Signal Processing Unit for Pneumatic Parameter Determination
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Solution Overview
Problem
Existing technologies fail to accurately and efficiently determine the pneumatic parameter associated with spontaneous breathing in mechanically ventilated patients, leading to uncertainties in synchronizing mechanical ventilation with spontaneous breathing and detecting anomalies.
Innovation Solution
A computer-implemented process and signal processing unit utilizing a lung-mechanical model and gradient model, combined with Kalman filters, to analyze volume flow, pressure, and respiratory signals from sensors, enabling the determination of the pneumatic parameter and improving synchronization and anomaly detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If mechanical ventilation is applied to patients, then life support is provided, but synchronization with spontaneous breathing becomes difficult and measurement uncertainties increase
Solution Approach 1:
The patent introduces an intermediary signal processing system that includes a lung-mechanical model and gradient model to mediate between the mechanical ventilation system and the spontaneous breathing signal. This intermediary processing chain (including Kalman filters and sensor arrays) extracts the pneumatic parameter Pmus from mixed signals, enabling reliable synchronization without direct interference with the physiological process.
Solution Approach 2:
The patent replaces direct mechanical measurement of breathing parameters with an electronic signal processing system. Instead of mechanically measuring breath volume or pressure directly, the system uses sensor arrays to detect physiological signals (EMG, MMG, or optical signals) and processes them through computational models to derive the pneumatic parameter, substituting mechanical measurement with electronic detection and calculation.
2Measurement precision
If multiple sensors are used to measure breathing parameters, then measurement accuracy improves, but device complexity increases
Solution Approach 1:
The patent implements a universal sensor array that can detect multiple types of physiological signals (EMG, MMG, or optical signals) related to spontaneous breathing. This multi-functional sensor system allows the same hardware infrastructure to capture different signal modalities, which are then processed through a unified signal processing chain that includes the lung-mechanical model and gradient model, reducing overall system complexity while maintaining measurement accuracy.
Solution Approach 2:
The patent merges multiple measurement functions into a single integrated system. Instead of using separate devices for different breathing parameters, the system combines sensor arrays, signal processing units, and computational models into one cohesive measurement system. The lung-mechanical model and gradient model process multiple signal types simultaneously to extract the pneumatic parameter, consolidating what would otherwise be separate measurement systems.
3Speed
If real-time processing of breathing signals is performed, then synchronization capability improves, but computational requirements and processing time increase
Solution Approach 1:
The patent applies preliminary action by pre-configuring the lung-mechanical model and gradient model with predetermined parameters and relationships. The signal processing unit is pre-programmed with the necessary computational algorithms and model structures, so that when real-time processing is needed, the system only requires executing pre-defined operations rather than performing complex calculations from scratch. This preliminary setup enables rapid real-time processing for synchronization.
Solution Approach 2:
The patent implements feedback mechanisms where the signal processing unit continuously monitors the output of the lung-mechanical model and gradient model and adjusts processing parameters accordingly. The Kalman filters use feedback from measured signals to continuously refine the estimation of the pneumatic parameter, enabling adaptive real-time processing that maintains synchronization speed while optimizing computational efficiency based on actual signal characteristics.
Data Source
AI summary
A process and a signal processing unit determine a pneumatic parameter (Pmus) for the spontaneous breathing of a patient. The patient is ventilated mechanically by a ventilator. A lung-mechanical model (20) and a gradient model (22) are preset. The lung-mechanical model (20) describes a relationship between the pneumatic parameter (Pmus) as well as a volume flow signal (Vol′), a volume signal (Vol) and/or a respiratory signal (Sig), which can be measured. The gradient model (22) describes a value for the pneumatic parameter (Pmus) as a function of N chronologically earlier values of the pneumatic parameter (Pmus) or of a variable correlating with the pneumatic parameter (Pmus). N values for the correlating variable are determined at first. At least one additional value is subsequently determined for the pneumatic parameter (Pmus). N chronologically earlier values of the correlating variable, current signal values, the lung-mechanical model (20) and the gradient model (22) are used for this purpose.