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Published on: December 11, 2019
Predicting cardiovascular disease from real-time electrocardiographic monitoring: An adaptive machine learning
Zhanpeng Jin1, Yuwen Sun, Allen C Cheng
1Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15261 USA. zhj6@pitt.edu
Insights
This study introduces a novel cell phone technology for real-time cardiovascular disease (CVD) monitoring. It uses an adaptive artificial neural network (ANN) to detect heart irregularities, offering personalized health insights.
Area of Science:
- Biomedical Engineering
- Cardiology
- Artificial Intelligence in Healthcare
Background:
- Cardiovascular disease (CVD) remains the leading global cause of mortality.
- Standard electrocardiograms (ECG) and short-term Holter monitors often miss intermittent heart rhythm irregularities.
- Existing portable solutions lack real-time feedback capabilities for continuous cardiac monitoring.
Purpose of the Study:
- To develop a cell phone-based system for real-time cardiovascular disease (CVD) monitoring.
- To enable continuous on-line ECG processing and personalized cardiac health reporting.
- To automatically detect and classify abnormal CVD conditions in real time using mobile technology.
Main Methods:
- Development of an adaptive artificial neural network (ANN) machine learning technique.
- Training the ANN using individual cardiac characteristics and clinical ECG databases.
- Implementing continuous on-line ECG processing and real-time CVD classification on a cell phone platform.
Main Results:
- The developed system achieves real-time ECG processing on a cell phone.
- The adaptive ANN demonstrates improved ECG feature extraction and CVD classification accuracy.
- The technology facilitates personalized cardiac health summaries in accessible language.
Conclusions:
- A novel cell phone-based real-time CVD monitoring system has been successfully established.
- Adaptive machine learning enhances the accuracy of mobile ECG analysis for cardiovascular conditions.
- This technology offers a promising approach for continuous, accessible cardiac health management.
Abstract:
To date, cardiovascular disease (CVD) is the leading cause of global death. The Electrocardiogram (ECG) is the most widely adopted clinical tool that measures the electrical activities of the heart from the body surface. However, heart rhythm irregularities cannot always be detected on a standard resting ECG machine, since they may not occur during an individual's recording session. Common Holter-based portable solutions that record ECG for up to 24 to 48 hours lack the capability to provide real-time feedback. In this research, we seek to establish a cell phone-based real-time monitoring technology for CVD, capable of performing continuous on-line ECG processing, generating a personalized cardiac health summary report in layman's language, automatically detecting and classifying abnormal CVD conditions, all in real time. Specifically, we developed an adaptive artificial neural network (ANN)-based machine learning technique, combining both an individual's cardiac characteristics and information from clinical ECG databases, to train the cell phone to learn to adapt to its user's physiological conditions to achieve better ECG feature extraction and more accurate CVD classification on cell phones.
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