US5339818A — Method for determining blood pressure utilizing a…

Claim 1

Independent Claim 195 words

A method for on-line calculation of a variable physiological parameter of a patient, said method comprising the steps of: (1.1) identifying the physiological parameter to be quantitatively monitored and estimated; (1.2) coupling at least one sensor to the patient, said sensor being responsive to register changes in the physiological parameter, which changes are quantitatively dependent on a particular value for the parameter; (1.3) activating the sensor to generate a sequence of on-line signals which register changes in the physiological parameter; (1.4) transmitting the on-line signals as input signals to a computer system, including input nodes of a neural network supported by the computer system, which neural network is capable of calculating an output signal corresponding to a parameter value from the on-line, input signals; (1.5) processing the input signals within the neural network to convert the sequence of input signals to an on-line output signal corresponding to a parameter value by applying fixed weighting factors to the input signals; (1.6) generating said fixed weighting factors by retrieving weighting factors which were previously generated by applying a training algorithm with respect to previously collected training data comprising neural network input signals and corresponding known parameter values.

Claim 2

Independent Claim 195 words

A method for on-line calculation of a variable physiological parameter of a patient, said method comprising the steps of: (1.1) identifying the physiological parameter to be quantitatively monitored and estimated; (1.2) coupling at least one sensor to the patient, said sensor being responsive to register changes in the physiological parameter, which changes are quantitatively dependent on a particular value for the parameter; (1.3) activating the sensor to generate a sequence of on-line signals which register changes in the physiological parameter; (1.4) transmitting the on-line signals as input signals to a computer system, including input nodes of a neural network supported by the computer system, which neural network is capable of calculating an output signal corresponding to a parameter value from the on-line, input signals; (1.5) processing the input signals within the neural network to convert the sequence of input signals to an on-line output signal corresponding to a parameter value by applying fixed weighting factors to the input signals; (1.6) generating said fixed weighting factors by retrieving weighting factors which were previously generated by applying a training algorithm with respect to previously collected training data comprising neural network input signals and corresponding known parameter values.

Claim 3

Dependent Claim 70 words

A method as defined in claim 1, further comprising the steps of: (2.1) selecting a plurality of sample signals from the sequence of signals for processing through a neural network which has been trained to associate such sample signals with a related value for the physiological parameter; and (2.2) identifying at least one feature within the sample signals which can be processed through the neural network as a feature signal.

Claim 4

Dependent Claim 80 words

A method as defined in claim 2, wherein step 2.2 includes the step of identifying amplitude of oscillatory signals as the feature which defines the feature signal, said method further including the step of developing a waveform based on the sequential signals generated in step 1.3, said waveform being represented by the locus of points representing the amplitude of each oscillatory signal graphed with respect to a measurement of blood pressure over a time period comprising a single measurement procedure.

Claim 5

Dependent Claim 65 words

A method as defined in claim 3, wherein step 2.2 comprises the more specific step of selecting at least two sample signals from all signals generated pursuant to step 3 for the single measurement procedure and processing the amplitude feature signal of these sample signals in accordance with the remaining steps of claim 1 to estimate the parameter value without processing all signals being generated.

Claim 6

Dependent Claim 75 words

A method as defined in claim 3, wherein steps 1.3 and 1.4 include the more specific steps as follows: (5.1) developing a waveform for each single measurement procedure comprising a predetermined number of sample signals, which number corresponds approximately to the number of input nodes existing in the neural network; (5.2) storing in memory the sample signals; and (5.3) transmitting the stored sample signals of the waveform to respective input nodes of the neural network.

Claim 7

Dependent Claim 81 words

A method as defined in claim 5, wherein step 5.1 comprises the more specific step of selecting at least two sample signals from all signals generated pursuant to step 1.3 for the single measurement procedure and processing the amplitude feature signal of these sample signals in accordance with the remaining steps of claim 1 to apply on-line signals at the neural network to calculate the estimated parameter value by sampling only several representative signals from the sequence of signals being generated.

Claim 8

Dependent Claim 40 words

A method as defined in claim 2, comprising the more specific step of identifying the physiological parameter for monitoring and estimation to be a blood pressure parameter selected from the group consisting of diastolic, means, and systolic intraarterial blood pressures.

Claim 9

Dependent Claim 60 words

A method as defined in claim 7 for estimating blood pressure parameters, including the additional steps of: (8.1) generating a sequence of oscillometric signals representing heart pulse from a pressure sensing means coupled externally to a patient's anatomy in sensing proximity to a heart pulse sensing location; and (8.2) identifying the feature within the oscillometric signals to be pulse amplitude.

Claim 10

Dependent Claim 30 words

A method as defined in claim 8, comprising the more specific step of measuring and recording pressure values at predetermined increments over pressure ranges from approximately 20 to 200 torr.

Claim 11

Dependent Claim 32 words

A method as defined in claim 8, including the more specific step of selecting less than all generated signals of step 8.1 for transmittal to the input nodes of the neural network.

Claim 12

Dependent Claim 69 words

A method as defined in claim 10, comprising the more specific step of selecting a representative sampling of at least two generated signals, thereby estimating the waveform developed by the signals transmitted to the input nodes of the neural network without requiring processing of all signals through all interactive nodes of the neural network to generate the desired output signal corresponding to the value of the blood pressure parameter.

Claim 13

Independent Claim 187 words

A method for on-line calculation of a variable physiological parameter of a patient, said method comprising the steps of: (12.1) identifying the physiological parameter to be quantitatively monitored and estimated; (12.2) coupling at least one sensor to the patient, said sensor being responsive to register changes in the physiological parameter, which changes are quantitatively dependent on a particular value for the parameter; (12.3) activating the sensor to generate a sequence of on-line signals which register changes in the physiological parameter; (12.4) transmitting the on-line signals as input signals to a computer system, including input nodes of a neural network supported by the computer system, which neural network is capable of calculating an output signal corresponding to a parameter value from the on-line, input signals; (12.5) processing the input signals within the neural network to convert the sequence of input signals to an on line output signal corresponding to a parameter value in accordance with the following substeps: 12.5a) processing the input signals within the neural network through at least one neural network layer having at least one node by applying fixed weighting factors to the input signals;

Claim 14

Independent Claim 187 words

A method for on-line calculation of a variable physiological parameter of a patient, said method comprising the steps of: (12.1) identifying the physiological parameter to be quantitatively monitored and estimated; (12.2) coupling at least one sensor to the patient, said sensor being responsive to register changes in the physiological parameter, which changes are quantitatively dependent on a particular value for the parameter; (12.3) activating the sensor to generate a sequence of on-line signals which register changes in the physiological parameter; (12.4) transmitting the on-line signals as input signals to a computer system, including input nodes of a neural network supported by the computer system, which neural network is capable of calculating an output signal corresponding to a parameter value from the on-line, input signals; (12.5) processing the input signals within the neural network to convert the sequence of input signals to an on line output signal corresponding to a parameter value in accordance with the following substeps: 12.5a) processing the input signals within the neural network through at least one neural network layer having at least one node by applying fixed weighting factors to the input signals;

Claim 15

Dependent Claim 200 words

5b) generating said fixed weighting factors by retrieving fixed weighting factors which were previously determined by applying a training algorithm with respect to previously collected training data comprising neural network input signals and corresponding known parameter values to generate said fixed weighting factors; 12.5c) for each input signal of each node within the neural network layer, calculating a product of the input signal and fixed weighting factor corresponding to each input signal and node combination; 12.5d) for each node within the neural network layer, summing the products of each input signal and fixed weighting factor combination calculated in the previous step 12.5c); 12.5e) for each node within the neural network layer, calculating a node output by applying an input/output function to the sum calculated in the previous step 12.5d); 12.5f) where the output of each node calculated in step 12.5e) represents the neural network output, displaying at least one node output as an estimated physiological parameter, or 12.5g) where the output of each node calculated in step 12.5e) represents the output of at least one hidden layer node, passing at least one output from outputs calculated in 12.5e) as input to any subsequent layer of nodes in the neural network.

Claim 16

Dependent Claim 200 words

5b) generating said fixed weighting factors by retrieving fixed weighting factors which were previously determined by applying a training algorithm with respect to previously collected training data comprising neural network input signals and corresponding known parameter values to generate said fixed weighting factors; 12.5c) for each input signal of each node within the neural network layer, calculating a product of the input signal and fixed weighting factor corresponding to each input signal and node combination; 12.5d) for each node within the neural network layer, summing the products of each input signal and fixed weighting factor combination calculated in the previous step 12.5c); 12.5e) for each node within the neural network layer, calculating a node output by applying an input/output function to the sum calculated in the previous step 12.5d); 12.5f) where the output of each node calculated in step 12.5e) represents the neural network output, displaying at least one node output as an estimated physiological parameter, or 12.5g) where the output of each node calculated in step 12.5e) represents the output of at least one hidden layer node, passing at least one output from outputs calculated in 12.5e) as input to any subsequent layer of nodes in the neural network.

Claim 17

Dependent Claim 51 words

A method as defined in claim 12, further comprising the steps of processing the output of the at least one hidden layer node from step 12.5g) by repeating steps 12.5c) through 12.5g) until the at least one node output defined in step 12.5(e)f) representing the estimated physiological parameter has been displayed.

Annuity Stage	Window Opens	Payment Deadline	Surcharge Period Until	Estimated Fee	Status
3.5 Years (Year 4)	1997-08-22	1998-02-21	1998-08-23	$2000.00	Window Closed / Lapsed
7.5 Years (Year 8)	2001-08-22	2002-02-21	2002-08-23	$3760.00	Window Closed / Lapsed
11.5 Years (Year 12)	2005-08-22	2006-02-21	2006-08-23	$7700.00	Window Closed / Lapsed

Method for determining blood pressure utilizing a neural network

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