초록 ▼

The present invention comprises systems and methods for handling large amounts of data prone to ambiguity and artifact in real-time in order to ensure patient safety while performing a procedure involving a sedation and analgesia system. The invention utilizes neural networks to weight data which ma

The present invention comprises systems and methods for handling large amounts of data prone to ambiguity and artifact in real-time in order to ensure patient safety while performing a procedure involving a sedation and analgesia system. The invention utilizes neural networks to weight data which may be more accurate or more indicative of true patient condition such that the patient condition reported to the controller and the user of a sedation and analgesia system will have increased accuracy and the incidence of false positive alarms will be reduced.

대표청구항 ▼

The invention claimed is: 1. A sedation and analgesia system, comprising: two or more patient health monitor devices adapted so as to be coupled to a patient and so as to each generate a separate input signal reflecting a parameter of a physiological condition of the patient; a user interface; a dr

The invention claimed is: 1. A sedation and analgesia system, comprising: two or more patient health monitor devices adapted so as to be coupled to a patient and so as to each generate a separate input signal reflecting a parameter of a physiological condition of the patient; a user interface; a drug delivery controller for delivering a drug dosage rate of sedative to the patient during a procedure; and an electronic controller interconnected with the patient health monitors, the user interface, and the drug delivery controller, wherein said electronic controller further comprises a threshold logic unit which receives said input signals, multiplies each of said input signals by a predetermined weight corresponding to each of said parameters to achieve a weighted input signal for each corresponding input signal, combines the weighted input signals, and compares the weighted input signals against a predetermined threshold value that correlates to safe and effective sedation during said procedure to determine an action of said electronic controller. 2. The sedation and analgesia system of claim 1, wherein said input signals are binary values. 3. The sedation and analgesia system of claim 1, wherein each of said predetermined weights is trained to be set at a value representative of the significance of each signal resulting from the corresponding input and wherein said training comprises providing a series of inputs into the threshold logic unit indicative of at least one patient condition and the predetermined weights are adjusted until the system accurately detects adverse patient conditions and retains normal functionality during non-critical situations. 4. The sedation and analgesia system of claim 1, wherein said action of said electronic controller comprises at least one of decreasing drug levels, increasing oxygen delivery, delivering a pharmacological antagonist, alarming clinicians, requesting additional patient information from patient monitors, testing patient responsiveness, and delivering positive airway pressure. 5. The sedation and analgesia system of claim 1, wherein said patient monitoring devices comprise at least two of a sensor monitoring nasal airway pressure, a sensor monitoring oral airway pressure, a sensor monitoring nasal capnometry, and a sensor monitoring oral capnometry. 6. A sedation and analgesia system, comprising: two or more patient health monitor devices adapted so as to be coupled to a patient and so as to each generate a separate input signal reflecting a parameter of a physiological condition of the patient; a user interface; a drug delivery controller supplying one or more drugs to the patient; and an electronic controller interconnected with the patient health monitors, the user interface, and the drug delivery controller, said electronic controller receiving said input signals from the patient health monitors and comparing said input signals to parameters that indicate whether a given patient is experiencing or in danger of experiencing an undesirable patient condition while receiving said one or more drugs at said drug delivery rate, and said electronic controller thereby generating a signal reflecting the monitored physiological condition of the patient and indicating modifications of said drug delivery to avoid said undesirable patient condition during said medical procedure wherein said electronic controller further comprises a neural network to evaluate input signals to determine an action of said electronic controller, wherein said neural network comprises a set of inputs that make up a first layer of nodes, a set of hidden nodes, and a set of output nodes, wherein said inputs are related to any suitable feature of said patient health monitors. 7. The sedation and analgesia system of claim 6, wherein each said input signal is weighted differently at each node to use parallelism of the neural network to accurately distinguish between normal and adverse patient conditions. 8. The sedation and analgesia system of claim 7, wherein each input signal propagated to said hidden nodes is weighted by a numerical coefficient that indicates the significance of the respective parameter for said input signal. 9. The sedation and analgesia system of claim 8, wherein said neural network adjusts its numerical coefficients through multiple iterations until it reduces its output error to a predefined acceptable range. 10. The sedation and analgesia system of claim 6, wherein said inputs comprise at least one of data derived from sensor fusion, data derived from orthogonally redundant monitoring, data trends, heart rate, blood pressure, data from other neural networks, pulse oximetry, capnometry, acoustical monitoring, respiratory rate, and the sum of squares from patient parameters over time. 11. The sedation and analgesia system of claim 6, wherein said action of said electronic controller comprises at least one of decreasing drug levels, increasing oxygen delivery, delivering a pharmacological antagonist, alarming clinicians, requesting additional patient information from patient monitors, testing patient responsiveness, and delivering positive airway pressure. 12. The sedation and analgesia system of claim 6, further comprising the incorporation of at least one of a perception, back-propagated, and radial basis function network, wherein said at least one network increases the accuracy of sedation and analgesia system.