ML Software for EMF Abnormality Detection
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Solution Overview
Problem
Current methods for analyzing electromagnetic fields (EMF) associated with human and animal tissues are inadequate in detecting abnormalities indicative of serious health conditions, as they lack efficient and accurate diagnostic tools for organs like the heart, brain, and stomach.
Innovation Solution
The development of devices, systems, and software that utilize machine learning algorithms, specifically neural networks, to analyze EMF data, generate medical images, and predict abnormalities by training on EMF measurements and corresponding health data, allowing for the detection of conditions such as cardiac abnormalities and ischemia.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional EMF analysis methods are used, then the analysis process is simple, but the detection accuracy and ability to identify abnormalities is insufficient
Solution Approach 1:
The patent replaces traditional mechanical/manual EMF analysis methods with machine learning algorithms and neural networks. The system uses automated computational models to process EMF data, identify patterns, and detect abnormalities without manual intervention, thereby improving detection accuracy while managing complexity through software-based automation.
Solution Approach 2:
The patent introduces machine learning software modules and neural networks as intermediary components between EMF data collection and abnormality detection. These intermediaries process raw EMF signals, extract relevant features, and generate diagnostic outputs, enabling accurate abnormality detection while maintaining system organization and manageability.
2Reliability
If machine learning algorithms are implemented to improve abnormality detection, then detection accuracy improves, but computational complexity and data processing requirements increase
Solution Approach 1:
The patent divides the diagnostic system into distinct software modules: EMF data acquisition module, machine learning processing module, and diagnostic output module. Each module performs a specific function, allowing the complex diagnostic task to be broken down into manageable segments that can be developed, tested, and maintained independently while improving overall diagnostic reliability.
Solution Approach 2:
The patent develops multi-functional machine learning software modules that can process various types of EMF data from different organs (heart, brain, stomach) and detect multiple types of abnormalities. This universal approach improves diagnostic reliability across different applications while avoiding the need for separate specialized systems for each organ or condition.
3Loss of information
If comprehensive EMF data analysis is performed to identify all possible abnormalities, then diagnostic completeness improves, but analysis time and computational resources increase
Solution Approach 1:
The patent implements preliminary processing steps including EMF data normalization, feature extraction, and preprocessing before main analysis. The machine learning models are pre-trained on comprehensive datasets to learn patterns efficiently. This preliminary preparation enables the system to perform comprehensive abnormality detection without requiring excessive analysis time during actual diagnostic operations.
Solution Approach 2:
The patent replaces time-consuming manual analysis and exhaustive computational searches with optimized machine learning algorithms. The neural networks and ML models automatically identify relevant patterns and abnormalities in EMF data through learned representations, significantly reducing analysis time while maintaining comprehensive detection capability compared to traditional systematic analysis methods.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate detection and prediction of health abnormalities by processing EMF data, providing a diagnostic tool that can identify and characterize physiological health issues in human tissues, improving early detection and treatment of conditions like congestive heart failure and ischemia.
Implementation Method 1
Human and animal tissue is associated with an electromagnetic field (EMF) due to electrical currents passing through said tissue
Data Source
AI summary
Abnormalities in electromagnetic fields in the heart, brain, and stomach, among other organs and tissues of the human body, can be indicative of serious health conditions. Described herein are methods, software, systems and devices for detecting the presence of an abnormality in an organ or tissue of a subject by analysis of the electromagnetic fields generated by the organ or tissue.