Portable ECG Y-Configuration for Personalized Lead Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing ECG analysis systems fail to provide individualized, accurate, and efficient analysis of cardiac signals across multiple leads, lacking customization and active patient participation, and do not utilize reinforcement learning for personalized algorithms.
Innovation Solution
A portable ECG device with a Y-configuration and machine-learning-based analysis using reinforcement learning, allowing independent analysis of each lead, patient input, and customizable algorithms for enhanced accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If ECG analysis is performed on the resultant ECG signal only, then the analysis process is simplified, but nuances and anomalies in individual Leads are neglected
Solution Approach 1:
The patent divides the ECG analysis into two independent segments: (1) analysis of the resultant ECG signal, and (2) individual analysis of each Lead. This segmentation allows the system to maintain simplicity in the overall process while achieving high precision by examining each Lead separately for unique anomalies that may be missed in the aggregated signal.
2Productivity
If a standardized ECG analysis algorithm is used for all patients, then the analysis process is efficient and consistent, but individualized and personalized analysis is not achieved
Solution Approach 1:
The patent implements a dynamic analysis system where the algorithm adapts to each patient's specific characteristics. The system dynamically adjusts the analysis approach based on individual patient data, allowing it to maintain efficiency through automation while achieving personalization by tailoring the analysis to each patient's unique cardiac patterns and risk factors.
Solution Approach 2:
The system incorporates feedback mechanisms where the analysis results from individual Leads and patient-specific data are fed back into the algorithm to refine and personalize subsequent analyses. This feedback loop enables the system to learn from each patient's data and improve the personalization of the analysis while maintaining operational efficiency.
3Measurement precision
If active patient participation and customization options are added to ECG analysis, then individualized accuracy is improved, but the ease of operation is reduced
Solution Approach 1:
The patent implements self-service functionality where the system automatically guides patients through the ECG process and performs personalized analysis without requiring complex user intervention. The device handles data collection, processing, and interpretation autonomously, allowing patients to benefit from customized analysis while maintaining simple operation through automated workflows and user-friendly interfaces.
Data Source
AI summary
This application relates in general to a portable ECG monitoring and, in particular, to a process for ECG signal analysis using an artificial intelligence backend software with machine-learning-based with a reinforcement learning algorithm. This invention presents a waterproof portable device for gathering and monitoring of ECG data of an individual, configured to work on either online or offline mode. The process comprises at least the steps of (a) preparing an ECG exam through a first device containing a software; (b) connecting the first device to a portable ECG device; (c) setting up the portable ECG device on a patient's chest; (d) collecting the ECG data; (e) uploading the data to an artificial intelligence backend software; (f) processing and classifying the data using a machine learning algorithm; (g) analyzing the classification provided in step âfâ and performing a second classification of data; and (h) providing a signed medical report.