Deep Learning ECG Models for Nonlinear Heart Disease Risk Prediction

The patent replaces statistical analysis methods with deep learning models that utilize neural networks to process electrocardiogram data. The deep learning model automatically extracts features and predicts heart disease risk, capturing nonlinear patterns that statistical methods cannot detect. This substitution of mechanical/statistical systems with intelligent computational systems resolves the contradiction between prediction accuracy and model complexity.

The patent transforms the analysis approach by changing from traditional statistical parameters to deep learning parameters including neural network weights, activation functions, and learning rates. The model processes electrocardiogram signals through multiple layers of transformation, converting raw signal data into predictive risk assessments. This parameter transformation enables the system to capture complex nonlinear relationships while maintaining computational feasibility.

The patent implements preliminary actions by pre-processing electrocardiogram data to extract relevant features and preparing training datasets before actual prediction occurs. The deep learning model is trained in advance on historical data, creating a predictive system that can quickly assess new patients. This preliminary preparation reduces the time required for real-time predictions while maintaining high accuracy.

The patent segments the electrocardiogram data into multiple components and processes them through different layers of the neural network independently. The model divides the complex prediction task into smaller sub-tasks including feature extraction, pattern recognition, and risk assessment. This segmentation allows parallel processing and reduces overall computation time while preserving the ability to capture complex patterns.

The patent introduces an intermediary layer of feature extraction and signal processing between the raw electrocardiogram data and the final prediction. The deep learning model acts as an intermediary that filters out noise and interference while preserving meaningful patterns. This intermediary processing layer enhances the reliability of risk stratification by ensuring that predictions are based on clean, accurate signal representations rather than raw, noisy data.

The patent incorporates feedback mechanisms where the model continuously refines its predictions based on validation data and performance metrics. The training process includes feedback loops that adjust model parameters based on prediction accuracy, allowing the system to learn from errors and improve its ability to handle signal interference. This feedback mechanism enhances reliability by ensuring the model adapts to varying signal qualities and noise conditions.