Method for determining blood pressure utilizing a neural network
September 9, 2013. Estimated Expiration Date is calculated based on simple USPTO term provisions. It does not account for terminal disclaimers, term adjustments, failure to pay maintenance fees, or other factors which might affect the term of a patent.Patent References 3568075 Methods of and apparatus for the measurement of blood pressure Self organizing general pattern class separator and identifier Self adjusting bio-feedback method and apparatus Cardiopulmonary exercise system Medical instrument for noninvasive measurement of cardiovascular characteristics Underwater computer Apparatus for and method of monitoring and controlling body-function parameters during intracranial observation Fuzzy logic basic circuit and fuzzy logic integrated circuit operable in current mode Method for determining systolic arterial blood pressure in a subject InventorsApplicationNo. 119451 filed on 09/09/1993US Classes:600/490, Force applied against skin to close blood vessel128/925, Neural network600/493, Electric signal generated by sensing means responsive to pulse or Korotkoff sounds706/924MedicalExaminersPrimary: Cohen, Lee S.Assistant: Nasser, Robert L. Attorney, Agent or FirmInternational ClassA61B 005/00AbstractA method and device for indirect, quantitative estimation of blood pressure attributes and similar variable physiological parameters utilizing indirect techniques. The method of practice includes (i) generating a sequence of signals which are quantitative dependent upon the variable parameter, (ii) transmitting and processing the signals within a computer system and associated neural network capable of generating a single output signal for the combined input signals, (iii) directly determining an actual value for the parameter concurrent with the indirect generation of signals of the previous steps, (iv) applying weighting factors within the neural network at interconnecting nodes to force the output signal of the neural network to match the true value of the parameter as determined invasively, (v) recording the input signals, weighting factors and true value as training data within memory of the computer, and (vi) repeating the previous steps to develop sufficient training data to enable the neural network to accurately estimate parameter value upon future receipt of on-line input signals. Procedures are also described for preclassification of signals and artifact rejection. Following training of the neural network, further direct measurement is unnecessary and the system is ready for diagnostic application and noninvasive estimation of parameter values.Other References
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