AI ECG Algorithm Detects Ventricular Premature Contractions
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for identifying patients with ventricular premature contractions (VPC) during sinus rhythm are limited by their need for prolonged ECG monitoring, which is costly and has low yield when VPC frequency is low.
Innovation Solution
An artificial intelligence-enabled ECG algorithm system using convolutional neural networks (CNNs) to detect subtle features of VPC in standard 10-second, 12-lead ECGs during normal sinus rhythm, allowing for early identification and treatment of VPC patients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If prolonged ECG monitoring is used to identify VPC patients, then detection reliability is improved, but cost increases and productivity decreases
Solution Approach 1:
The patent applies preliminary action by using AI algorithms to pre-screen and identify high-risk patients from routine ECG data before they develop VPC episodes. The system analyzes subtle ECG features in advance to flag patients who need prolonged monitoring, thereby improving detection reliability while avoiding unnecessary prolonged monitoring in low-risk patients, thus maintaining productivity.
Solution Approach 2:
The patent introduces an AI-based intermediary system that acts as a mediator between routine ECG screening and prolonged monitoring. This intermediary uses machine learning models to triage patients, determining who requires extended monitoring based on predicted VPC risk. This resolves the contradiction by enabling reliable detection through targeted monitoring only where needed, rather than applying prolonged monitoring universally.
2Reliability
If prolonged ECG monitoring is used to identify VPC patients, then detection reliability is improved, but cost increases
Solution Approach 1:
The system performs preliminary risk stratification using AI algorithms on routine ECG data, identifying patients who are most likely to develop VPC. This allows healthcare providers to allocate prolonged monitoring resources only to high-risk patients, improving detection reliability for those who need it while significantly reducing overall monitoring costs by avoiding unnecessary prolonged monitoring in low-risk patients.
Solution Approach 2:
The AI intermediary system mediates between the need for reliable VPC detection and the constraint of monitoring costs. It processes routine ECG data to predict VPC risk, creating a triage mechanism that directs prolonged monitoring only to patients with high predicted risk. This resolves the cost-reliability contradiction by enabling cost-effective targeted monitoring rather than universal prolonged monitoring.
3Ease of operation
If traditional ECG analysis methods are used, then ease of operation is maintained, but measurement precision deteriorates for detecting subtle VPC features
Solution Approach 1:
The patent replaces manual mechanical ECG analysis with an AI-based automated system. The machine learning models automatically detect subtle VPC features from routine ECG recordings, achieving superior measurement precision compared to traditional manual analysis. The system maintains ease of operation by providing automated interpretation that can be integrated into existing clinical workflows, eliminating the need for operators to manually analyze complex ECG patterns.
Solution Approach 2:
The patent applies parameter changes by transforming routine ECG data into enhanced feature representations that highlight subtle VPC patterns. The AI system processes standard ECG parameters through multiple transformation layers, converting them into optimized feature sets that improve detection precision. This allows the system to maintain compatibility with existing ECG equipment and workflows while achieving superior measurement precision through computational parameter transformation.
Data Source
AI summary
An artificial intelligence-enabled ECG algorithm system and method thereof are applied in the environment of the identification of patients with ventricular premature contractions (VPC) during sinus rhythm. The present invention of the artificial intelligence-enabled ECG algorithm system and method thereof can provide, a standard 10-second, 12-lead ECGs algorithm based on artificial intelligence for the identification of patients with ventricular premature contractions (VPC) during normal sinus rhythm; and, the ECG algorithm using artificial intelligence can detect some minimal changes in the patient's sinus rhythm ECG without VPC episodes, and can also identify patients having ventricular premature contraction for early treatment to reduce the patent's risk of heart failure or sudden death.