Cardiac Exertion Monitoring via Visual and Physical Feedback
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Solution Overview
Problem
Current cardiovascular disease diagnosis and treatment methods face challenges in accurately detecting cardiac arrhythmic events and myocardial ischemia due to noise and abnormalities in signal data, leading to potential misdiagnosis and inefficient resource allocation.
Innovation Solution
A system that combines wearable visual displays and exercise equipment with sensors to measure cardiac exertion, adjusting visual and physical stress to maximize cardiac exertion while using neural networks to denoise and classify electrocardiogram signals, thereby improving signal quality and reducing misclassification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual displays and exercise equipment are used to maximize cardiac exertion, then diagnostic accuracy is improved, but patient stress and physical burden increase
Solution Approach 1:
The system continuously monitors cardiac exertion through sensors and provides real-time feedback by adjusting visual imagery and exercise equipment parameters. This closed-loop feedback mechanism allows the system to maximize diagnostic accuracy while dynamically managing patient stress levels based on measured physiological responses.
Solution Approach 2:
The system changes multiple parameters simultaneously including visual imagery characteristics, exercise equipment settings, and monitoring thresholds. By coordinating these parameter changes, the system optimizes cardiac exertion for diagnostic accuracy while preventing excessive stress through controlled parameter adjustments.
2Measurement precision
If neural networks are used to denoise ECG signals, then signal quality is improved, but computational complexity and processing time increase
Solution Approach 1:
The neural network models are trained in advance on large datasets of ECG signals with various noise patterns. This preliminary training allows the models to quickly denoise new signals during actual cardiac monitoring without requiring complex real-time computations, thus improving signal quality while maintaining acceptable processing speeds.
Solution Approach 2:
The system uses pre-trained neural network models that can be deployed as software copies across multiple devices. This allows complex denoising capabilities to be replicated without requiring each device to have identical computational hardware, reducing overall system complexity while maintaining high signal quality through sophisticated neural network processing.
Data Source
AI summary
Methods, systems, and apparatuses are described for cardia treatment and analysis. A computing device may cause a head mounted visual display worn by a patient to output visual imagery. The visual imager is configured to affect a psychological perception of the patient. The computing device may cause exercise equipment in use by the patient to affect a physical exercise performed by the patient. The computing device may measure, via one or more sensors, a cardiac exertion of the patient. The computing device may determine, based on the cardiac exertion of the patient, that the patient has not reached a maximum cardiac exertion. The computing device may adjust one or more of the visual imagery or the physical exercise. The adjustment may cause the patient to reach the maximum cardiac exertion.