18393358. METHOD AND SYSTEM FOR DETERMINING CARDIAC ABNORMALITIES USING CHAOS-BASED CLASSIFICATION MODEL FROM MULTI-LEAD ECG simplified abstract (Tata Consultancy Services Limited)
METHOD AND SYSTEM FOR DETERMINING CARDIAC ABNORMALITIES USING CHAOS-BASED CLASSIFICATION MODEL FROM MULTI-LEAD ECG
Organization Name
Tata Consultancy Services Limited
Inventor(s)
SUNDEEP Khandelwal of Noida (IN)
SAKYAJIT Bhattacharya of Kolkata (IN)
METHOD AND SYSTEM FOR DETERMINING CARDIAC ABNORMALITIES USING CHAOS-BASED CLASSIFICATION MODEL FROM MULTI-LEAD ECG - A simplified explanation of the abstract
This abstract first appeared for US patent application 18393358 titled 'METHOD AND SYSTEM FOR DETERMINING CARDIAC ABNORMALITIES USING CHAOS-BASED CLASSIFICATION MODEL FROM MULTI-LEAD ECG
The abstract discusses a method and system for determining cardiac abnormalities using a chaos-based classification model from multi-lead ECG signals. This approach combines various chaos-related statistical parameters to improve disease classification accuracy, particularly for conditions like Atrial Fibrillation (AF).
- The method utilizes chaos-related features such as non-linearity, self-similarity, Chebyshev distance, and spectral flatness to enhance the detection of cardiac diseases.
- By incorporating a holistic approach to studying cardiac abnormalities, the system aims to improve the accuracy of disease diagnosis associated with cardiac abnormalities.
- The use of chaos-based classification models in Machine Learning helps in capturing the underlying signs of diseases, leading to more precise detection of conditions like AF.
- The method ultimately enhances the accuracy in the percentage of AF burden, improving overall disease management and treatment outcomes.
Potential Applications: - Medical diagnostics and disease management - Cardiac health monitoring systems - Research in the field of cardiology and machine learning
Problems Solved: - Improved accuracy in the diagnosis of cardiac abnormalities - Enhanced detection of conditions like Atrial Fibrillation - Better understanding of chaos-related features in ECG signals for disease classification
Benefits: - Increased accuracy in disease diagnosis - Early detection of cardiac abnormalities - Enhanced treatment planning and monitoring for patients with heart conditions
Commercial Applications: Title: "Chaos-Based Classification Model for Cardiac Abnormalities: Commercial Applications and Market Implications" This technology can be utilized in: - Medical device manufacturing - Healthcare software development - Cardiac clinics and hospitals - Research institutions in cardiology and machine learning
Questions about Chaos-Based Classification Model for Cardiac Abnormalities: 1. How does the method improve the accuracy of disease diagnosis associated with cardiac abnormalities? - The method combines chaos-related statistical parameters to enhance disease classification accuracy, particularly for conditions like Atrial Fibrillation. 2. What are the potential applications of this technology beyond cardiac health monitoring? - This technology can also be applied in medical diagnostics, research in cardiology and machine learning, and disease management.
Original Abstract Submitted
Improvement in the accuracy of disease diagnosis associated with cardiac abnormalities is an open research area. Appropriate feature selection to capture the underlying signs of a disease is critical in Machine Learning (ML) based approaches. A method and system for, determining cardiac abnormalities using chaos-based classification model from multi-lead ECG signals, is disclosed. The method combines the commonly used chaos parameter with other set of chaos-related statistical parameters like non-linearity, self-similarity, Chebyshev distance and spectral flatness for a holistic approach to the study of cardiac abnormalities. The method disclosed thus attempts to use above ML based measures for disease classification. The set of chaos-related features used herein contribute to improving the accuracy of detection of various cardiac diseases arising due to cardiac abnormalities such as Atrial Fibrillation (AF) and the like. The improved accuracy in the detection of AF effectively improves the accuracy in percentage of AF burden.
