis. Instead of relying on clinician interpretation or simple algorithmic rules, the system uses trained neural networks to automatically detect disease patterns, substituting mechanical/manual analysis processes with intelligent automated systems that can process and interpret complex ECG data more effectively
2Productivity
If large volumes of ECG data are processed using conventional methods, then comprehensive disease screening is achieved, but processing efficiency and scalability are reduced
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on large volumes of labeled ECG data before deployment. The models are trained in advance on diverse datasets containing ECG recordings from patients with various conditions, enabling them to learn disease patterns beforehand. This pre-learning process allows the system to efficiently process new ECG data with high accuracy without requiring complex real-time analysis computations
Solution Approach 2:
The patent uses copying by creating standardized feature representations from ECG data that can be efficiently processed and stored. The system extracts key features from raw ECG signals and creates compressed representations or embeddings that capture essential disease-related information. These copied feature sets enable faster processing while maintaining detection accuracy, allowing scalable analysis of large volumes of ECG data
3Measurement precision
If automated machine learning analysis is implemented, then disease detection accuracy is improved, but system complexity and computational requirements increase
Solution Approach 1:
The patent applies extraction by isolating and focusing on specific disease-related features from the complete ECG signal. Rather than analyzing the entire complex ECG waveform in detail, the machine learning model extracts key discriminative features and patterns that are most relevant for disease detection. This feature extraction approach maintains high detection accuracy while reducing computational complexity by concentrating analysis on the most informative signal components
Data Source
AI summary
Artificial intelligence enabled disease profiling is described. An electrocardiogram analysis module is configured to derive disease vectors for a plurality of diseases using electrocardiogram training data from both disease-negative and disease-positive individuals. A standardized input is generated, via a data preprocessor of the electrocardiogram analysis module, from an electrocardiogram recorded from an individual. The standardized input is encoded, by a deep learning autoencoder of the electrocardiogram analysis module, into an embedding, the embedding being a lower-dimensional latent space representation of features extracted from the standardized input. At least one disease risk score for the individual is generated, by a statistical modeling algorithm of the electrocardiogram analysis module, for the plurality of diseases based on the embedding and the disease vectors.