Ventilation Trigger Detection via Neural Network Waveform Matching

The patent replaces traditional mechanical threshold-based detection with a neural network-based intelligent detection system. The neural network model automatically learns optimal detection thresholds and patterns from training data, substituting simple mechanical comparison operations with adaptive intelligent processing, thereby improving measurement precision while managing complexity through software-based solutions.

The patent dynamically adjusts detection parameters by training the neural network on diverse patient-ventilator interaction data. The system changes detection thresholds and sensitivity parameters based on learned patterns rather than using fixed values, allowing adaptive optimization of detection accuracy for different clinical scenarios and patient conditions.

The patent extracts and processes only the most critical ventilation parameters (airway pressure and flow rate) that are essential for detecting patient-ventilator synchrony. By selecting and monitoring only these key parameters rather than all available sensors, the system maintains high detection reliability while minimizing energy consumption associated with continuous multi-parameter monitoring.

The neural network model performs self-optimization by automatically adjusting which parameters require intensive monitoring based on learned patterns. The system identifies and focuses computational resources on the most informative parameters for each specific patient-ventilator interaction scenario, reducing unnecessary energy expenditure on monitoring less relevant parameters.

The patent replaces complex real-time waveform similarity matching algorithms with a pre-trained neural network inference system. The heavy computational work of pattern recognition is performed offline during training, while real-time operation uses the trained model for rapid classification. This substitution maintains high detection speed while reducing the complexity of real-time signal processing requirements.

The neural network model is trained in advance on extensive datasets of normal and abnormal patient-ventilator interactions. This preliminary action of offline training allows the system to rapidly classify new waveforms during real-time operation without performing complex similarity calculations, thereby achieving fast detection speed with reduced real-time computational complexity.