Enhanced Method and System for Determining Patient Volume Responsiveness

An improved method and system for determining a patient's volume responsiveness, providing more accurate and efficient fluid management through advanced signal processing and machine learning techniques.

The original patent disclosed a method and system for determining a patient's volume responsiveness. However, it had limitations in terms of signal processing and accuracy. The new inventive concept addresses these limitations by introducing advanced signal processing techniques, machine learning algorithms, and real-time monitoring capabilities, enabling more accurate and efficient fluid management.

The new inventive concept comprises a system with a pulse signal analyzer, a processor, and a display unit. The pulse signal analyzer detects pulse signals of a patient over multiple respiratory cycles. The processor determines an indicator representative of the patient's volume responsiveness using advanced signal processing techniques, such as non-negative smooth bell-shaped curves or Cauchy-Lorentz functions. The system also incorporates machine learning algorithms to predict a patient's volume responsiveness based on collected datasets. The display unit provides real-time monitoring of the determined indicator. Additionally, the system can be trained using a dataset of pulse signals and corresponding volume responsiveness indicators for multiple patients.

The new inventive concept introduces advanced signal processing techniques, machine learning algorithms, and real-time monitoring capabilities, which are not present in the original patent. These features provide more accurate and efficient fluid management, making the new inventive concept novel and non-obvious.

Alternative embodiments of the inventive concept could include using different signal processing techniques, such as wavelet analysis or Fourier transform, or incorporating additional physiological parameters, such as blood pressure or oxygen saturation, to improve the accuracy of volume responsiveness determination. Variations could also include using different machine learning algorithms or training datasets to adapt the system to specific patient populations or medical conditions.

The enhanced method and system for determining patient volume responsiveness have significant commercial potential in the medical device industry, particularly in critical care and anesthesia. The system could be integrated into existing patient monitoring systems, providing a competitive advantage in terms of accuracy and efficiency. The market potential is substantial, with an estimated global market size of over $1 billion by 2025.