Patent classifications
A61B5/311
Conductive instrument
Disclosed is an instrument assembly for a selected procedure. The procedure may include a dissection and neural monitoring. The instrument may be insulated to allow for a selected and precise electrical conductive path.
Conductive instrument
Disclosed is an instrument assembly for a selected procedure. The procedure may include a dissection and neural monitoring. The instrument may be insulated to allow for a selected and precise electrical conductive path.
Selection of sensing electrodes in a spinal cord stimulator system using sensed stimulation artifacts
A sensing electrode selection algorithm is disclosed for use with an implantable pulse generator having an electrode array. The algorithm automatically selects optimal sensing electrodes in the array to be used with a pre-determined stimulation therapy appropriate for the patient. The algorithm preferably senses stimulation artifacts using different sensing electrodes, and more specifically different sensing electrode pairs as is appropriate when differential sensing is used. The algorithm further preferably senses these stimulation artifacts with the patient placed in two or more postures. The algorithm processes the stimulation artifact features measured at the different sensing electrodes and at the different postures to automatically determine one or more sensing electrode pairs that best distinguishes the two or more postures given the prescribed stimulation therapy.
Selection of sensing electrodes in a spinal cord stimulator system using sensed stimulation artifacts
A sensing electrode selection algorithm is disclosed for use with an implantable pulse generator having an electrode array. The algorithm automatically selects optimal sensing electrodes in the array to be used with a pre-determined stimulation therapy appropriate for the patient. The algorithm preferably senses stimulation artifacts using different sensing electrodes, and more specifically different sensing electrode pairs as is appropriate when differential sensing is used. The algorithm further preferably senses these stimulation artifacts with the patient placed in two or more postures. The algorithm processes the stimulation artifact features measured at the different sensing electrodes and at the different postures to automatically determine one or more sensing electrode pairs that best distinguishes the two or more postures given the prescribed stimulation therapy.
System and method for predictive modeling and analysis of neuron flow
A neuron pulse acquisition system is disclosed for capturing and processing neuron flow signals from the human body in a non-invasive manner. The system includes a pad with a skin sensor configured to detect neuron pulse potentials, a variable gain amplifier (VGA) to amplify detected signals, and an analog filter (A-FILTER) to remove noise. An analog-to-digital converter (ADC) digitizes the filtered signals, which are processed by a digital signal processing (DSP) and Bluetooth unit executing Fourier Transform and Cross-Correlation algorithms to analyze neuron flow characteristics in time and frequency domains. A D/A calibration module maintains analog accuracy, and a system software controller performs spectral analysis, cross-correlation, and machine learning-based predictive modeling. The system enables real-time monitoring, classification, and diagnostic interpretation of neuron flow behavior, thereby facilitating predictive analysis of neural conditions with high precision and reliability.
System and method for predictive modeling and analysis of neuron flow
A neuron pulse acquisition system is disclosed for capturing and processing neuron flow signals from the human body in a non-invasive manner. The system includes a pad with a skin sensor configured to detect neuron pulse potentials, a variable gain amplifier (VGA) to amplify detected signals, and an analog filter (A-FILTER) to remove noise. An analog-to-digital converter (ADC) digitizes the filtered signals, which are processed by a digital signal processing (DSP) and Bluetooth unit executing Fourier Transform and Cross-Correlation algorithms to analyze neuron flow characteristics in time and frequency domains. A D/A calibration module maintains analog accuracy, and a system software controller performs spectral analysis, cross-correlation, and machine learning-based predictive modeling. The system enables real-time monitoring, classification, and diagnostic interpretation of neuron flow behavior, thereby facilitating predictive analysis of neural conditions with high precision and reliability.